Build
Custom integrations
The AI is rarely the hard part. Getting it to the data is.
What is in scope
- Integration design, including what is the source of truth for each field
- API, database, file and email-based integration — whatever the system actually supports
- Older systems with no real API, via the routes that do exist
- Data mapping, validation and reconciliation
- Error handling, retries and alerting on failure
- Logging that makes a failed sync diagnosable rather than mysterious
How the engagement runs
- Discovery. What each system can actually expose, which is frequently less than the vendor claims.
- Contract. Agree field mapping and the source of truth before writing code. Skipping this is why integrations rot.
- Build and test against real data, including the malformed records that exist in every production system.
- Monitor. An integration nobody is watching is an outage waiting to be discovered by a customer.
What determines the price
Driven by the worst system in the chain. A modern REST API on both ends is quick; a twenty-year-old system whose only export is a scheduled CSV is not.
When this is the wrong service
Stated plainly, because an engagement that should not have happened costs us more than it costs you.
- You are replacing one of the systems within six months. Wait.
- A one-directional export would do. Bidirectional sync is an order of magnitude more work and needs a real reason.
Bring us the process that keeps breaking
A short call is usually enough to tell you whether this is the right service.
