Runs in your infrastructure  ·  Your Domain Model  ·  Your LLM

Governed analytics, from ingestion to AI, inside your own warehouse.

Connect your sources, define every metric once, then let analysts and an AI analyst work from the same governed numbers. No migration, no copies, no new silo.

Book a demo
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.

Five steps, one Domain Model. Every step runs on your warehouse and inherits the same definitions, so the number in the dashboard is the number the agent reasons over.

01

Connect

Bring every source into your warehouse.

02

Model

Define every metric, join and rule once.

03

Analyze

Dashboards and self-service on trusted numbers.

04

Ask

An AI analyst on the same definitions, with the SQL shown.

05

Act

Alerts, scheduled deliveries and embedded analytics.

// 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 the number can be trusted

Every answer can be checked.

The first question every team asks about AI analytics is how they would know if it were wrong. Sprinkle answers it four ways, on every answer.

  • 01
    Your definitions, enforced.
    Metrics come from the Domain Model. If the agent writes its own aggregate over a governed metric, the query is rejected before it runs — so the answer uses the same formula as the dashboard.
  • 02
    Arithmetic in your warehouse, not in the model.
    Every sum, count and ranking runs as SQL against your data. The agent reads the result; it does not do the maths from a preview of rows.
  • 03
    SQL, steps and raw data on every answer.
    Open the query, see which tables and formulas it used, and download the rows behind the chart. Verify rather than trust.
  • 04
    Scored against known answers.
    Answer quality is tested against reference questions whose answers are computed by SQL, so a change of prompt or model shows up as a score, not a surprise.

And when the data does not support an answer, the agent says so instead of filling the gap.

// 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 on your own warehouse: connect, model, dashboards, and the Analyst Agent answering from the same governed numbers — plus the architecture with your security team, if you'd like.

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