Map customer data across every system — without adding another silo
Salesforce, Stripe, your data warehouse, Amplitude, Zendesk, HubSpot — each holds a slice of revenue truth. Eru maps entities across all of them and delivers board-ready dashboards, revenue drift alerts, and health score feeds. No new data silo. No six-month warehouse project.
The cross-system data mapping problem
Every growing company hits the same wall. Customer data lives in multiple systems, each with its own schema, its own definitions, its own version of the truth. And every attempt to unify it creates yet another system to maintain.
In Salesforce: A customer is an Account with Opportunities and Contacts. Custom objects hold renewal data. The schema drifts every quarter as ops adds fields.
In Stripe: A customer is a Customer object with Subscriptions. The identifier is a Stripe customer ID, maybe linked to your internal ID via metadata. Revenue definitions don't match your CRM.
In your data warehouse: You've built dbt models to normalize this — but they break when schemas change upstream. Your Snowflake or BigQuery instance has its own definition of "active customer" that may not agree with Salesforce or Stripe.
In Amplitude or Mixpanel: Product usage lives in event streams with anonymous IDs that need to be resolved back to billing entities. Engagement signals that predict churn or expansion are locked in a silo your finance team can't access.
In Zendesk or Intercom: Support ticket history and sentiment data that should inform health scores — but the customer ID mapping to other systems is unreliable or missing entirely.
When someone asks "build me a revenue leakage monitoring system that integrates Salesforce, Stripe, and the warehouse" — the answer today is a six-month project, three new tools, and another data silo. Eru does it in an afternoon.
What cross-system data mapping actually requires
True data unification isn't about dumping everything into a warehouse and writing more dbt models. It's not another Reverse ETL pipeline or another Looker instance. It requires:
Entity resolution across systems
Mapping records across Salesforce, Stripe, your data warehouse, and product analytics to the same real-world customer entity. The Account in Salesforce, the Customer in Stripe, the organization_id in Snowflake, the anonymous user in Amplitude — they all need to resolve to one canonical entity. This is harder than it sounds when IDs don't match, email addresses have typos, and your dbt models haven't been updated since the last schema migration.
Semantic schema alignment
Understanding that "MRR" in Stripe and monthly_revenue in your warehouse mean the same thing — but "revenue" in Salesforce might include pipeline, not just closed-won. Eru builds a semantic layer that aligns field definitions across systems without requiring you to maintain yet another mapping table.
Temporal consistency
Knowing when each system was updated and whether the data is current. Stripe updates in real-time. Your Snowflake warehouse might refresh on a schedule. Salesforce syncs whenever ops remembers to run the workflow. Comparing them at the same timestamp compares different realities.
Continuous maintenance — without a data engineering queue
Schemas drift. Salesforce admins add custom objects. Engineers rename columns during refactors. dbt models break silently. Eru monitors for schema changes and re-maps automatically, so your finance team's self-service dashboards don't break every time the upstream data model evolves.
How Eru maps data across systems
Autonomous schema discovery
When you connect Salesforce, Stripe, your Snowflake warehouse, Amplitude, or any other source, Eru's AI agent explores the schema systematically. It samples data, analyzes column distributions, and builds a semantic model of what each table and field represents. No pre-built connectors. No manual schema mapping. No six weeks of dbt development.
Cross-system entity resolution
Eru resolves entities across systems using probabilistic matching — shared identifiers, email domain patterns, temporal correlation, and structural similarity. The Account in Salesforce, the Customer in Stripe, the org_id in your warehouse, the company in HubSpot, the user in Zendesk — all resolved to a single canonical entity with confidence scores and full audit trails.
Data normalization layer
Eru creates a normalized data layer that sits on top of your existing systems — not beside them. It doesn't copy your data into another warehouse. It doesn't create another silo. It reads from your sources and builds a unified semantic model that your team can query directly. Think of it as a managed semantic layer that understands your specific schema, not a generic BI connector.
Continuous reconciliation and drift detection
Eru doesn't just map once and walk away. It runs Truth Checks on a schedule to verify that mappings stay valid, that data flows correctly, and that systems remain consistent. When a Salesforce admin adds a custom object, when a dbt model changes upstream, when Stripe introduces a new subscription type — Eru detects the drift and re-maps automatically. Revenue drift alerts go to the right channel before the discrepancy shows up in your board deck.
What this delivers
Board-ready dashboards
NRR, GRR, churn by cohort, expansion by segment — built from reconciled cross-system data, not a spreadsheet someone updated last Thursday. Self-service for your finance team without filing a data engineering ticket.
Revenue drift alerts
When Stripe says $1.2M and Salesforce says $1.3M, Eru catches the discrepancy and tells you exactly where it came from. Daily automated reconciliation between billing, CRM, and warehouse — surfaced to the right Slack channel before it becomes a board-level surprise.
Health score feeds
Cross-system customer health scores that combine Amplitude usage data, Zendesk ticket sentiment, Stripe payment status, and Salesforce engagement — fed directly into your CRM or delivered as watchlists your CS team can act on.
Self-service for every team
Ask questions in plain language and get answers that combine data from Salesforce, Stripe, your warehouse, and product analytics. "Show me enterprise customers with declining usage and open support tickets" — no SQL, no analyst queue, no waiting.
Built for data teams — not against them
If you're a data lead or analytics engineer, you've probably built some version of this before. dbt models that normalize CRM and billing data. Looker dashboards that try to join across sources. A Metabase instance that only you know how to query. Eru isn't trying to replace your warehouse or your dbt project. It sits alongside them.
Reads from your existing infrastructure
Eru connects directly to Salesforce, Stripe, HubSpot, Amplitude, Zendesk, Snowflake, BigQuery, PostgreSQL, and any system with an API or database. Read-only access. It doesn't move your data — it builds a semantic understanding on top of what you already have.
No new data silo
The most common objection from data teams: "we don't need another tool that copies our data somewhere else." Eru doesn't. It queries your sources directly, resolves entities on-the-fly, and caches only the metadata needed for schema mapping. Your data stays where it is.
Complements your dbt and warehouse workflow
Already have dbt models in Snowflake? Eru can read from them. It treats your transformed warehouse tables as another source of truth — and can reconcile them against Stripe and Salesforce to catch when your models drift. If you don't have dbt, Eru handles the normalization for you. Either way, you don't need to choose.
Technical depth under the hood
Eru's entity resolution engine uses probabilistic matching with configurable confidence thresholds. Schema alignment is semantic, not string-based — it understands that mrr_cents and MonthlyRecurringRevenue represent the same concept with different scales. Field-level lineage tracking shows exactly which source fields contribute to each derived metric. Every mapping is versioned, auditable, and exportable.
Compared to traditional approaches
| Approach | Initial setup | Maintenance | Cross-system queries | Creates new silo? |
|---|---|---|---|---|
| Manual documentation | Weeks of interviews | Always outdated | Not possible | No (but useless) |
| Data warehouse + dbt | Months of modeling | Engineering time | SQL required | Yes — a big one |
| Looker / Metabase | Warehouse required first | Dashboard rot | Pre-built views only | Depends on warehouse |
| Reverse ETL tools | Per-connector setup | Sync monitoring | Limited to synced data | Yes — per destination |
| Eru | Connect and explore | Autonomous | Natural language | No |
Supported systems
Eru connects to any system with an API or database. Here are the most common for cross-system data mapping:
- CRM: Salesforce, HubSpot
- Billing: Stripe, Chargebee
- Product analytics: Amplitude, Mixpanel, Segment
- Support: Zendesk, Intercom
- Data warehouses: Snowflake, BigQuery, Redshift, PostgreSQL
- Any database: If it has a connection string, Eru can read it
Related
Unified revenue insights — no new silo
Eru maps your customer data across Salesforce, Stripe, your warehouse, and every other system. Board-ready dashboards, revenue drift alerts, and health score feeds — without adding another data silo to manage.