Vertical Advantage: Transforming Industries with Lakebase and Agentic AI

Vertical Advantage: Transforming Industries with Lakebase and Agentic AI | Databricks Blog

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Leading consulting and SI partners have built production-ready solutions on Databricks Lakebase that pair deep industry expertise with a single governed foundation for operational data, analytics, and AI.

Industry-specific solutions span financial services, manufacturing and energy, retail, CPG, travel and hospitality, healthcare and life sciences, communications and media, and the public sector — from regulatory-change and fraud agents to real-time claims, prior authorization, dynamic pricing, and grid intelligence.

Every solution is ready to deploy today, using Lakebase's serverless Postgres, sub-10ms operational serving, zero-copy branching, and agent-native memory to collapse the distance between insight and action and turn platform capability into measurable business value.

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 custome