AI integration services, wired to your real systems of record.

AI CRM integration services connect a model to the records your sales and service teams live in, so the AI works on live deals and contacts instead of an export. Typical jobs: score and route inbound leads, enrich contacts and companies, summarise calls and email threads onto the record, draft follow-ups for a person to send, and keep pipeline fields clean. In production for OmniTrade Brasil, an agent reads 1,200 CSV files a month and writes only the qualified leads into HubSpot; for Grupo Filtrarte, lead routing runs across HubSpot, Outlook and WhatsApp. Whatever the CRM — HubSpot, Salesforce or Dynamics 365 — the rules are the same: the AI writes through the API with its own scoped user, every change is logged, and anything ambiguous lands in a queue for a person.

• OmniTrade case study
• Filtrarte case study

Three integrations running in production. Seed Real Estate: bank statements reconciled against InvoiceXpress invoices, OpenAI models categorising each transaction and Airtable as the system of record, in an autonomous 6-hour cycle. OmniTrade Brasil: construction-project CSVs scored against configurable rules and pushed into HubSpot, 1,200 files a month, with screening down from days to hours. And on the data integration side, a UK agritech operation: a field-sensor network feeding an edge processing layer and an agronomist dashboard, which cut fertiliser cost by 23% in the first season. The pattern is the same every time — one authoritative system per field, a single path proven end to end, then wider scope.

• Seed Real Estate case study
• Agritech case study
• AI automation services

Enterprise system integration means connecting systems that were never designed to talk to each other, and there are three patterns we use, in this order of preference.

1. 1First, direct API integration: if the system has a usable API, this is the cheapest and by far the most reliable option, and it is worth arguing for even when it means waiting on a vendor.
2. 2Second, a read replica or an event and queue layer with a staging table: use this when the API exists but is rate-limited, slow or unreliable, so your AI reads from something you control and the source system is written to on your own schedule.
3. 3Third, and only as a last resort, driving the user interface the way a person would, which is what classic RPA does: it works on systems with no API at all, and it breaks whenever a screen changes, so we treat it as a stopgap with a documented replacement plan rather than an architecture. Most system estates end up as a mix, chosen per system rather than as a single company-wide decision.

Four checkpoints, and we will not ship without them. Permissions: record-level access from the source system is carried through retrieval, so authorisation is enforced where the data lives rather than re-implemented in the AI layer. Data boundaries: before anything connects, we establish what counts as personal or regulated data under GDPR, where it is allowed to be processed, and what must never leave your infrastructure — for EU companies that usually decides the hosting question outright. Write safety: every write-back has validation, a dry-run mode, an audit trail naming the actor and the reason, and a rollback path, so a bad model output cannot quietly corrupt a system of record. Observability: logs, alerting on error-queue depth and schema drift, and a written runbook, so the team can tell the difference between the integration being down and the source system being down. If you have an existing security review or a DPO, these are the artefacts they will ask for, and producing them at the end costs several times what producing them by design costs.

We stage it so the risky assumptions fail early and cheaply. Phase one is read-only against a single system in staging, which proves the auth model, the latency and the real token cost on your data before anyone has written a line of production write-back. Phase two adds write-back for one flow in dry-run, where the integration produces exactly the payload it would have sent and a person reviews the diff. Phase three runs it in production on a narrow slice — one region, one team, one document type — with the old manual path still live, so you can compare outputs rather than trust them. Phase four widens scope and adds the remaining systems, hardening retries and rate-limit handling against real volumes. Phase five is the handover. Teams that skip straight to a broad production cutover almost always discover the permissions edge case and the schema drift at the same time, on real customer data, which is the one place you cannot afford to find them.