Vertical Advantage: Transforming Industries with Lakebase and Agentic AI

In the first blog of this series, we looked at how Lakebase Postgres is rewriting the foundation of enterprise applications - collapsing the decades-old divide between operational and analytical systems into a single governed platform. By bringing a serverless, Postgres transactional database directly onto the Data and AI Platform, Lakebase eliminates the pipelines and duplicate governance that used to sit between a transaction and a decision. We covered the cross-industry and function-specific accelerators our partners have built on that foundation - the reusable patterns for agent memory, database modernization, and real-time operations that apply no matter what business you're in. The market response has been decisive: since launch, Lakebase adoption has grown at more than twice the rate of our data warehousing product, with thousands of companies now running production workloads.

But foundational capability only becomes competitive advantage when it meets the specific realities of an industry - the regulatory change a bank must respond to and prove it did, the claim an insurer needs to adjudicate in minutes instead of days, the prior authorization a provider can't keep a patient waiting on, the empty shelf a retailer has to catch before the shopper walks out. This is where our consulting and SI partners turn the platform into an outcome. In this second blog, we showcase the industry-specific solutions Databricks partners built on Lakebase - spanning financial services, manufacturing and energy, retail, CPG and travel and hospitality, healthcare and life sciences, communications and media, and the public sector. Each pairs deep vertical expertise with the operational speed, unified governance, and agent-readiness of the Databricks Lakebase, and each is production-ready today.

This blog showcases innovative partner solutions built on Databricks Lakebase across the following categories: 

• Financial Services
• Manufacturing and Energy
• Retail, CPG, Travel & Hospitality
• Healthcare and Life Sciences
• Communications, Media, Entertainment and Gaming
• Public Sector 

Financial Services  

Advancing analytics 
Advancing Analytics’ Regulation Change Agent helps financial services firms respond to regulatory change, and prove they have, using Databricks Lakebase. Compliance teams face rising volume from FCA, PRA and EU bodies, but the hard part is not just reading rules. It is proving what changed, when it was identified and how the firm responded. Specialist agents monitor, interpret and gap-check each change, while Lakebase acts as the system of record for events, analysis, drafts and decisions. Unity Catalog governs the lakehouse context the agents read. The result is faster response, less manual effort and a defensible audit trail for regulators. Read this blog to learn more. 

Bitwise
The Bitwise AI-Native Claims Operations Platform, built on Databricks Lakebase and the Databricks Data Intelligence Platform, transforms claims processing from fragmented, manual workflows into an intelligent, AI-driven operational platform. Rather than replacing core insurance systems such as Guidewire or Duck Creek, it complements them by serving as the System of Work, while Databricks becomes the System of Intelligence. Lakebase provides a high-performance collaborative workspace for claims intake, investigations, document management, vendor coordination, and adjuster activities, with continuous synchronization into the Databricks Lakehouse through Lakeflow. Powered by a Claims Knowledge Graph and Mosaic AI agents, the platform delivers real-time fraud detection, severity prediction, reserve recommendations, document intelligence, and adjuster copilots. Insurers can reduce claims adjudication from days to minutes, lower indemnity leakage and Loss Adjustment Expenses (LAE), eliminate complex ETL and CDC pipelines, improve adjuster productivity, accelerate settlement cycles, and ultimately optimize loss ratio, combined ratio, and customer satisfaction.

Capgemini
KYC + pKYC industry accelerator: Capgemini’s KYC + pKYC accelerator is a domain-driven, agentic-powered solution designed to transform KYC and pKYC processes. By leveraging deep domain expertise and data-driven automation alongside conversational AI, the accelerator streamlines client onboarding, continuous monitoring, and periodic recertification. It establishes a unified KYC Data Foundation on a Lakehouse architecture, incorporating policy- and regulation-aware reasoning. This solution utilizes Lakebase to enable some of the key processes in report management and overall markedly reduces manual effort, accelerates onboarding timelines, enhances explainability and auditability, and delivers a scalable, future-ready framework for intelligent KYC and pKYC operations.

Datapao
The Datapao Hyper-Personalization Accelerator combines Databricks Genie and Lakebase to deliver real-time, individualized customer experiences at enterprise scale. The solution unifies analytical intelligence from the lakehouse with low-latency operational serving, enabling marketing, e-commerce, and customer experience teams to generate tailored recommendations, content, and offers with sub-second responsiveness. Business users can explore customer segments and validate personalization hypotheses through natural-language queries in Genie, while Lakebase powers the production-grade serving layer for live applications. Built on Unity Catalog for governed, compliant access to customer data, the accelerator provides a repeatable blueprint for operationalizing personalization across industries. Read this blog to learn more.

Entrada
Entrada's Mortgage Intelligence Platform unifies internal pipelines, Cotality property intelligence, competitive signals, and geospatial context into an actionable view. AI-native insights reveal hidden opportunities and risks, empowering loan, underwriting, and risk teams. Built-in agent orchestration and Lakebase audit trails ensure every recommendation is transparent, reviewable, and fully compliance-ready. Leveraging Genie's conversational analytics and Agent Bricks orchestration, the platform enables organizations to query insights in plain language, assemble complete intelligence dossiers, and act decisively on retention and origination opportunities. Read this blog to learn more. 

IBM
Claims & Underwriting Copilot: Claims and underwriting are where customer experience, loss economics, and compliance collide. A Claims & Underwriting Copilot built on Genie + Lakebase gives adjusters and underwriters one real-time decision layer: unified policy, claims, third-party, and document data; automated extraction from submissions, FNOLs, medical records, estimates, and correspondence; predictive risk scoring; and governed recommendations with human oversight. Capgemini’s November 2025 World Cloud Report findings show insurers are targeting AI agents at underwriting (68%) and claims processing (65%), yet only 10% of financial institutions have agents deployed at scale. That gap is the opportunity: accelerate decisions without sacrificing control or trust at enterprise speed. 

Impetus
Impetus’ Near real-time credit card fraud detection solution framework on Databricks, powered by Lakebase and Genie, enables low-latency processing of high-volume transactions. Streaming pipelines ingest and transform data, while Lakebase serves intelligent features for fast, scalable inference. Machine learning models, combined with rule-based logic, enable accurate fraud detection, and Genie delivers conversational insights on compliance and risk data. This unified approach ensures near real-time decisioning, governed intelligence, and scalable performance, empowering enterprises to proactively detect fraud while maintaining transparency, auditability, and easy access to insights.

Impetus’ Solution Framework for Real-Time Claims Processing, built on Databricks Lakebase: Insurance claims processing is a critical function for insurers, encompassing claim submission, validation, adjudication, and settlement. Traditional systems often operate in silos with batch-driven processing, resulting in delayed approvals, fragmented data, and reactive decision-making. Impetus modernizes this use case with a Lakebase-centric architecture, where claim and payment transactions are captured in real time, enabling low-latency, ACID-compliant operations with high concurrency. Leveraging Databricks’ unified data platform, transactional data can be read and written directly to Lakebase for real-time operations, while data is synced to the Lakehouse for analytics, insights, and improved operational efficiency. Read this blog to learn more. 

Indicium AI
Enterprise Risk Intelligence gives executives continuous visibility into operational, financial, and compliance risk across the business. Built on Databricks with Lakebase, the solution unifies risk signals into governed executive views, surfaces anomalies as they emerge, and enables conversational investigation of root causes. Leaders move from quarterly risk reviews to real-time decision-making, while governance teams reduce manual oversight effort by 30 to 50% without compromising control. Carriers, banks, and regulated enterprises strengthen audit posture, resolve incidents in hours instead of weeks, and free capacity from reactive reporting. The outcome is a risk function that scales with the business rather than against it. Read this blog to learn more. 

Koantek
Risk and Compliance based on Ascend AI AppBase productizes Databricks Apps and Lakebase best practices into governed operational-app delivery. A growing library of Lakebase-first starter kits runs on a shared Data-Intelligent Starter foundation, starting with customer intelligence, AI agent operations, risk and compliance, and industrial operations. Each kit serves Unity Catalog data via Synced Tables, stores transactional app state in Lakebase, and ships via app resources, valueFrom bindings, service-principal permissions, and Declarative Automation Bundles. Koantek adds the field layer that moves a kit beyond a demo: vertical schemas, grant matrices, generated bundles, QA harnesses, and proof-ready evidence. Read this blog to learn more. 

LTM
LTM's Risk Sentinel is an early risk warning system for Banks. In a world where risks whisper before they roar, Risk Sentinel cuts through the noise, detecting weak signals, connecting the dots across payments, transactions, and market shifts, and surfacing prioritized, evidence-backed risk cases before accounts turn non-performing. Its agentic operations ensure zero alerts are ignored, delayed, or lost, every signal is owned from creation to closure. It is built using two platforms - Databricks and LTM's BlueVerse. LTM BlueVerse a low-code/no-code AI platform with a marketplace of prebuilt reusable agents. Risk sentinel leverages pre-built agents and tools from BlueVerse and seamlessly integrates with Databricks platform. The application is hosted on Databricks Apps, Lakebase for configuration and token management, Lakehouse for data storage, AI Gateway for LLMs, guardrails & rate limiting, LLM-as-Judge for evaluation, and finally Unity catalog for governance and enterprise grade security. Read this blog to learn more. 

LTM’s Cyber Risk Navigator is a solution by LTM designed to address a core challenge in insurance - equipping underwriters and risk advisors with unified, decision-ready cyber risk intelligence. The platform delivers comprehensive insights such as cyber risk exposure summaries, loss potential indicators, peer benchmarking views, and coverage recommendations, enabling richer risk conversations and faster decision-making during client engagements. The application is built with Lakebase as the central backbone for event processing and analytics, creating a unified application layer that ingests data from multiple internal and external sources. User inputs from the front-end are captured and processed directly within Lakebase, which orchestrates API calls, manages analytical processing, and consolidates model outputs and insights into a single governed layer. Powered by the Databricks ecosystem, this architecture eliminates multi-hop data movement, reduces latency, and enables real-time, data-driven cyber risk advisory - empowering underwriters to make faster, more informed decisions at scale. Read this blog to learn more. 

LTM’s Customer Centricity: Customer Centricity demonstrates how banks can operationalize customer centricity using Databricks Genie and Lakebase to drive measurable business outcomes. It unifies customer data across core banking, cards, transactions, service, and digital channels into a governed lakebase to create an action-ready customer profile. Genie enables personas such as Relationship Managers and Marketing Analysts to interact conversationally with customer insights, prioritize next best actions, and generate campaigns with data-backed rationale. A supervisor agent operationalizes outcomes by assigning customers, recommending products, and generating outreach scripts—helping banks increase wallet share, improve product mix, reduce churn, and enhance customer experience at scale. Read this blog to learn more. 

Polestar Analytics
WealthPulse is an AI-powered Wealth Management Operations Platform built for RIA firms. It automatically ingests, unifies, and operationalizes data from 35+ vendors across 7 source types - custodians, CRMs, billing, compliance, market data, and more. Its 118-KPI progressive unlock engine delivers deeper analytics as data sources connect, while purpose-built dashboards serve three personas: IT Ops Admins (pipeline health), Financial Advisors (household analytics), and Firm Leadership (firm-wide KPIs). It leverages Databricks Lakebase as the underlying data store, Unity Catalog for governance and lineage, Mosaic AI for ML model training (attrition risk, portfolio optimization), and Databricks Genie for conversational AI across the platform. Read this blog to learn more. 

Wipro
Wealth AI: WealthAI is a comprehensive, AI-driven platform spanning the entire wealth management lifecycle—from enhancing financial advisor experiences and deepening client engagement to accelerating middle- and back-office operations. Built on a multi-agentic, scalable architecture leveraging Databricks Lakebase/Lakehouse, it enables real-time analytics, governed data access, and resilient orchestration of AI agents. WealthAI applies advanced marketing analytics, personalized product recommendations, and automation across trade surveillance, trade break analysis, and SAR processes. For financial advisors, it generates high-quality proposals, personalized market research, and advisory content. With Databricks Genie enabling conversational analytics and strong model and data governance, WealthAI improves efficiency, compliance, data quality, advisor productivity, and overall business excellence. Watch the demo and read the blog to learn more. 

Zeb 
zeb Agentic Lakebase for Financial Services is a productized, agent-native pattern that operationalizes Databricks' "database for agents" positioning. An AI agent receives a scoped Lakebase environment as its persistent memory and transactional runtime, with tools to query, execute, evolve schemas, and ingest data. It runs in two modes. Greenfield: the agent takes a business prompt, provisions Lakebase, designs the data model, generates application code, and auto-deploys a live Databricks App. Brownfield: the agent ingests an existing prototype from Lovable, Bolt, v0, or Cursor, infers the model, and migrates it to production. Where others give agents read-only context, zeb gives full transactional ownership. Read this blog to learn more. 

Manufacturing and Energy  

Aimpoint Digital
Aimpoint Digital’s Energy Intelligence Application helps energy, utility, and AI infrastructure organizations turn fragmented operational data into faster, smarter action. Built on Databricks and powered by Lakebase for near-real-time operational updates, it unifies live telemetry, asset conditions, historical context, workflow coordination, and AI-assisted investigation in a single governed experience. Teams can detect anomalies earlier, investigate issues with greater confidence, and respond faster without relying on disconnected tools or manual handoffs. The result is a more resilient operating model that improves visibility, accelerates incident response, reduces manual effort, and creates a practical path from raw signals to informed, governed decisions. 

Celebal Technologies
CT Vision is redefining enterprise AI architecture by keeping operational databases inside the Databricks workspace using Lakebase. Instead of relying on external databases like RDS, the platform unifies AI compute, storage, governance, and transactional operations within a single secure boundary. This approach reduces network complexity, minimizes compliance friction, simplifies deployments, and improves auditability. By leveraging PostgreSQL JSONB schemas, CT Vision enables AI models and KPI structures to evolve without disruptive database migrations. The platform delivers scalable, enterprise-ready video intelligence with faster deployments, stronger governance, simplified infrastructure management, and seamless adaptability for continuously evolving AI workflows. Read this blog to learn how a unified Databricks-native architecture is simplifying enterprise AI operations while accelerating innovation.

Datapao 
Datapao's Real-Time Supply Chain Intelligence platform unifies shipments, production, inventory, and risk into one live view on Databricks. When disruption strikes, affected shipments are flagged instantly, rerouted automatically, and the impact is traced all the way to the factory floor in seconds — turning a days-long scramble across disconnected systems into an immediate, informed decision. Built on Lakebase, the live operational state and analytical layer share one foundation, making it AI-ready from day one rather than after months of integration. It works across any transport mode — ocean, air, rail, road — and runs what-if simulations so teams can test decisions before committing. Read this blog to learn more. 

Delaware
Next-gen MDMS on Databricks – Scalable, trusted meter data: Utilities face increasing pressure to process massive volumes of smart meter data while ensuring accuracy, compliance, and operational insight. Delaware’s Databricks-based Meter Data Management System (MDMS) consolidates data from all head-end and legacy systems into Lakebase as a persistent, trusted system of record. Built on the Lakehouse, it enables scalable ingestion, standardization, and validation of billions of readings in near real time. Unity Catalog provides end-to-end governance and lineage, ensuring regulatory compliance and data consistency across the organization. Genie enables business users, from customer service to field operations, to access insights through natural language. Compared to traditional MDMS platforms, this approach offers greater scalability, lower cost, and faster access to insights; improving billing accuracy, reducing operational overhead, and enabling smarter grid operations.

Delaware’s (Em)powering the connected worker with Genie & Lakebase: Delaware enables operators, engineers, and plant managers to make faster, data-driven decisions by turning factory data into a conversational experience. Through integration with a broad OT partner ecosystem supported, real-time OT data is captured and contextualized directly into the Databricks Lakehouse, creating a unified view across IT and OT systems. Lakebase acts as a persistent foundation for reliable, high-volume industrial data. With Genie, users can query performance, quality, and downtime using natural language; without relying on static dashboards or engineering support. Unity Catalog ensures secure, governed access with full lineage and auditability across all data and interactions. The result is faster root cause analysis, improved traceability, and reduced downtime, while providing a scalable foundation for MES modernization and Industry 4.0 use cases.

Diggibyte 
LakePulse - Real-Time Manufacturing Intelligence Powered by Databricks Lakebase: LakePulse is a real-time manufacturing operations platform built on Databricks, designed to bridge the gap between operational data and frontline action. By combining the analytical power of the Databricks Lakehouse with the low-latency serving capabilities of Lakebase, LakePulse delivers live KPIs, equipment health insights, operational alerts, and recent process trends directly to operators, supervisors, and plant managers. The platform enables instant alert notifications, rapid acknowledgement workflows, and seamless mobile access across devices. With unified governance through Unity Catalog, LakePulse transforms manufacturing data into actionable intelligence, empowering organizations to improve responsiveness, reduce downtime, enhance operational efficiency, and accelerate decision-making on the shop floor. Read this blog to learn more. 

Diggibyte LakeForge - Manufacturing Application Modernization on Databricks: LakeForge is a unified manufacturing execution and intelligence platform built on Databricks, designed to seamlessly converge operational transactions, business processes, and enterprise analytics. Leveraging Databricks Lakebase as the transactional foundation, LakeForge powers production workflows, inventory movements, quality processes, and operational applications with low-latency performance, while continuously synchronizing with curated Lakehouse data for advanced analytics and AI. By eliminating data silos between operational systems and analytical platforms, LakeForge creates a connected digital manufacturing ecosystem where applications, insights, and decisions operate on the same trusted data foundation. The result is faster innovation, improved process visibility, enhanced operational agility, and intelligent manufacturing at scale. Read this blog to learn more. 

IBM
Supply Chain Intelligence Hub: Supply Chain Intelligence Hub (Lakebase) enables organizations to build an AI-driven intelligence layer across their supply chain, unifying data from ERP, TMS, WMS, MES, EDI, and supplier systems into a single, trusted foundation. Built on the Databricks Lakehouse, it delivers real-time visibility, predictive insights, and automated recommendations to balance supply and demand. From anticipating disruptions like severe weather to optimizing parts availability against production schedules, the platform powers proactive, data-driven decisions. By connecting planning, procurement, and logistics, it transforms fragmented operations into a coordinated, resilient, and responsive supply chain that improves service levels, reduces risk, and drives measurable business value.

IBM Field Service Agent Assist: Field Service Agent Assist empowers field teams with real-time, context-aware intelligence, delivering hands-free guidance, diagnostics, and automated documentation directly within existing EAM platforms. By integrating asset data, work orders, and enterprise knowledge sources, it supports guided inspections, repair workflows, and parts recommendations while capturing activities seamlessly through voice interaction. Built on a secure, governed AI platform, it ensures traceability and human-in-the-loop oversight. The solution reduces mean time to repair, increases first-time fix rates, minimizes administrative burden, and strengthens safety compliance while preserving critical knowledge and enabling more efficient, informed, and resilient field operations.

IBM Drone Operations Control Plane is a map-first operations platform that unifies assets, work orders, drone imagery, and environmental data into a single, trusted view of infrastructure health. Built on Databricks Lakebase, it ingests and refines SCADA, EAM/CMMS, weather, and media data to power risk scoring, vegetation analysis, and predictive insights. Automated image classification links drone photos directly to assets and work orders, eliminating manual processes and enabling anomaly detection and maintenance prioritization. By surfacing vegetation encroachment and asset risks proactively, the solution improves inspection traceability, accelerates triage, optimizes resource planning, reduces outages, and enhances safety, compliance, and grid resilience.

Infosys 
Energy.AI - Production Optimization: Energy.AI optimizes oil well performance using AI-driven engineering and a Databricks-powered data intelligence platform. It unifies SCADA, historian, and enterprise data to enable rapid onboarding, probabilistic forecasting, real-time surveillance, and predictive flow assurance. Insights and model outputs are delivered to engineers via a Databricks app, while LangGraph with Lakebase enables scalable, stateful agent workflows with human approval checkpoints. The solution enhances forecast accuracy, reduces intervention time, and lowers lifting costs, driving efficient and data-driven production operations. 

Koantek
Intelligent Operations based on Ascend AI AppBase productizes Databricks Apps and Lakebase best practices into governed operational-app delivery. A growing library of Lakebase-first starter kits runs on a shared Data-Intelligent Starter foundation, starting with customer intelligence, AI agent operations, risk and compliance, and industrial operations. Each kit serves Unity Catalog data via Synced Tables, stores transactional app state in Lakebase, and ships via app resources, valueFrom bindings, service-principal permissions, and Declarative Automation Bundles. Koantek adds the field layer that moves a kit beyond a demo: vertical schemas, grant matrices, generated bundles, QA harnesses, and proof-ready evidence. Read this blog to learn more. 

Lovelytics 
Gridlytics: Lovelytics Gridlytics AI accelerator combines environment and asset performance data with the power of the Databricks Lakehouse and AI models to enable proactive risk assessment and ensure system resilience. Gridlytics AI, developed by Lovelytics in partnership with Databricks, is a purpose-built accelerator designed to modernize utility grid operations. By unifying siloed data—including environmental signals, asset performance, and field metrics—into a single "pane of glass" on a governed lakehouse architecture, it enables proactive risk assessment and faster decision-making. The solution leverages GenAI and predictive analytics to automate manual workflows, simulate outage scenarios, and provide natural language querying. Key benefits include up to 30% efficiency gains and 20% productivity boosts, helping utilities transition from reactive maintenance to a resilient, data-driven strategy for modern energy demands. Watch this demo to learn more. 

Lovelytics Dronelytics: Dronelytics is an end-to-end aerial intelligence accelerator built on the Databricks Data Intelligence Platform. It automates the ingestion and processing of drone imagery to streamline asset health inspections across transmission, distribution, and renewables. By utilizing Agentic AI and computer vision, the solution identifies critical defects—such as turbine cracks and damaged insulators—with up to 40% faster identification rates. The platform unifies siloed drone data into a secure "single pane of glass" via Unity Catalog, enabling predictive maintenance and prioritized scheduling. This centralized architecture reduces manual effort and redundant field operations, delivering an estimated $750k–$900k in annual labor efficiency gains per asset type. Watch this demo to learn more. 

Lovelytics Veglytics: Veglytics is an end-to-end vegetation management solution built on Databricks Apps and powered by Lakebase. It accelerates time-to-value by utilizing a purpose-built data model and automated pipelines that integrate LiDAR, aerial imagery, and asset data to generate high-resolution risk insights. The platform enhances visibility through unified dashboards and high-performance 3D LiDAR visualizations, allowing utilities to move from reactive to proactive maintenance. By incorporating AI-assisted planning and integrated crew workflows, Veglytics significantly increases operational efficiency. This enables faster work order generation and targeted tree-trimming, ultimately reducing wildfire risks, ensuring regulatory compliance, and optimizing multi-million dollar annual O&M expenditures. Watch this video to learn more. 

Lovelytics Meteolytics: Meteolytics is a high-performance analytics solution designed to transform complex meteorological data into actionable business intelligence. Built on the Databricks platform, it unifies disparate weather sources to provide energy and utility companies with predictive insights into supply, demand, and climate impacts. The tool features geospatial mapping, automated alerting, and scenario simulations to safeguard infrastructure and optimize renewable energy grids. By integrating real-time weather feeds with operational data, Meteolytics AI helps organizations reduce O&M expenses and achieve up to 30% time savings for meteorologists through streamlined, interactive visualizations. Watch this video to learn more. 

Lovelytics Windsights: Windsights is an AI-powered predictive maintenance accelerator built on the Databricks Data Intelligence Platform. By unifying high-frequency SCADA telemetry, meteorological data, and aerial imagery into a governed Lakehouse, it enables utilities to transition from reactive to proactive asset management. Utilizing custom deep learning models and LLMs, Windsights identifies complex failure patterns to predict component degradation before it occurs. This approach reduces turbine downtime by 35–50% and significantly lowers O&M costs through planned, condition-based interventions. Delivered via a user-friendly Databricks App, the solution optimizes fleet performance, improves technician safety, and extends the overall lifespan of wind assets. Watch this video to learn more. 

Lovelytics Energylytics: Energylytics is a unified, AI-powered intelligence platform developed by Lovelytics to modernize energy trading. Built on the Databricks Data Intelligence Platform, it centralizes disparate data—including market prices, weather patterns, and grid conditions—into a single source of truth. By replacing manual spreadsheets with real-time visibility and predictive analytics, the platform helps utilities optimize trading decisions and manage risk. Key features like Databricks Genie and Agent Bricks enable autonomous bid packaging and natural language queries, potentially delivering $20M–$80M in incremental margins while significantly improving operational efficiency for energy trading teams. Watch this video and read this blog to learn more. 

Perficient 
Battery Passport AI (BPAI) tracks EV battery data from factory to recycling: scoring health, forecasting end-of-life, and serving verified battery telematics at the point of sale for automotive OEMs and battery lifecycle stakeholders. The analytical heavy-lifting runs in the lakehouse, while Lakebase, Databricks' managed Postgres transactional engine, transforms BPAI into a highly-interactive user experience. Curated health scores, telemetry, and service history sync from Delta into Lakebase, which BPAI reads over a standard Postgres connection with sub-second latency: fleet dashboards, battery-level telemetry, and service intelligence, all live. Alert triage writes back to Lakebase in real-time, coordinated with Postgres advisory locks, so the operational state sits right next to the analytics that produced it. A dealer pulls a verified battery passport by VIN at the counter in milliseconds; agents read and write decisions transactionally against live state; partners and marketplaces receive resale-grade battery telematics on demand, all served from one unified platform governed through Unity Catalog, with no bolt-on operational database to run.

Solita
Solita’s Energy Management Foundation includes a Databricks reference architecture and reference data model that helps energy-intensive manufacturers bring together utility data for clear baselines and predictive forecasting. Built on the Databricks Data Intelligence Platform, it organizes factory telemetry and metering data into an ISA-95 standard asset hierarchy. Governed by Unity Catalog and using Lakebase as the operational store, the accelerator processes time-series data to track metrics like Specific Energy Consumption (SEC). This practical foundation gives energy managers the exact insights needed to optimize facility loads, cut costs, simplify compliance reporting, and reach concrete decarbonization targets. Read this blog to learn more. 

Solita’s Installed Base Foundation includes a Databricks reference architecture and reference data model to help equipment OEMs and asset-heavy operators gather mixed-fleet data under a single governed foundation. Built on the Databricks Data Intelligence Platform, it combines machine telemetry and service records using industry-standard asset models. Governed by Unity Catalog and using Lakebase as the operational store, the accelerator provides a clear, real-time view of fleet performance and availability. This practical foundation gives energy managers a structured basis to optimize facility loads and cut time to value, while giving teams a solid base to deliver digital services like predictive maintenance, asset live views, and service planning tools. Read this blog to learn more. 

Syren Cloud
Real-Time Order Visibility with Databricks Lakebase: A leading Global Indian power and distribution transformer manufacturer needed real-time order tracking and end-to-end visibility across a lifecycle that spanned presales, design, manufacturing, inspection, and dispatch, but data was scattered across siloed enterprise systems. Syren experts built-on Databricks a full-stack portal with two faces: a customer-facing B2B portal giving external clients live order tracking, and an internal tracker for eleven engineering and operations roles. Databricks Lakebase serves as the single operational database, handling live application writes while serving ERP-sourced Gold data replicated via Scheduled Sync, with no separate OLTP database and no custom CDC pipeline. The result collapsed three traditional systems into one, delivering low-latency unified order views to customers and internal teams alike. Read more about Syren’s expert solutions on Databricks here.

Tredence 
T-Discovery for Manufacturing: Real-Time Feature Engineering Accelerator for Lakebase: T-Discovery uses agentic AI to solve the hardest part of real-time ML: knowing what features to build. Domain experts describe business objectives in natural language; Milky Way's agentic hypothesis discovery engine explores the lakehouse, generates feature hypotheses, and validates candidates against labeled outcomes — replacing weeks of manual notebook exploration. The output is production-ready features with Unity Catalog metadata and primary/foreign key definitions, ready for Spark Real-Time Mode to execute and Lakebase Online Feature Store to serve. T-Discovery discovers, builds the SQL, and validates. The Databricks platform handles everything else.

Zeb 
zeb Agentic Lakebase for Manufacturing and Energy is a productized, agent-native pattern that operationalizes Databricks' "database for agents" positioning. An AI agent receives a scoped Lakebase environment as its persistent memory and transactional runtime, with tools to query, execute, evolve schemas, and ingest data. It runs in two modes. Greenfield: the agent takes a business prompt, provisions Lakebase, designs the data model, generates application code, and auto-deploys a live Databricks App. Brownfield: the agent ingests an existing prototype from Lovable, Bolt, v0, or Cursor, infers the model, and migrates it to production. Where others give agents read-only context, zeb gives full transactional ownership. Read this blog to learn more. 

Retail, CPG, Travel and Hospitality   

Avanade
Retail Fit Room using Lakebase: A leading UK fashion retailer modernised its fit room process using an agent-based AI solution built on Databricks Apps, Lakehouse, and Lakebase. Manual, fragmented workflows for capturing and consolidating fit notes and images were replaced with a real-time, in-session experience, where technologists dictate observations and capture photos that are instantly transcribed, structured, and enriched by AI. Governed through Unity Catalog and powered by Lakebase for operational workloads, the solution unifies transactional and analytical data, enabling seamless integration with supplier systems. This approach eliminates post-session rework, reduces resource requirements, and accelerates supplier communication, while establishing a scalable foundation for AI-driven product development.

Celebal Technologies
The Dynamic Pricing Accelerator for Aviation is using Databricks Lakebase to transform pricing from a reactive process into a real-time revenue intelligence capability. While Databricks Lakehouse provides the analytical foundation for demand forecasting, elasticity modeling, competitor intelligence, and price optimization, Lakebase delivers the operational layer required to execute decisions at market speed. Zero-copy Lakebase branches power scenario simulations, while transactional records manage pricing proposals, approvals, decision logs, and Genie session state. With sub-10 ms reads and writes and native Unity Catalog governance, the platform connects insight to action across the pricing lifecycle. Read this blog to see how Lakebase helps close the gap between pricing intelligence and operational decision-making.

CI&T
Reflex: POS Data Activation Solution: From Reports to Reflexes - How Databricks Lakebase and Agent Bricks are rewiring decision latency in Retail & CPG: Reflex turns POS data into action in under 90 seconds. Built on Databricks Lakebase + Agent Bricks. No Kafka, no Debezium, no overnight ETL. Retail has spent a decade optimizing dashboards while the gap between transaction and decision remains measured in hours. The real bottleneck is architectural: OLTP and OLAP live in separate systems, stitched together by brittle middleware. Databricks Lakebase collapses that divide — POS transactions reach the Lakehouse in under 60 seconds, with no Kafka, no Debezium, no overnight ETL. Pair this with Agent Bricks, and monitoring stops being a human task: autonomous agents detect stockouts, draft replenishment orders, and trigger markdowns before a manager notices the empty shelf. This is the foundation of Reflex — CI&T's operating model for Retail & CPG, where data infrastructure becomes a reflex, not a report. Read this blog to learn more. 

Cognizant
LiveLink — Real-Time VIP Cart Rescue on Databricks Lakebase (VIP Rescue Accelerator): Retailers already hold the data to act on a high-value customer — cart value, lifetime value, tier, purchase history — but it only meets in a nightly batch, long after the moment has passed. This accelerator closes that gap on Databricks. Live app and kiosk check-ins land in Lakebase (managed Postgres), and Unity Catalog federation joins them in place to historical customer value in Delta Gold — no streaming pipeline, no reverse-ETL, no copies. One governed view powers a store-manager dashboard, AI/BI Genie for executives, and an in-store signal to associates, turning abandoned VIP carts into recovered revenue while the customer is still in the store. Read this blog to learn more. 

Datasentics
Real-time pricing decisions with Databricks Lakebase: Retail pricing runs across thousands of SKUs, and most teams still steer it by competitor feeds and gut feel. Margin gets left on the table, and the effect of a price change only shows up later in the P&L instead of in a simulation. PriceWise models demand and price elasticity per product, projects the impact on revenue, margin, and volume before anything ships, and recommends a strategy per segment inside the retailer's own rules and guardrails. The interactive layer is where Lakebase earns its place. PriceWise runs on a managed Postgres database that keeps pricing and product data continuously synced from Unity Catalog, so every simulation and price lookup answers in milliseconds while the data stays fully governed inside Databricks. A/B tests back the numbers: 36% revenue growth in a pilot market, 12% sustained at rollout, up to 5% margin gains, and one production pricing program returning €470k a year. Rule-based tools typically land in single digits.

Infocepts
Infocepts OptiStoreAI is a suite of AI‑powered retail solutions that integrates seamlessly with existing store operations to improve operational efficiency, reduce downtime, and elevates shopper experience. With its Databricks native architecture comprising of Unity Catalog, Lakehouse, Lakebase, MLOps, AI/BI Dashboards and Databricks Apps,  it enables faster identification of underperforming stores with comprehensive diagnostics, impact analysis, proactive store‑wise alerts, and recommendations to prevent lost sales powered by AgentBricks and AI/BI Genie. Leverage OptiStoreAI to address a range of use cases from store performance, store launch risk, expansion intelligence, planogram compliance to store assist across your retail network. Watch this demo to learn more. 

Koantek
Customer Intelligence based on Ascend AI AppBase productizes Databricks Apps and Lakebase best practices into governed operational-app delivery. A growing library of Lakebase-first starter kits runs on a shared Data-Intelligent Starter foundation, starting with customer intelligence, AI agent operations, risk and compliance, and industrial operations. Each kit serves Unity Catalog data via Synced Tables, stores transactional app state in Lakebase, and ships via app resources, valueFrom bindings, service-principal permissions, and Declarative Automation Bundles. Koantek adds the field layer that moves a kit beyond a demo: vertical schemas, grant matrices, generated bundles, QA harnesses, and proof-ready evidence. Read this blog to learn more. 

LatentView Analytics
CatalogMate is LatentView's agentic content intelligence engine built to scale product content and accelerate retail growth. As AI-powered discovery gateways redefine how consumers search, compare and buy, CatalogMate decodes multimodal product sheets into high-performing, brand-compliant PDP copy, eliminating manual bottlenecks. At its core, Databricks Lakebase serves as the low-latency operational data layer a Postgres-native transactional store that grounds every generation in live, structured product and brand attributes, so copy is always built from the current catalog rather than stale exports. Because Lakebase unifies this operational data directly on the Databricks platform, CatalogMate continuously refreshes content, pivots with seasonal demand, and integrates real-time customer feedback to keep products discoverable, writing back updated content and engagement signals to the same source of truth in real time. A built-in human-in-the-loop layer keeps copywriters in control, verifying and approving every output before it goes live. It connects to brand guardrails held in Lakebase and enforces brand and legal compliance across the pipeline. Optimized for SEO, GEO, and AEO, CatalogMate is engineered to maximize visibility across AI-driven discovery channels. Powered by Databricks - from 90-day proof-of-value to global scale. Read this blog to learn more. 

Lingaro
T-Ops Twin: Databricks Lakebase is the operational system of record for T-Ops Twin, a modular transportation command center and digital twin for global FMCG logistics unifying real-time visibility, governed workflows, approvals, carrier collaboration, and analytics into one operating model across multi-region, multi-mode, multi-carrier networks. Lakebase provides the near-real-time data path, holding workflow/shipment/task updates, search and document metadata, and geospatial snapshots for the live twin. T-Ops Twin solves the challenges caused by dispersed data and processes across TMS, ERP, email, spreadsheets, and carrier channels; late, reactive exception handling with unclear ownership and long cycle times and manual, lengthy approval paths with weak threshold controls. Read this blog to learn more. 

Lovelytics
SKUlytics: SKUlytics, a strategic retail intelligence platform by Lovelytics, transforms disconnected data into a measurable competitive advantage. By unifying macro-market intelligence with SKU-level precision, the platform empowers teams to grow sales and outpace the competition. It focuses on four critical pillars: Brand Visibility, Engagement, Conversion, and Optimization, tracking key drivers like SEO rank, content health, and stock availability. These insights drive quantifiable business value, achieving up to 98% On-Shelf Availability (OSA), 20% ROAS improvement, and 95% automation of data flows. Ultimately, SKUlytics harmonizes media and pricing data to optimize the customer experience and protect profitability margins. Watch this demo and read this blog to learn more. 

Lovelytics Merchlytics - Data products for retail merchandising: Merchlytics establishes a Foundational Data infrastructure that eliminates manual reconciliation, providing a unified 360-degree view of sales and performance. The suite drives quantifiable value through AI-driven Forecasting, optimizing reorder points to reduce out-of-stocks and excess inventory. By leveraging Vendor Scorecards and Assortment Optimization, teams can rationalize SKUs and align localized inventory with specific demand patterns. Finally, the platform maximizes Pricing & Promo Effectiveness, utilizing scenario planning to reduce margin leakage and ensure optimal price points across all channels. Watch this demo to learn more. 

Manuka
Manuka TwinOS: Manuka's TwinOS is a Databricks native retail and CPG digital twin built on Lakebase, designed to unify operational data, analytical intelligence, and AI driven action in one platform. It creates a live model of stores, suppliers, inventory, promotions, distribution and fulfillment flows, then connects that model to Lakehouse analytics and conversational decision experiences. With Lakebase as the low-latency operational backbone, TwinOS enables real-time monitoring, scenario simulation, and agentic workflows for supply chain, merchandising, and commercial teams. The result is a governed, production-ready decision OS that helps retailers and CPG brands move from insight to action faster. Monitor. Predict. Decide. Act. Watch this demo and read this blog to learn more. 

MathCo
Lakebase Accelerator for Always-on Marketing Mix Modeling (MMM): Bridging the gap to real-time MMM demands more than better models. It requires purpose-built infrastructure. Enterprises face three critical gaps: no persistent data and context layer, no low-latency access for decisioning, and no reusable data products at scale. Lakebase Accelerator powering MathCo’s solution - Always-On MMM directly addresses each. A Unified Data & Context Fabric built on Lakebase, Delta, and Unity Catalog creates a governed, reusable foundation. HTAP-powered tables enable real-time querying and simulation. The Application & Consumption Layer delivers live dashboards and embedded decision intelligence, while the Decision Intelligence & Agent Layer automates scenario planning and budget optimization, driving a 90% increase in consumption through real-time, Lakebase-powered apps. Read this blog to learn more. 

Sigmoid Analytics
Marketing Budget Optimization: A global beverage enterprise’s scenario planning platform faced severe operational friction because its transactional MLAPI and UI databases were siloed on Azure PostgreSQL, entirely separate from its core Databricks analytics and GenAI workloads. This architectural split required custom connectors, created an unsupported infrastructure gap, and introduced a 24-to-48-hour latency delay between analytical insights and operational activation. To eliminate these friction points, the enterprise migrated to Sigmoid LatticeIQ, powered natively by Databricks Lakebase. This shift unified the platform under a single Unity Catalog governance plane with connector-less Spark integration, completely eliminating 2 bespoke ETL pipelines. Consequently, the solution helped in unlocking sub-10ms UI query latency, thereby planners to make faster decisions, resulting in a 50% higher marketing ROI, and a 30% increase in data coverage.

Sigmoid Analytics’ AI-native CDP: A leading CPG enterprise faced severe operational delays because its operational consumer profile data was siloed from analytical models running on Databricks. This disjointed architecture required custom ETL pipelines, introducing a 24-to-48-hour latency gap and dual governance overhead for the same consumer records. To resolve these bottlenecks, the company deployed Sigmoid LatticeIQ, leveraging Lakebase as the operational serving layer for its Consumer Data Platform. LatticeIQ's Synced Tables successfully delivered sub-10ms query latency while Moonlink CDC eliminated 2 bespoke pipelines. This unified framework processed over 1M+ unique records and achieved 92% profile accuracy.

Sigmoid Analytics’ Agentic S&OP Forecasting: A premium FMCG enterprise faced unacceptable user interface latency when attempting to serve demand forecast insights directly from Databricks Gold tables to operations analysts. Spinning up a traditional standalone operational database would have required building a new ETL pipeline, introducing significant maintenance overhead and data freshness lag. To resolve these bottlenecks, the company deployed Sigmoid LatticeIQ as a unified database serving layer. Driven by 3 weekly Databricks Workflow jobs and Mosaic AI Agent Bricks, the architecture eliminates all separate ETL infrastructure. This configuration delivered interactive sub-10ms UI query latency alongside scale-to-zero cost efficiencies.

Zeb
Zeb Agentic Lakebase for Retail, CPG and Travel and Hospitality is a productized, agent-native pattern that operationalizes Databricks' "database for agents" positioning. An AI agent receives a scoped Lakebase environment as its persistent memory and transactional runtime, with tools to query, execute, evolve schemas, and ingest data. It runs in two modes. Greenfield: the agent takes a business prompt, provisions Lakebase, designs the data model, generates application code, and auto-deploys a live Databricks App. Brownfield: the agent ingests an existing prototype from Lovable, Bolt, v0, or Cursor, infers the model, and migrates it to production. Where others give agents read-only context, zeb gives full transactional ownership. Read this blog to learn more. 

Healthcare and Life Sciences   

CitiusTech
Metadata Driven Ingestion Framework (MDIF) and Data Quality: Healthcare organizations face growing challenges in ingesting and harmonizing data from diverse sources such as EHRs, payor systems, labs, and third-party platforms arriving in multiple formats including RDBMS, CSV, HL7, EDI, and FHIR. The absence of standardized orchestration leads to redundant pipelines, high engineering effort, inconsistent data quality, and increased risk from schema evolution during M&A or source-system upgrades. CitiusTech’s integrated Metadata Driven Ingestion Framework (MDIF) and Data Quality solution, powered by Databricks Lakebase, provides a configuration-driven approach to source onboarding, validation, and governance. With a central metadata repository, intuitive UI, ADF-based orchestration, and Databricks notebooks, it simplifies onboarding and improves trust in Lakebase data. Read this blog to learn more.

Cognizant
No More Waiting on Care: Real-Time Prior Authorization with Databricks Lakebase: Prior authorization decisions in utilization management often stall provider workflows because analytical lakehouse tables aren't built for instant, point-in-time lookups. The HealthCare Utilization Management platform solves this by adopting Databricks Lakebase — a fully managed, Postgres-compatible operational layer natively governed through Unity Catalog. Synced Tables replicate Gold-layer eligibility, benefit, provider, and clinical guideline data into Lakebase in near real time, while read replicas and autoscaling absorb high-concurrency provider traffic. The result: sub-millisecond authorization lookups, faster clinical decisions, and a single governed architecture spanning operational and analytical workloads — without a separate database to manage. Read this blog to learn more. 

Manuka AI
CareIQ by Manuka AI is a Databricks-native solution built on Lakebase for behavioral health, autism services, and ABA therapy. It unifies clinical operations, revenue cycle, staffing, and patient engagement into a real-time, decision-ready system. CareIQ creates a live operational model of patients, providers, authorizations, sessions, claims, and payer interactions, connecting it to Lakehouse analytics and AI-driven workflows. Lakebase enables monitoring of utilization, authorization leakage, staffing capacity, payer performance, scheduling, and agentic workflows. Genie-powered conversational interfaces enable natural language queries and actions, helping providers improve utilization, reduce revenue leakage, and support better outcomes for children and families. Watch this demo to learn more. 

Persistent Systems
CRO Analytics in a Box: CRO Analytics in a Box is a Databricks-native accelerator that integrates clinical, operational, quality, and financial data from multiple CRO and sponsor systems into a governed Lakehouse. Using a medallion architecture, it transforms raw real-time and historical data into standardized, business-ready data products for study oversight, site performance, enrollment, quality, and financial analytics. Databricks Genie and AI/BI provide conversational and dashboard-based insight consumption, while Lakebase adds an operational Postgres layer for low-latency serving, workflow applications, and writeback-driven action tracking. Together, the solution enables not only insight generation, but operational execution and closed-loop performance improvement. Read this blog to learn more. 

SunnyData
SunnyCoach for Healthcare & Life Sciences is an AI simulation platform where payer and provider contact center teams practice high-stakes member and patient conversations—claim denial appeals, benefit coverage questions, billing disputes, triage calls—with realistic, voice-based AI personas, receiving rubric-scored feedback on empathy, accuracy, and compliance in seconds. Lakebase is the operational system of record: every conversational turn writes session state, scores, and agent progress for low-latency reads and resumable sessions, while that same data flows into medallion tables for program analytics. Databricks serving endpoints power the AI personas and the real-time feedback engine, governed end-to-end by Unity Catalog. 

Zeb
Zeb Agentic Lakebase for Healthcare is a productized, agent-native pattern that operationalizes Databricks' "database for agents" positioning. An AI agent receives a scoped Lakebase environment as its persistent memory and transactional runtime, with tools to query, execute, evolve schemas, and ingest data. It runs in two modes. Greenfield: the agent takes a business prompt, provisions Lakebase, designs the data model, generates application code, and auto-deploys a live Databricks App. Brownfield: the agent ingests an existing prototype from Lovable, Bolt, v0, or Cursor, infers the model, and migrates it to production. Where others give agents read-only context, zeb gives full transactional ownership. Read this blog to learn more. 

Communications, Media, Entertainment and Gaming  

Lovelytics
Audiencelytics: Audiencelytics is a sophisticated audience intelligence solution developed by Lovelytics in partnership with Locality to revolutionize local advertising. By leveraging the Databricks Data Intelligence Platform, it unifies fragmented viewership data into a single, actionable asset for brands. The platform utilizes generative AI and automation to streamline campaign planning, marketing activation, and outcome measurement across both linear and streaming channels. This centralized approach eliminates siloed workflows and reliance on expensive third-party processors, significantly reducing operational costs. Ultimately, Audiencelytics empowers advertisers with precise targeting and real-time insights, ensuring local media campaigns are both highly scalable and consistently effective. Watch this video and read this blog to learn more. 

Lovelytics Churnlytics: Churnlytics is an advanced predictive analytics solution developed by Lovelytics to help organizations in the communications, media, and entertainment sectors combat customer attrition. Built on the Databricks Data Intelligence Platform, it integrates disparate data sources—such as usage patterns, billing history, and customer interactions—to create a unified view of subscriber behavior. By applying machine learning models, Churnlytics identifies "at-risk" customers before they leave, allowing businesses to launch proactive, personalized retention campaigns. This data-driven approach shifts companies from reactive responses to strategic prevention, ultimately protecting recurring revenue and increasing long-term customer lifetime value through actionable, real-time intelligence. Watch this video to learn more. 

Wipro
Wipro Telecom Sales and Marketing Accelerator: This solution targets the Telecom Small & Medium Business (SMB) segment by re-engineering the complete Customer Lifecycle—from Prospecting and Conversion to Selling, Onboarding, Installation, Service, and continuous Cross-sell/Upsell. The Wipro Agentic AI solution, powered by Lakebase and Genie, enables intelligent, context-aware decisioning across Sales, Marketing, Customer Service and Revenue Management. By unifying Customer 360 intelligence, propensity-driven insights, dynamic pricing, real-time recommendations and zero-touch fulfilment, telcos can deliver B2C-like simplicity while ensuring B2B regulatory compliance - driving faster deal closures, higher ARPU, reduced churn and sustainable global growth of the SMB business.

Zeb
Zeb Agentic Lakebase for Communications and Media is a productized, agent-native pattern that operationalizes Databricks' "database for agents" positioning. An AI agent receives a scoped Lakebase environment as its persistent memory and transactional runtime, with tools to query, execute, evolve schemas, and ingest data. It runs in two modes. Greenfield: the agent takes a business prompt, provisions Lakebase, designs the data model, generates application code, and auto-deploys a live Databricks App. Brownfield: the agent ingests an existing prototype from Lovable, Bolt, v0, or Cursor, infers the model, and migrates it to production. Where others give agents read-only context, zeb gives full transactional ownership. Read this blog to learn more. 

Public Sector  

Slalom
LakeSpeak: One of Slalom’s public sector customers is modernizing emergency response by using AI-powered tools like LakeSpeak, Slalom’s MCP powered Brickbuilder accelerator, to create dynamic, real-time Situation Reports. This enhances decision-making, reduces manual reporting, and offers an AI assistant for targeted data queries during disasters. LakeSpeak delivers a secure, standardized gateway to expose Databricks Genie and Lakebase to external apps, agents, and enterprise users - without duplicating logic, breaking governance, or rewriting integration patterns. Read this blog to learn more. 

Zeb
Zeb Agentic Lakebase for Public Sector is a productized, agent-native pattern that operationalizes Databricks' "database for agents" positioning. An AI agent receives a scoped Lakebase environment as its persistent memory and transactional runtime, with tools to query, execute, evolve schemas, and ingest data. It runs in two modes. Greenfield: the agent takes a business prompt, provisions Lakebase, designs the data model, generates application code, and auto-deploys a live Databricks App. Brownfield: the agent ingests an existing prototype from Lovable, Bolt, v0, or Cursor, infers the model, and migrates it to production. Where others give agents read-only context, zeb gives full transactional ownership. Read this blog to learn more. 

Call to Action

Accelerate Data and AI Outcomes with Partner Solutions 

The through-line across every one of these solutions is the same: when operational data, analytics, and AI share one governed foundation, the distance between insight and action collapses - and it's industry expertise that turns that speed into measurable business value. The partners featured here have already moved from architecture to production, and the accelerators they've built are ready to deploy in your environment now. Connect with your partner or Databricks account team to scope a pilot. Whatever your industry, the fastest path to a Lakebase-powered advantage is a partner who already knows the terrain.

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