Your customer data lives in 4 different systems and none of them agree.
When customer data is scattered across a CRM, a billing system, a support platform, and a data warehouse with no single authoritative source, every cross-functional report is wrong and every customer conversation requires pulling context from multiple places. We build the integration infrastructure that unifies your data and makes a single source of truth possible.
Your sales team uses Salesforce, your billing team uses QuickBooks, your support team uses Intercom, and your product team uses a PostgreSQL database. Each system has different customer records, different account structures, and different histories. Nobody fully trusts any of them.
Data fragmentation is a predictable consequence of a business that has grown by adding tools rather than building systems. At 5 employees, Salesforce for sales tracking and QuickBooks for accounting are independent and manageable. At 50 employees with Salesforce, QuickBooks, Intercom, a product database, a Stripe billing system, and a data warehouse, the fragmentation has compounded into a serious operational problem.
The symptoms are universal: "The numbers in the board report don't match the numbers in Salesforce." "Our support team doesn't know which customers are on the enterprise plan when they're handling a ticket." "We can't calculate the true LTV of a customer because their revenue data is in Stripe, their contract data is in Salesforce, and their usage data is in the product database."
These are not reporting problems — they're data architecture problems. The business has multiple systems of record for the same entity (the customer) and no reconciliation mechanism between them. When Salesforce calls a customer "Acme Corp" and QuickBooks calls them "Acme Corporation, Inc." they're treated as different entities. When a customer closes a deal in Salesforce and the Stripe subscription is created manually, there's no link between the CRM opportunity and the billing record.
The solution is not always replacing all the systems — it's building the integration layer that reconciles them.
A unified customer data infrastructure — either a data integration layer or an API gateway — that provides a consistent view of customer data across all systems, eliminates conflicting records, and supports the business reporting that's currently impossible.
Data model audit
We map the data entities that exist across all systems — the customer record, the account record, the contract record, the invoice record — and identify the canonical version of each entity and the divergence points between systems.
Master record system
A single authoritative record for each customer (or account) that aggregates the identifiers from all other systems. The master record is the lookup key: given a customer, resolve their Salesforce account ID, their Stripe customer ID, their Intercom contact ID, and their internal database user ID.
Bi-directional sync for critical data flows
The data fields that need to be consistent across systems are kept in sync with conflict resolution logic: which system is authoritative for each field, and how conflicts are resolved when two systems diverge.
Unified customer API
An internal API endpoint that returns the complete customer record — aggregated from all source systems — in a single call. Customer-facing team members (support, account management) get the full customer context without switching between systems.
Operational dashboards and reporting
Cross-system reports that were previously impossible (LTV by acquisition channel, support ticket volume by plan tier, churn prediction by usage pattern) built on the unified data layer. Built on Next.js API routes or Node.js workers, Postgres, TypeScript.
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
A unified customer data infrastructure — either a data integration layer or an API gateway — that provides a consistent view of customer data across all systems, eliminates conflicting records, and supports the business reporting that's currently impossible.
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
Data integration projects are scoped to specific systems and specific data entities. The scope is defined by the number of systems, the data entities to unify, and the specific reports to enable. Fixed scope, fixed price.
Related engagements.
Your business runs on API integrations that fail silently and break at the worst possible time.
Read more02Every hour your team spends copying data between systems is an hour they're not doing the job they were hired for.
Read more03You've tried every SaaS tool in the category. None of them fits how your business actually works.
Read moreQuestions, answered.
A data warehouse is the right choice for analytical workloads (business intelligence, historical analysis, cross-functional reporting). A custom integration layer is the right choice for operational workloads (customer support context, real-time billing checks, product access control). Most businesses of meaningful scale need both — the operational integration layer and the analytical data warehouse.
Historical data reconciliation is a project in itself. The first step is establishing the master record system for new data going forward. Historical reconciliation — matching records across systems that were never linked — requires data cleaning and matching logic that's scoped separately based on the volume and quality of historical data.
Most modern SaaS platforms (Salesforce, HubSpot, Intercom, Stripe) have robust APIs. Legacy systems and some niche business tools have limited or poorly documented APIs. In those cases, the integration architecture may rely on CSV exports, database-level integrations, or file-based sync rather than real-time API calls.
A customer data unification project across 3–5 source systems with a master record API and operational dashboards typically runs $25k–$55k. Historical data reconciliation, if required, is scoped separately. Fixed-price.
8 to 14 weeks for a production data integration layer from audit to live operation.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.