An AI agent that maps your data. Automatically.
Eru connects to your databases and APIs, discovers how your data relates, and monitors it continuously. No manual mapping. No stale documentation. Just a living source of truth.
The problem with business data
Your company runs on data spread across dozens of systems. CRM holds customer records. Billing tracks subscriptions. Support logs conversations. Product analytics measures usage. Finance reconciles revenue.
Each system has its own definition of "customer." Its own version of "revenue." Its own understanding of what happened and when.
When the numbers don't match—and they never do—someone spends hours tracing discrepancies through spreadsheets, SQL queries, and Slack threads. By the time you find the answer, the data has already changed.
What Eru does differently
Eru doesn't just connect your data. It understands it.
When you connect a source, Eru's AI agent explores the schema, samples the data, and maps entities to your business concepts. It figures out that workspaces in your database means the same thing as accounts in HubSpot means the same thing as customers in Stripe.
Then it keeps watching. When schemas drift, when data stops flowing, when numbers stop matching—Eru catches it before you do.
Core capabilities
Truth Map
A living knowledge graph of your business entities. Eru discovers tables, endpoints, and business concepts across all your systems, then maps the relationships between them. Every mapping includes a confidence score and full audit trail. When you need to know "what is a customer, actually?"—there's one canonical answer.
Truth Checks
Continuous reconciliation between systems. Eru runs checks on a schedule to verify that your data stays consistent: Stripe revenue matches database revenue. Event counts stay within expected ranges. Foreign keys remain valid. When something breaks, you find out in Slack—not in a board meeting.
Evidence Packs
Every answer Eru gives comes with proof. Not just "revenue dropped 15%" but the exact queries that produced that number, the schema versions in effect, the data samples examined. You can reproduce any insight. You can trust the math.
Watchlists
Dynamic lists of entities that matter. At-risk accounts showing churn signals. High-usage customers on starter plans. Deals stuck in pipeline. Eru analyzes data across multiple sources, scores each entity, and alerts you when the list changes significantly.
Natural Language Q&A
Ask questions in Slack. "Why did activation drop yesterday?" Eru plans the analysis, executes queries across relevant systems, correlates the results, and responds with an evidence-backed answer. No SQL required—but you can see every query it ran.
Board-Ready Revenue Metrics for Series A–C SaaS
Every board meeting starts the same way: someone scrambles to reconcile ARR across Stripe, Salesforce, and a spreadsheet. Eru eliminates that scramble. It aggregates ARR, NRR, gross revenue retention, cohort expansion data, and billing reconciliation into board-deck-ready reports—pulled directly from your connected CRM, billing, and product analytics systems.
Eru calculates board reporting revenue metrics from live, reconciled data—not quarterly snapshots assembled by hand. NRR calculations for due diligence are produced automatically with full audit trails, so your numbers hold up when investors start asking questions. Customer cohort analysis for VCs shows retention trends by acquisition quarter, segment, and plan tier—all generated from cross-system signals without manual spreadsheet work.
Audit-ready revenue data for due diligence
When investors open your data room, they want to see reconciled revenue records from multiple systems—not conflicting exports from five different tools. Eru produces audit-ready revenue data trails that reduce time to close data room requests by consolidating multi-system revenue records into a single source of truth. Every metric includes the underlying queries, data sources, and reconciliation logic so your finance team can defend the numbers under scrutiny.
Gross revenue retention metrics are calculated from reconciled billing data alongside NRR, giving investors the full picture of your retention dynamics—not just the expansion-masked headline number. Eru tracks how each cohort performs over time, so you can show whether your product is getting stickier or whether you’re running on an expansion treadmill.
Built for CFOs and founders
Stop reconciling your cap table metrics manually before every board meeting. Eru connects to Stripe, Salesforce, HubSpot, Chargebee, and your product database to produce a single, reconciled view of ARR, NRR, GRR, and cohort performance. The numbers update continuously—not the night before a board deck is due.
Give your investors one dashboard, not five exports. Instead of emailing spreadsheets with caveats, share a live view of your revenue metrics that traces every number back to source systems. Eru’s evidence-backed reporting means your CFO presents with confidence and your founders spend less time on data wrangling and more time on strategy.
What Eru surfaces for board reporting
- ARR and MRR — reconciled across billing and CRM, with variance explanations for every discrepancy.
- Net revenue retention (NRR) — calculated from reconciled billing data with confidence intervals and driver attribution, ready for board reporting revenue metrics decks.
- Gross revenue retention (GRR) — gross revenue retention metrics that show true retention without the mask of expansion revenue.
- Customer cohort analysis for VCs — retention and expansion trends by acquisition cohort, segment, and tenure—the exact view investors request during due diligence.
- Expansion revenue attribution — which accounts are expanding, by how much, and what signals predicted it.
- Billing reconciliation — continuous Stripe-to-CRM reconciliation that catches discrepancies before they compound into board-level problems.
How it works
1. Connect your sources
Add databases (Postgres, MySQL, Snowflake, BigQuery, Redshift) and APIs (REST, GraphQL, OAuth). Credentials are stored encrypted and never sent to the AI. Eru tests connectivity before it starts exploring.
2. Eru explores autonomously
The agent scans schemas, samples data, and infers relationships. It asks clarifying questions when needed—but only good questions, and only a few per week. It learns your specific data model, not a generic template.
3. Review and approve mappings
Eru proposes mappings with confidence scores and impact analysis. You approve the ones that matter. Every change is versioned with a full audit trail. This isn't a black box—it's your data model, documented and maintained automatically.
4. Monitor continuously
Once mapped, Eru watches for drift. Schema changes, missing data, reconciliation failures. Problems surface in Slack before they cascade into broken dashboards and confused stakeholders.
What Eru connects to
Eru works with any database that speaks SQL and any API that returns JSON. No pre-built connectors required—the agent learns your specific implementation.
Safe by design
Eru is read-only. It never writes to your systems, never modifies data, never executes mutations. Every operation is audited. Query execution is sandboxed with rate limits and cost controls.
Credentials live in encrypted storage, separate from the AI context. The agent sees schema metadata and statistical summaries—not raw PII dumps. And when Eru samples data, it redacts sensitive fields automatically.
Ready to see your data clearly?
Early access is open for teams with 5+ data sources.