Sarvaswa AI Labs

Databricks Intelligence: document AI, Genie analytics and multi-agent automation on your Lakehouse.

The models that will define your market are trained on data your competitors do not have. You have it, sitting in Delta tables and document stores. We build the systems that use it on your Databricks Lakehouse, governed by Unity Catalog, and hand them over documented so your team can run them.

Book a callSee what we build
Built onDelta LakeUnity CatalogMosaic AI Vector SearchGenie SpacesMosaic AI Agent FrameworkMLflow

The architecture

How the pieces fit, from raw sources to answers.

Every Databricks build we do follows the same path. Data lands in Delta Lake, Unity Catalog governs it before any model sees it, Mosaic AI does the reasoning, and MLflow watches every output in production.

  1. 01

    Sources

    Documents, tables, streams, ERP

  2. 02

    Delta Lake

    Bronze, Silver, Gold tables

  3. 03

    Unity Catalog

    PII scrubbed, lineage traced

  4. 04

    Mosaic AI

    Vector Search, Genie, agents

  5. 05

    Your users

    Document Q&A, analytics, agents

  6. MLflow tracing and evaluation on every model, every query, in production.

The Databricks difference

Bring the AI to your data, not your data to the AI.

Most enterprise AI runs on shared foundation models reached through an API. That is a commodity anyone can buy, and it means your most sensitive data has to leave the platform to get an answer. Databricks inverts the pattern.

Calling an external AI API

  • Customer records, contracts and clinical notes travel to third-party inference outside your audit scope.
  • The vendor may learn from your data. Their model improves. Your advantage does not.
  • Structured and unstructured data live in different tools, so one question needs two systems.
  • Governance is bolted on afterwards, usually by the compliance conversation that stalls the project.

Building on your Databricks Lakehouse

  • Models run on Databricks Model Serving, against data that stays in Delta Lake.
  • Open-weight models fine-tuned on your data keep improving as the data grows.
  • Delta Lake holds tables and documents together, so one agent can cross-reference both.
  • Unity Catalog strips PII at ingestion and traces every query, so governance is structural.

The three layers below are what we build on every time: the Lakehouse for storage, Unity Catalog for governance, and Mosaic AI for the models and agents.

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.

What we build

Three production systems we build on Databricks.

Secure document intelligence, conversational analytics through Genie, and multi-agent workforce automation. Most enterprise engagements combine at least two, on one Databricks environment.

System 01

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.

How we handle data residency and audit evidence

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."

Stack

Mosaic AI Vector SearchDelta Lake Gold TablesUnity Catalog PII scrubbingMLflow Evaluation
System 02

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.

The same pattern on Snowflake Cortex

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?"

Stack

Databricks Genie SpacesServerless SQL WarehousesSemantic Business GlossariesDelta Live Tables
System 03

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.

How we make agents production-ready

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.

Stack

Mosaic AI Agent FrameworkMLflow TracingLangChain / LlamaIndexDatabricks Model Serving

Already on Databricks?

Fifteen minutes to work out which of the three systems to build first.

Bring the document corpus nobody can query, the dashboard that answers last quarter's questions, or the process that still needs three people and a spreadsheet. We will tell you which system fits, what the first four weeks would look like in your workspace, and whether the data is ready.

Book a callSee the industry playbooks

Industry playbooks

Databricks AI use cases by industry.

The architecture is the same in every industry. The questions it answers are not. Pick yours to see the queries, the agent workflows and the outcome. The scenarios are illustrative: the figures show the shape of the output, not measured client results.

Databricks for Financial Services

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

  1. Supervisor Agent reads the filing schema.
  2. Data Agent pulls capital, RWA, and exposure figures from lakehouse tables.
  3. Validation Agent checks figures against prior period and internal policy thresholds.
  4. Document Agent cross-references narrative sections against approved language repository.
  5. Compliance Agent flags any delta from last submission that requires board-level sign-off.
  6. 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.

What is possible next

What becomes possible once the Lakehouse is in place.

The three systems are the foundation. Teams that establish the Lakehouse correctly grow into higher-order capabilities quickly, because the data architecture, the governance layer and the model serving 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.

Fine-tuned models

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.

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.

Governance for 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.

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.

Enterprise-wide orchestration

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.

Analytics across entities

The architecture argument

Why Databricks rather than a cloud AI API?

Your data never trains a shared model

Calling an external AI API puts your data outside your audit scope. On Databricks, inference runs on Model Serving under the governance you already operate, and your proprietary data improves only your models.

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

We configure PII detection and masking at the ingestion layer in Unity Catalog, before data reaches any model, so governance is structural rather than an afterthought. 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.

What you get

What is in your workspace when we hand over.

Every model, agent and pipeline runs in your Databricks workspace under the governance you already operate. These are the artefacts your team receives at the end of an engagement.

  • 01

    Governed Medallion tables

    Bronze, Silver and Gold Delta tables with automated ingestion for every agreed source, and Unity Catalog PII scrubbing at the Bronze layer.

  • 02

    Vector indexes and retrieval

    Mosaic AI Vector Search indexes with semantic chunking, embedding pipelines and cited retrieval over your document corpus.

  • 03

    A Genie Space that speaks your language

    Genie configured against your KPI definitions, metric logic and business glossary, tested with the questions your executives actually ask.

  • 04

    Agent workflows with traces

    Supervisor and child agents on the Mosaic AI Agent Framework, with every step traced in MLflow so the output is auditable.

  • 05

    An evaluation dashboard

    MLflow Evaluation measuring semantic precision, hallucination rate, latency and cost per token, running continuously in production.

  • 06

    Repository, runbook and support

    The full repository, a runbook your team can operate from, and a thirty-day post-launch support window as standard.

How it works

How a Databricks engagement runs, week by week.

From messy data to production AI in twelve to sixteen weeks, across four phases. Every phase has a defined deliverable your team signs off before the next begins.

  1. 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.

  2. 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.

  3. 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.

  4. 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, repository, and a 30-day post-launch support window as standard.

Databricks Intelligence, answered.

Databricks Intelligence is a set of three custom production systems Sarvaswa builds on a customer's Databricks environment. Secure Document Intelligence makes sensitive unstructured content (contracts, clinical records, compliance filings, policy documents) safely queryable, with PII stripped at ingestion via Unity Catalog. Conversational Analytics uses Databricks Genie Spaces mapped to the customer's business vocabulary so executives and operators get SQL-grounded answers without a data analyst in the loop. Multi-Agent Workforce Automation builds compound agent systems on the Mosaic AI Agent Framework for complex multi-step processes that single-prompt AI cannot handle. Every system runs inside the customer's cloud account.
Foundation model APIs send your data to shared inference infrastructure. Databricks runs the models on Model Serving under Unity Catalog governance, against data that stays in Delta Lake. You can also fine-tune open-source models (Llama 3, DBRX, Mistral) on your proprietary data, which a foundation API cannot do, so the domain advantage compounds in a model only you run.
Yes. Every model, agent and pipeline runs in your Databricks workspace on Databricks Model Serving, under Unity Catalog governance. No model provider sees your data.
Delta Lake for structured and unstructured storage with the Medallion architecture (Bronze, Silver, Gold). Unity Catalog for governance, PII scrubbing at ingestion, role-based access, and lineage tracing. Mosaic AI Vector Search for semantic retrieval. Mosaic AI Agent Framework for multi-agent workflows. Databricks Genie Spaces and Serverless SQL Warehouses for conversational analytics. Delta Live Tables for streaming ingestion. MLflow Tracing and Evaluation for production model monitoring.
Yes. We configure Unity Catalog to detect and mask PII at the ingestion layer, before any data reaches the AI model. Access is role-scoped to the customer's existing entitlements. Every query is lineage-traced and auditable. We design so your architecture review has nothing to push back on: every component sits under the governance Unity Catalog already applies.
Most engagements move from kickoff to production in 12 to 16 weeks across a four-phase delivery. Phase 1 Discovery (weeks 1–3) defines architecture and scope. Phase 2 Foundation (weeks 4–7) deploys the Medallion architecture and Unity Catalog governance. Phase 3 AI Pipeline (weeks 8–12) builds vector indexes, configures Genie Spaces, and ships the first agent workflows. Phase 4 Production and Optimisation (weeks 13–16) launches the production UI, embeds MLflow evaluation, runs load testing, and hands over.
Both, depending on the engagement. Foundation models (Claude, Llama, DBRX) handle most general reasoning tasks well. We fine-tune open-source models on your proprietary data when domain accuracy on a specific task matters more than general capability. The Databricks Mosaic AI Training stack supports parameter-efficient fine-tuning so you do not have to train from scratch.
Every model, agent and pipeline runs in your Databricks workspace under Unity Catalog governance. At handover Sarvaswa delivers the repository, MLflow evaluation dashboards, runbooks, and a 30-day post-launch support window, so your team operates and extends the system independently.
MLflow Evaluation runs continuously in production. It measures semantic precision, factual accuracy, response drift, latency, and cost-per-token across every deployed model. When a model starts hallucinating on a class of queries, the system flags it before users notice. Reliability is part of the architecture, not a quarterly check.
Yes. Delta Lake stores structured tables and unstructured document corpora in the same governed environment. Mosaic AI Vector Search and Genie Spaces operate across both. A single agent can cross-reference a contract clause with a transaction record without moving data between systems, which is one of the core architectural reasons for choosing Databricks in the first place.
It works with your existing workspace in most cases. Phase 1 Discovery audits your current environment, Unity Catalog configuration, and data infrastructure to decide whether the build extends what is there or warrants a clean parallel environment. Either path is supported.
Databricks runs natively on AWS, Azure, and Google Cloud. For multi-region or multi-entity organisations, Delta Sharing allows federated analytics across separate lakehouses without centralising the data. Each entity keeps its data local and governed. One query can operate across all of them with zero data movement.

On Snowflake instead? See Snowflake Intelligence. Still working through data residency and access control before choosing a platform? Start with enterprise AI. Curious about the fine-tuning step? Read the fine-tuning explainer, or browse all services.

Your data is the advantage. Let us build the AI that uses it.

Whether you start with document intelligence, Genie analytics or multi-agent automation, we scope it, build it inside your Databricks account, and hand it over. Most teams reach production in twelve to sixteen weeks.

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