Sarvaswa AI Labs

Snowflake Intelligence: document search and natural language analytics on Snowflake Cortex.

Your Snowflake is full of answers and almost nobody can reach them. Employees search five tools and find nothing. Analysts queue for reports. We build two intelligence layers on the Snowflake you already run, on Cortex Search and Cortex Analyst, and hand them over documented so your team can run them.

Book a callSee what we build
Built onCortex SearchCortex AnalystRole-based access controlDynamic data maskingSnowflake audit logs

How it works

How Snowflake Intelligence works, from sources to answers.

Knowledge sources land in the Snowflake you already run. Cortex Search and Cortex Analyst do the retrieval and the SQL. Two intelligence layers sit on top, and every team gets the access its Snowflake role already allows.

Data sources

  • Confluence / Notion
  • Jira / Linear
  • PDFs & Documents
  • CRM (Salesforce / HubSpot)
  • Email threads
  • SQL Databases

Your data warehouse

Snowflake

Cortex Search, Cortex Analyst, governance. Already in place.

Intelligence layers

  • Document Intelligence

    Semantic search and AI Q&A across all of your knowledge sources.

  • NL Analytics

    English questions become SQL, then answers with written explanations.

Who gets access

  • Operations teams
  • Business analysts
  • Executives
  • New employees
  • Product managers

The real cost

Why your Snowflake data goes unused.

The problem is not a shortage of data. Most enterprise Snowflake environments hold years of structured and unstructured information. The bottleneck is access: finding the right document, getting a query written, and trusting the result all depend on a person who is already busy.

Today, in most Snowflake accounts

  • Employees search Confluence, Jira, shared drives and email, then ask a colleague, and still cannot be sure the answer is current.
  • A simple business question waits in the data team's queue behind everything else, and the decision waits with it.
  • Dashboards answer last quarter's questions. Anything new needs an analyst and a ticket.
  • The warehouse is queried by a handful of engineers. Everyone else works from memory and instinct.

With the two intelligence layers

  • One place to ask, in plain language, with the answer cited back to the source document.
  • English questions become schema-aware SQL through Cortex Analyst, executed against live tables, explained in writing.
  • Complex questions chain queries and come back as a short written report, not a table to interpret.
  • Every team member sees exactly what their Snowflake role already allows. Nothing more, nothing less.

What we build

Two systems we build on Snowflake Cortex.

Both run on your existing Snowflake environment. No new data warehouse, no migration, and no new vendor for your security team to approve.

System 01

Document Intelligence & Knowledge Base

Every document your company has ever produced gets ingested into Snowflake and made intelligently searchable. Confluence, Jira, PDFs, CRM notes, email threads, and internal databases. Employees ask questions in plain language and get precise, cited answers. Not a list of links. The actual answer, with the source document linked. This is your institutional memory, finally retrievable.

Put the same answers inside Slack

Example question

“What are our service-level commitments to enterprise customers for P1 incidents?”

Example question

“What did the product team decide about mobile architecture in the Q3 review?”

System 02

Natural Language Analytics

Business users ask questions in plain English. The AI writes schema-aware SQL, executes it against your Snowflake tables, and returns results with a plain-language explanation. Not just a table of numbers. For complex questions, the system chains multiple queries, identifies patterns across dimensions, and generates a written report. No analyst required. No ticket. No wait.

The same pattern with Genie on Databricks

Example question

“Which customer segments had the highest churn rate last quarter?”

Example question

“Why did conversion drop in the Northeast in October? Show me the breakdown.”

Already on Snowflake?

Fifteen minutes to work out which layer to build first.

Bring the question your team asks most and cannot answer without an analyst, or the document set nobody can search. We will tell you which layer fits, which Cortex features it uses, and what the first two weeks of discovery would map in your account.

Book a callSee the industry playbooks

Industry playbooks

Snowflake AI use cases by industry.

Every industry has the same underlying problem of fragmented knowledge and inaccessible data. The questions, the stakes and the payoffs differ. Pick yours. The conversations are illustrative: they show the shape of the answers, not measured client results.

Snowflake for Financial Services

Compliance burden and analyst bottlenecks on risk data

Compliance teams manually hunt for policy documents and audit evidence across six systems. Risk analysts queue for SQL reports on portfolio exposure. Relationship managers cannot self-serve client history without a data engineer. Your Snowflake already holds all of it. It just is not accessible.

Document Intelligence

User asks

What are our KYC requirements for corporate account onboarding in the UAE?

AI responds

Retrieves from 3 compliance documents and 1 regulatory update. Summarises the specific requirements, flags the 2024 CBUAE amendment, and cites each source with a direct link.

User asks

Find all audit evidence related to our Basel III Tier 1 capital calculations.

AI responds

Surfaces 12 documents across your drive, CRM notes, and PDF archive. Groups by evidence type, dates each one, and flags gaps against the audit checklist.

Outcome

Compliance teams reduce audit preparation from days to hours.

Natural Language Analytics

User asks

Which loan categories had default rates above 3% last two quarters, broken down by geography?

AI responds

Queries your loan performance tables. Returns a ranked breakdown with trend comparison. Flags that SME loans in 3 regions are approaching your internal risk threshold.

User asks

Why is our net interest margin declining this quarter?

AI responds

Runs 4 chained queries across funding cost, loan yield, and product mix tables. Identifies term deposit repricing as the primary driver. Generates a 3-paragraph written summary with the supporting numbers.

Outcome

Risk analysts self-serve in minutes instead of queuing 3 days for reports.

What is possible next

What becomes possible once the access layer exists.

Document Intelligence and Natural Language Analytics are the first layer. Teams that build correctly on Snowflake grow into higher-order capabilities quickly, because the data is now structured, accessible and governed in one place.

Proactive anomaly intelligence

Instead of waiting for someone to ask, the system monitors your Snowflake data continuously and surfaces anomalies before your team would think to look. Revenue spikes. Defect trends. Unusual customer behaviour patterns.

Phase two expansion

Revenue intelligence & forecasting

Predictive models for churn probability, expansion likelihood, and pipeline conversion. Models build directly on your CRM, product usage, and transactional data in Snowflake. Revenue teams get a forward view, not just a rearview mirror.

Phase two expansion

Automated compliance reporting

For regulated industries, the AI reads your operational data, maps it to the relevant framework (SOC 2, HIPAA, Basel III, GDPR), and generates a draft evidence pack or regulator-ready report. Compliance becomes continuous, not a quarterly sprint.

Expansion for regulated industries

Customer 360 agent

A unified customer profile assembled from CRM, support tickets, transactional history, and product usage. Any team can interrogate it in plain English. CS, sales, and product finally see the same complete picture without a data team in the loop.

Phase two expansion

Competitive signal monitor

External data (earnings calls, job postings, pricing pages, news) flows into Snowflake and gets synthesised by AI into a weekly competitive intelligence brief. The first team to act on a signal wins; this closes the lag.

Strategic expansion

Employee onboarding accelerator

New hires ask the knowledge base anything (process history, product decisions, client context, team norms). The system answers from your institutional memory. The target: onboarding measured in weeks, not quarters.

Expansion for people teams

The architecture argument

Why build on Snowflake rather than a new system?

Your data stays in Snowflake

Snowflake Cortex handles vector search and LLM inference natively. Sensitive data stays in Snowflake, behind the auth and governance you already operate. One compliance conversation, already had.

No migration. No new vendor.

No separate vector database, no new ETL pipeline, no new platform your security team has to approve. Everything runs on infrastructure you already operate and pay for.

Your governance layer keeps working

Role-based access, data masking, audit logs. The Snowflake governance you have already invested in applies automatically to everything we build on top of it. Nothing leaks across boundaries.

One platform for structured and unstructured data

Snowflake now handles vectors, documents, and structured data in a single query layer. Building separately for each data type creates fragmentation. Building on Snowflake compounds the value over time.

What you get

What is in your Snowflake account when we hand over.

Everything is handed over documented, with a runbook and the evaluation set behind it. These are the artefacts your team operates from the day we leave.

  • 01

    Ingestion connectors

    Pipelines for Confluence, Notion, Jira, Linear, PDFs, CRM notes, email and SQL sources into Snowflake, built so a new connector does not mean a rebuild.

  • 02

    A Cortex Search service

    Semantic retrieval over your document corpus with chunking, metadata and citation back to the source, scoped by the reader's Snowflake role.

  • 03

    A Cortex Analyst semantic model

    Your schema, metric definitions and business vocabulary mapped so English questions become correct SQL, with the executed query returned alongside the answer.

  • 04

    A prompt registry

    Versioned prompts and verification passes for retrieval and SQL generation, so behaviour changes are reviewed rather than improvised.

  • 05

    An admin page

    Daily Cortex token spend in dollars, per-query latency, per-source ingestion volume, and configurable spend caps and per-user quotas.

  • 06

    Repository and runbook

    The full repository plus a runbook for schema re-discovery, new tables, deprecated columns and renamed fields, so your data team extends the system without us.

How it works

How a Snowflake engagement runs, week by week.

From a one to two week discovery to production in eight to fourteen weeks, across four phases. Every phase has a deliverable your team signs off before the next begins.

  1. 01

    Discovery

    Weeks 1–2

    What happens

    We map your document sources and Snowflake schema, confirm the access model, pick the first questions to answer, and price the build.

    What you get

    Source map, scope document, access-control design, priced build plan.

  2. 02

    Document layer

    Weeks 3–6

    What happens

    Connectors land the first sources in Snowflake. Cortex Search is configured with chunking and metadata, and cited answers are tested against real questions from your team.

    What you get

    Live ingestion for the agreed sources, Cortex Search service, cited Q&A in staging.

  3. 03

    Analytics layer

    Weeks 7–10

    What happens

    The Cortex Analyst semantic model is built against your schema and vocabulary. Chained queries, verification passes and written explanations are tuned on the questions that matter most.

    What you get

    Semantic model, natural-language analytics in staging, executed-SQL transparency, confidence flags.

  4. 04

    Rollout and handover

    Weeks 11–14

    What happens

    The interface goes to the first teams, the admin page tracks spend and latency, and the runbook and repository are handed over with a support window.

    What you get

    Production rollout, admin page, runbook, repository, thirty-day post-launch support.

Snowflake Intelligence, answered.

Snowflake Intelligence is two products built on the customer's existing Snowflake environment. Document Intelligence ingests every internal knowledge source (Confluence, Notion, Jira, Linear, PDFs, CRM notes from Salesforce or HubSpot, email threads, internal SQL databases) into Snowflake and makes the content semantically searchable with cited answers. Natural Language Analytics translates plain-English questions into schema-aware SQL against the customer's Snowflake tables and returns answers with written explanations. Both layers run on Snowflake Cortex.
No. Both intelligence layers run on the Snowflake environment you already operate. There is no new vendor for your security team to approve, no separate vector database, and no parallel ETL pipeline. Your existing Snowflake governance applies automatically to everything Sarvaswa builds on top of it.
The system uses Snowflake Cortex Search for semantic retrieval over unstructured content, Snowflake Cortex Analyst for natural-language-to-SQL translation, and standard Snowflake role-based access control, dynamic data masking, and the existing audit log infrastructure for governance.
No data leaves your Snowflake account. Inference and vector search both happen inside Snowflake Cortex. The model only sees the records needed to answer the current question, scoped by the same access controls applied to the user asking. There is no shared model fleet across customers.
Confluence, Notion, Jira, Linear, PDFs, CRM notes from Salesforce and HubSpot, email threads, and structured SQL databases. The ingestion architecture is built so new connectors can be added without rebuilding the access layer or the prompt registry.
The AI is grounded in your actual Snowflake schema and uses Cortex Analyst for translation. For complex questions it chains queries, runs verification passes, and returns the SQL it executed alongside the results so analysts can validate the logic. Confidence flags surface for low-confidence joins and ambiguous date ranges.
Most engagements move from kickoff to production in 8 to 14 weeks. Sarvaswa begins with a focused 1 to 2 week discovery to map your data sources, define scope, and price the build. The result is a working system your team can extend without Sarvaswa.
The ingestion pipelines, the Cortex Search service, the Cortex Analyst semantic model, the prompt registry and a complete deployment runbook, so your data team can extend the system independently.
Yes. The system inherits Snowflake's existing role-based access control and dynamic masking. A user asking the bot a question sees only the data their Snowflake role would have returned in a direct SQL query. Sensitive columns stay masked, and audit log entries identify the user behind every query.
The system re-discovers the schema on a configurable schedule. Sarvaswa hands over a runbook for handling new tables, deprecated columns, and renamed fields, so your data team can keep the access layer current without Sarvaswa's involvement.
A lightweight admin page surfaces daily Cortex token spend in dollars, per-query latency, and per-source ingestion volume. Spend caps and per-user quotas are configurable so heavy use does not become a budget surprise.
Yes. The retrieval and SQL-generation layers work in the languages Cortex supports, and numbers are returned in the account's actual currency rather than assumed USD. Multi-region rollups produce correctly denominated outputs per query.

Running on Databricks instead? See Databricks Intelligence. Still working through data residency and access control before choosing a platform? Start with enterprise AI. Want the model itself trained on your data? Read the fine-tuning explainer, or browse all services.

Your Snowflake is already there. Let us activate it.

We start with a focused one to two week discovery: map your data sources, define scope, price the build. Most teams go from kickoff to production in eight to fourteen weeks.

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