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Beta · early access open

AI agents that turn data into decisions.

DataCraves connects to your warehouse, watches every metric that matters, and ships insights, forecasts, and weekly reports straight to your team — so people stop staring at dashboards and start acting on the signal.

✓ Connects to Snowflake, BigQuery, Postgres
app.datacraves.com / overview
Live
MRR
$487K
▲ 6.2%
NRR
118%
▲ 2 pp
Churn
2.1%
▼ 1.0 pp
agent analysing · 23s ago
Insight · 30-day MRR forecast tracking 6.2% above plan, driven by Pro-tier expansion in the SMB segment.

How DataCraves agents work.

Three steps. No engineering team required.

1

Connect your data

OAuth into your warehouse, BI tool, or product analytics. Read-only by default. Schema is auto-mapped in under five minutes.

2

Pick your agents

Five purpose-built agents — Insights, Forecast, Cohort, Reporter, Pipeline. Mix and match per team.

3

Get answers, not dashboards

Agents push proactive briefings to Slack, email, or your tool of choice. No more dashboard fatigue.

Built for the questions your team asks every Monday.

Five battle-tested use cases shipped on day one.

All use cases →
SaaS

Reduce churn by detecting at-risk cohorts in real time

By the time a customer cancels, the warning signs have been in your data for weeks — declining logins, unanswered support tickets, billing disputes. Most teams catch them too late because no human has time to watch every account every day.

📈 30–45% reduction in voluntary churn
E-commerce

Forecast inventory demand 30 days out — by SKU, by region

Stockouts kill conversion; over-ordering kills margin. Most demand forecasts are spreadsheets updated weekly by one analyst, blind to weather, promo cadence, and competitor pricing.

📈 Stockouts down ~25%, working capital tied up in inventory down ~15%
Fintech

Detect transaction anomalies before they become fraud cases

Rule-based fraud systems miss novel attack patterns and drown your ops team in false positives. Tuning them is a full-time job and the fraudsters always move first.

📈 1.8–2.5x lift in fraud catch rate at the same false-positive budget
Marketing

Auto-generated weekly campaign performance reports

Your marketing team spends 6+ hours every Monday assembling the weekly performance deck. By the time it's ready, half the insights are stale and nobody on the exec team reads past slide three.

📈 ~6 hours/week saved per marketing analyst
Operations

Pipeline health monitoring with auto-rerun and root-cause hints

Your data pipelines fail at 3am. Your on-call engineer wakes up, spends 40 minutes finding which DAG broke, another 30 figuring out why, and a final 20 reassuring downstream stakeholders. Repeat 3x a week.

📈 ~70% reduction in manual triage time on data incidents

Five agents. One pane of glass.

Each agent specialises in one job and does it well. Compose them like Lego.

Insights Agent

Auto-finds anomalies and opportunities in your data

Forecast Agent

Predicts your key metrics 7, 30, and 90 days out

Cohort Agent

Segments users into actionable behavioral cohorts

Reporter Agent

Writes weekly summaries to Slack, email, or Notion

Pipeline Agent

Monitors data quality and auto-reruns failed jobs

Start in five minutes. Scale when you're ready.

Starter at ₹999/mo · Pro at ₹4,999/mo · Enterprise on contact. No annual lock-in.

Trusted patterns from real deployments.

Aggregate outcomes from pilot deployments. Individual clients remain confidential under NDA — every visualization on this site uses synthetic data.

35%
reduction in voluntary churn

Across SaaS pilots after the Cohort + Insights agents reach steady-state (typically week 8).

27%
fewer stockout events

Mid-market e-commerce inventory deployments using the hybrid Prophet + XGBoost forecaster.

78%
reduction in on-call pages

Data-platform teams using the Pipeline agent's auto-retry classifier vs raw alerting.

📊 All numbers reflect aggregate pilot outcomes; individual deployments vary. Charts and dashboards across this site use synthetic data to preserve client confidentiality.