Runs in your infrastructure  ·  Your Domain Model  ·  Your LLM

An AI analyst that knows your business.

It runs on your Domain Model — every metric, entity, join and access rule, defined once — inside your own infrastructure, on the LLM you approve. Nothing leaves your boundary.

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4.8★ on Capterra No migration. No copies. No new silo.
// trusted by data teams at some of India's fastest-growing companies
// warehouse-native

Your data stays where it belongs.

Sprinkle runs on the warehouse you already have. There is no forced migration, no second copy of your data in a vendor's cloud, and no new silo to govern. Access rules are defined once in Sprinkle and enforced on every dashboard, export, embed and agent answer.

  • No migration
  • No second copy
  • No new silo
Snowflake
BigQuery
Redshift
Databricks
PostgreSQL
All warehouses →
// the platform

From data to decision.
One governed platform.

Connect → Model → Analyze → Ask → Act. Every step runs on your warehouse and inherits the same Domain Model, so the number in the dashboard is the number the agent reasons over.

01 · connect

Bring every source into your warehouse.

02 · model

Build your Domain Model once. Every metric, join and rule — governed and tested.

select region, sum(amount)
from orders_fact
group by region;
03 · analyze

Dashboards and self-service on trusted numbers.

04 · ask

An analyst that investigates the “why” — not just answers.

Ask in plain English. The agent works off your Domain Model, respects the same row-level rules as every dashboard, and shows the SQL behind every answer.

user
Why did APAC revenue drop?
sprinkle agent
Investigating orders_fact, payment_events…
sprinkle agent
Root cause: gateway timeout. Draft RCA ready.
05 · act

Turn answers into alerts, deliveries and decisions.

Schedule delivery to Slack, email and Sheets. Trigger alerts on thresholds. Ship the same analytics inside your own product.

alert · GNPA > 2.1% · #risk-ops
delivered · weekly_collections.xlsx · 06:00
embed · /v1/dashboard · tenant=acme
// in the product

The agent works alongside you.

A workspace, not a chatbot. Browse dashboards, write SQL, model metrics — the agent picks up context as you go.

Revenue.dash cohorts.sql + new

Revenue — APAC drill-down

workspace / revenue / apac
REVENUE · APAC
$284,120
↓ 4.1% wow
ORDERS
12,481
↑ 2.0% wow
REVENUE TREND · LAST 30 DAYS
// why it works

The LLM is replaceable. Your Domain Model isn't.

An LLM with a database connection is a demo, not a deployment. Four things go missing without a Domain Model between the model and your data:

// consistency
Same question, same answer.
One definition for everyone — not one SQL query per ask. The number in the dashboard is the number the agent reasons over.
// scope
A branch head sees their branch.
Row-level rules apply to the AI exactly as they do to dashboards, exports and embedded views.
// audit
Every answer shows its SQL.
Logged and verifiable by risk and finance — not taken on trust.
// portability
Swap the LLM, or the warehouse.
The definitions, joins and rules stay. They're yours, not a vendor's.

The LLM and the warehouse can be swapped. The Domain Model is what makes it safe to give the AI to everyone.

See the full argument in the architecture

// industries

Built for data-intensive businesses.

The same governed platform, with the metrics, questions and proof that matter in your sector.

Lending & Fintech

Collections · Portfolio · Disbursals · Risk · Branch
Aavas · mPokket · Bright Money
Explore →

Ecommerce & Consumer Internet

Growth · Marketing · Retention · Unit economics
Swiggy · Tata CLiQ · Ace Turtle
Explore →

Logistics & Mobility

Fleet · SLAs · City ops · Cost per order
Yulu · Swiggy
Explore →

SaaS & Technology

ARR · Churn · Product usage · Pipeline · Embed
LeadSquared · Netenrich · Atomicwork
Explore →
// customer

“I'd recommend Sprinkle to every analyst I know — it's genuinely self-serve. Where RCA volume or repeated data requests are high, Sprinkle's no-code analytics comes in very handy.”

Ishu Jain
Director, Analytics
Swiggy
— 40% reduction in ad-hoc query backlog after 90 days
// why teams choose sprinkle

Outcomes, not row counts.

Measured by customers on their own warehouses — not on a vendor benchmark.

40%
// fewer ad-hoc data requests in 90 days — Swiggy
1–2 days
// from model to dashboard, down from weeks — Yulu
4×
// faster to build a pipeline than in PySpark — Yulu
4.8/5
// rating on Capterra
// built for control

Your data. Your rules. Your model.

Sprinkle runs inside your infrastructure. The four questions a security review asks, answered by the architecture rather than a certificate.

// where the data lives
In your warehouse. Nothing leaves.
Deployed in your cloud account or your own data centre and operated by your team. No extracts, no copies, no vendor-side data store.
// who sees what
Granular rules, defined in Sprinkle.
Roles and row-level rules by branch, region, product or tenant — set by your data team, no warehouse changes. Applied to every dashboard, export, embed and agent answer. Every query audited.
// whether to trust the answer
One Domain Model. Full lineage.
Every answer comes from your Domain Model — the AI's map of your business: every metric defined once, every entity and join, every access rule — traced back to source, with the SQL behind every AI answer visible, so risk and audit teams verify rather than trust.
// which model does the thinking
Bring your own LLM.
Connect the model your organisation has approved, in your cloud account or self-hosted. Prompts, schema and results stay inside your boundary.
RBACSSO / SAMLRow-level securityAudit trailMetric lineageSQL visibilityYour LLM

See the architecture

// get started

See it run in
your environment.

A 30-minute walkthrough of the Analyst Agent on your own warehouse — and of the architecture with your security team, if you'd like.

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GDPR · compliance built-in
SSO & RBAC · governance from day one
Runs in your infrastructure · nothing leaves your boundary