The models that will define your market are trained on data your competitors do not have. You have it.
Most enterprise AI runs on shared foundation models trained on public internet data. That is a commodity anyone can buy. The real advantage is an AI system fine-tuned on your proprietary operations, your domain expertise, your customer history, running entirely inside your infrastructure, governed by your rules, owned by you. That is what we build on Databricks.
Bring the AI to your data.
Not your data to the AI.
The traditional pattern
Every traditional AI integration follows the same dangerous pattern. Your most sensitive operational data (customer records, contracts, financial models, clinical notes) leaves your infrastructure and travels to a third-party API somewhere on the public internet. The vendor learns from it. Their model improves. Your data is gone.
The Databricks inversion
The models run inside your cloud environment, directly against your data. Nothing moves. Nothing leaks. Your proprietary data is the intelligence advantage. And it stays yours.
We build on three layers every time.
01
The Data Lakehouse
One unified environment for structured data (transactions, CRM, ERP tables) and unstructured data (PDFs, emails, call logs, contracts). No more moving data between systems to answer a single question.
02
Unity Catalog
Governance before the AI ever sees anything. PII is detected and stripped at the ingestion layer. Access is role-scoped. Every query is lineage-traced. Compliance is not bolted on. It is built into the foundation.
03
Mosaic AI
The AI engine. Fine-tune open-source models (Llama 3, DBRX) on your proprietary data. Run multi-agent workflows. Serve low-latency inference endpoints. Evaluate outputs continuously so hallucinations do not reach production.
Three production systems.
One Databricks environment.
We build across three distinct capability tracks depending on what your business needs. Most enterprise engagements combine at least two.
Secure Document Intelligence
For regulated industries with sensitive unstructured data
Your most valuable unstructured data (contracts, clinical records, compliance filings, policy documents) has been too sensitive to touch with most AI systems. We solve this at the architecture level. Before any document reaches the AI layer, Unity Catalog strips PII at ingestion: names, identifiers, financial data, protected health information. What the model works with is governed, clean, and traceable. Your legal and compliance teams finally get the AI capability they have needed, without the risk they could not accept.
Example use cases
Example query
"Flag all supplier agreements with change-of-control clauses where our entity is not listed as an approved assignee."
Example query
"Summarise all adverse clinical event narratives from trial documentation where the event occurred within 14 days of first dose."
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Conversational Analytics via Databricks Genie
For executives and operators who do not speak SQL
Databricks Genie replaces the BI ticket queue. We configure Genie Spaces mapped to your actual business vocabulary, your KPI definitions, your metric logic, your terminology. When a VP asks "how is the European business performing?" the system understands exactly what "the European business" means in your context. It generates the SQL, queries your live lakehouse, and returns structured results with real-time visualisations. No data analyst in the loop. No dashboard that was built three months ago for a question you are not asking today.
Example use cases
Example query
"Show net margin trends across our top 5 product lines in EMEA vs. APAC for the last 6 quarters, broken down by customer segment."
Example query
"Which distribution centres had the highest labour cost per unit shipped this month, and how does that compare to our Q1 baseline?"
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Multi-Agent Workforce Automation
For complex multi-step operational processes that break single-prompt systems
Single-agent AI fails the moment a business problem requires more than one data source, more than one decision, or more than one action. We build compound agent systems. A Supervisor Agent reads the incoming problem, breaks it into discrete sub-tasks, and delegates each to a specialised child agent. One checks your inventory database. Another validates against your policy documents. A third cross-references fraud patterns. The Supervisor synthesises the outputs into a coherent, auditable response. MLflow traces every step so you can see exactly what happened and why. This is how you replace manual multi-step workflows, not just a single chatbot interaction.
Example use cases
Example query
Claims triage agent: reads submission, checks policy terms, cross-references fraud pattern database, routes to correct handler, drafts decision rationale.
Example query
Procurement compliance agent: receives new vendor contract, checks against approved terms playbook, flags non-standard clauses, escalates to legal with a structured diff.
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Specific problems.
Specific answers.
Databricks works across every industry that runs on data. Which is all of them. The architecture is the same. The problems it solves are not. Select your industry.
Key pain · Risk and compliance data siloed. Insight arrives days after the decision needs to be made.
Risk teams produce reports manually from fragmented data lakes. Compliance requires human review of thousands of documents before any regulatory filing. Model risk committees cannot get consistent answers from the same data. And executives are making capital allocation decisions off dashboards that were built for the questions of last quarter, not this one. The Databricks lakehouse architecture was, in many ways, built for finance first.
Databricks Genie Analytics
User asks
"Show me our Tier 1 capital ratio by legal entity over the last 8 quarters alongside our internal stress test thresholds."
AI responds
Genie maps the query to your regulatory reporting tables and stress scenario datasets. Returns a time-series comparison by entity, with threshold breach flags and a footnote on which scenario assumptions are active. Finance review time drops from a day to a conversation.
User asks
"Which counterparty exposures in our derivatives book have grown more than 20% month-on-month and are in sectors with current credit watch status?"
AI responds
Queries across your trading book, counterparty master, and credit ratings tables simultaneously. Returns a ranked exposure list with month-on-month delta, sector classification, and current credit status, in under 10 seconds.
Outcome
Risk analysts stop building reports. They start making decisions.
Multi-Agent Workflow
Business process trigger
Regulatory filing cycle begins for quarterly ICAAP submission.
What the agent system does
- Supervisor Agent reads the filing schema.
- Data Agent pulls capital, RWA, and exposure figures from lakehouse tables.
- Validation Agent checks figures against prior period and internal policy thresholds.
- Document Agent cross-references narrative sections against approved language repository.
- Compliance Agent flags any delta from last submission that requires board-level sign-off.
- Final output: pre-drafted submission package with exceptions surfaced for human review.
Outcome
Regulatory filing prep that took 3 weeks of analyst time takes 3 days.
The Lakehouse compounds.
So does the intelligence built on it.
The three capabilities we build are the foundation. Teams that establish the Databricks lakehouse correctly unlock a set of higher-order AI capabilities, because the data architecture, the governance layer, and the model serving infrastructure are already there.
Proprietary Model Fine-Tuning
Once your data is clean and structured in the lakehouse, fine-tuning an open-source model (Llama 3, Mistral, DBRX) on your proprietary domain is the next logical step. A model trained on your clinical data, your legal precedents, or your engineering specs outperforms any general foundation model on your specific tasks. And you own it completely.
Advanced · Model ownership
Continuous Hallucination Monitoring
MLflow Evaluation does not just check model outputs at deployment. It continuously measures semantic precision, factual accuracy, and response drift in production. When a model starts hallucinating on a class of queries, the system flags it before your users notice. AI reliability becomes operational, not aspirational.
LLMOps · Production reliability
Enterprise-Wide AI Governance Layer
Unity Catalog's governance framework scales from one application to your entire AI estate. Every model, every dataset, every inference call carries lineage. Who ran this query, on what data, at what time, with what result. Auditable to regulators, internal audit, and your own risk function. Governance that actually works at enterprise scale.
Compliance · Regulated industries
Real-Time Streaming Intelligence
Delta Live Tables enables continuous data ingestion (IoT sensors, transaction streams, live market feeds, operational telemetry) with the same governance and AI capabilities applied to static data. The intelligence layer works on data as it arrives, not as of yesterday's batch load.
Advanced · Event-driven AI
Cross-Functional Agent Orchestration
As individual agent workflows mature, they can be connected across business functions. A customer escalation triggers agents in CS, finance, and operations simultaneously. A regulatory change triggers agents in legal, compliance, and product. The compound agent mesh becomes your operational nervous system.
Advanced · Enterprise-wide
Federated Analytics Across Subsidiaries
For organisations operating across regions, entities, or business units with separate data environments, Delta Sharing allows cross-lakehouse analytics without centralising the data. Each entity keeps its data local and governed. The analytics layer operates across all of them. One query, many lakehouses, zero data movement.
Enterprise · Multi-entity
Why Databricks,
not a cloud AI wrapper?
Your data never touches a shared model
Cloud AI APIs send your data to shared inference infrastructure where model providers can use it for training. Databricks runs inference inside your cloud account, on infrastructure you control. Your proprietary data trains your models. No one else benefits from it.
Structured and unstructured. One environment.
Most AI tools handle one or the other. Mosaic AI works natively across Delta Lake's structured tables and unstructured document stores in the same query. A single agent can cross-reference a contract clause with a transaction record without moving data between systems.
PII governance is built in, not bolted on
Unity Catalog strips PII at the ingestion layer, before data reaches any model. This is not a setting you configure. It is the architecture. Regulated industries (financial services, healthcare, insurance, legal) get AI capability without the compliance conversation that kills most AI projects.
You can measure and improve what you build
MLflow gives you continuous evaluation of every model in production: semantic precision, latency, cost-per-token, hallucination rate. Most AI deployments are black boxes. On Databricks, you have observability built into the foundation. The system gets better over time, measurably.
We do not build AI that phones home. Every model we train on Databricks, every agent we deploy, every pipeline we build runs inside your cloud, governed by your rules, owned by you. When we hand over the code, it is yours. The intelligence you build on your data does not belong to anyone else.
From messy data to production AI,
in 16 weeks.
Enterprise Databricks engagements follow a structured four-phase delivery. Every phase has a defined deliverable your team signs off before the next begins.
- 01
Discovery
Weeks 1–3
What happens
We audit your existing data infrastructure, map your unstructured document corpus, define compliance requirements, and identify the highest-ROI AI applications to build first.
What you get
Architectural blueprint, data readiness assessment, compliance boundary document, prioritised build roadmap.
- 02
Foundation
Weeks 4–7
What happens
We build the Medallion architecture (Bronze, Silver, Gold Delta tables), deploy Unity Catalog governance across your environment, and establish PII scrubbing pipelines. Your data is now AI-ready.
What you get
Live Databricks environment, Unity Catalog cluster, governed Gold tables, automated ingestion pipelines for all agreed data sources.
- 03
AI Pipeline
Weeks 8–12
What happens
We build the semantic chunking and embedding workflows, initialise Mosaic AI Vector Search, configure Genie Spaces with your business glossary, and deploy the first agent workflows.
What you get
Live vector indexes, Genie Space configured and tested with your actual queries, first multi-agent workflow in staging.
- 04
Production & Optimisation
Weeks 13–16
What happens
We launch the production-grade UI, embed MLflow tracing and evaluation across all deployed models, run load testing, and hand over with full documentation.
What you get
Production application, MLflow evaluation dashboard, runbook, full source code, and a 30-day post-launch support window as standard.