06Service
Data & Integrations
Integrations that keep working on the day the other system is having a bad one.
- Service
- 06 / 06
- Outcome
- Making systems that ignore each other talk
- Core stack
- Node.js · Python · PostgreSQL
Connecting two systems is easy for about a week. Then an API rate-limits, a webhook is delivered twice, a field arrives null that never used to, and the sync quietly stops without anyone noticing until the numbers look wrong.
We build integrations that expect this. Explicit schemas at every boundary, idempotent writes so a duplicate delivery is harmless, retries with backoff, dead-letter handling for what cannot be processed, and alerting when a pipeline stalls — because silent failure is the expensive kind.
The result is one place where the business is true, and reporting built on the same records rather than on a monthly export that someone has to remember to run.
Signs you need this
- The same customer exists three times with three different spellings
- Reporting means exporting from four tools and joining them by hand
- A sync broke weeks ago and nobody noticed until the numbers were wrong
- A vendor has an API and nobody has had time to use it
- Two departments quote different figures for the same month
- Problem
- The CRM, the billing tool, the spreadsheet and the messaging app each hold part of the truth, and no one can see the whole picture.
- Solution
- APIs, integrations and data pipelines that move information between systems reliably, with schemas, retries and failure handling designed in.
- Outcome
- One reliable view of the business, and reporting that doesn't depend on someone exporting a CSV every Monday.
/What the work includes
How the work actually runs.
Scope is agreed in writing before the build starts, and every stage produces something you can inspect.
- 01
Systems audit
What holds which data today, which system is authoritative for each field, and where the duplicates come from.
- 02
Schema and contracts
Explicit shapes at every boundary, versioned, so a change on their side surfaces as an error rather than as bad data.
- 03
Pipelines
Sync jobs and webhook handlers with idempotency, retries, backoff and dead-letter queues for anything unprocessable.
- 04
Observability
Monitoring and alerting on every pipeline, because an integration that fails silently is worse than one that never existed.
- 05
Reporting layer
Dashboards built on the consolidated data, so the numbers come from the system rather than from a spreadsheet.
Typical stack
- Node.js
- Python
- PostgreSQL
- Redis
- REST
- Webhooks
- Docker
What you receive.
- REST and webhook APIs
- Third-party integrations
- Data pipelines and sync jobs
- Schema and database design
- Reporting layers
Straight answers.
- What if a tool has no API?
- There is usually a path — an export, a database connection, a partner endpoint. Where there genuinely is not one, we will tell you plainly rather than building something fragile that breaks on their next update.
- How do you handle the systems disagreeing?
- By deciding in advance which system is authoritative for each field, and making that rule explicit in the pipeline. Most data conflicts are unmade decisions rather than technical problems.
- Will this break when a vendor changes their API?
- Sometimes — that is outside anyone's control. What we control is that it fails loudly and immediately, with an alert and a clear error, rather than quietly writing wrong data for a month.
Other services
Need Data & Integrations? Tell us the problem.
The first conversation is about your operation, not our stack. If a smaller fix would do the job, we will say so before quoting a larger one.