AI integration services, wired to your real systems of record.
Our AI integration services are the plumbing that makes AI real: the model works in a demo but can't see your data, write back to your CRM, or be trusted with your permissions model — so we connect it to your live systems, safely. No rip-and-replace.
Everything you need.Wired into reality.
Not a demo against a stale export. AI connected to your live systems of record, with read/write you can trust, a permissions model that holds, and a runbook your team owns.
AI that reads your live systems of record
Current CRM, ERP, database and warehouse data — not a CSV export or a copy that's a week stale.
Write-back you can trust
When AI updates a record or triggers a workflow, it ships with validation, a dry-run mode, an audit trail and rollback — so a bad output can't silently corrupt your system of record.
A permissions model that holds at the AI layer
Record-level access carried through retrieval, so no user — and no model — ever sees data they shouldn't.
Pipelines that survive the real world
Rate limits, retries, idempotency, schema-change detection and error queues with replay. It doesn't quietly break on a Tuesday.
No rip-and-replace
It runs on the stack you already have — on-prem or cloud, alongside your existing tools. We integrate into reality, we don't re-platform it.
A system your team owns
An architecture doc, monitoring, alerting and a written runbook — so you can operate it without us when you're ready.
Map it. Connect one path.Then to production, fast.
Five steps from a read-only assessment to a staged cutover — your data proves the auth, latency and cost before we widen scope.
30–60 minutes with an engineer, then read-only access to the systems in scope. We map the data, the systems of record, the auth model, rate limits and the compliance constraints — and tell you what's actually integrable, what's blocked, and where the real risk is. If integration isn't your bottleneck, we say so here.
Written architecture: which systems connect, the direction of data flow, the retrieval design, the permissions model and the failure modes. Fixed milestones and a success metric, in writing, before any code.
We wire a single real flow through the whole stack — one system, one data path, into the AI layer and back — in staging. This proves the auth, permissions, latency and cost on your actual data before we widen scope.
Add the remaining systems and flows, add retries, idempotency and rate-limit handling, lock down permissions and audit logging, load-test against real volumes and dry-run the write-backs. Weekly demos on your data throughout.
Production cutover, usually staged or dual-run, with monitoring and alerting live and the runbook written. We stay accountable through stabilization; ongoing operation can roll into Support & Scale or back to your team.
We're not for everyone.We’re for teams wiring AI into the real stack.
If any of these sound familiar, we should talk.
The demo works, production doesn't
Founder or CTO with an AI feature stuck in isolation
- Impressive in isolation, useless in production
- Reads a stale export, can't write back
- Not tied into your auth
Outcome: The demo becomes a feature your systems actually run.
AI can't touch your data because of where it lives
Data spread across ERP, legacy on-prem and SaaS
- Data scattered across ERP, legacy DB and SaaS tools
- A real PII or data-residency regime
- No way to rip-and-replace
Outcome: AI reaches your data without breaking your compliance constraints.
You need retrieval grounded in your own systems
Internal AI that answers from your data and acts in your tools
- Support, search or an agent that updates records
- Must never retrieve a record a user shouldn't see
- The hard part is retrieval, permissions and write-back
Outcome: Grounded answers and safe actions inside your own tools.
Senior engineers. No handovers. No fluff.
What do AI integration services include?
AI integration services cover five things, and a real quote reflects all of them: mapping your systems of record and deciding which one is authoritative for each field; the retrieval design, including how record-level permissions travel with the data so the model can never surface something the user has no right to open in the source system; the write-back path, with validation, a dry-run mode, an audit trail and rollback; the pipeline that keeps it alive — rate limits, retries, idempotency, schema-change detection, error queues you can replay; and the handover, meaning an architecture doc, monitoring, alerting and a runbook your team can operate without us. The part teams underestimate is the second and third: reading data is easy, and reading it with the permissions model intact while writing back safely is where the engineering actually goes.
How much do AI integration services cost, and how long do they take?
Project-based with fixed milestones agreed in writing after a 30–60 minute technical assessment — not a day rate, and not a per-seat fee. A single read-only path into one clean system is a few weeks. Write-back across several systems, with a real permissions and audit regime, is a larger engagement and we scope it milestone by milestone before you commit. The biggest cost variable is not the AI, it is the age and quality of the systems: a documented REST API with a sandbox is cheap to integrate, and a legacy ERP with no sandbox, no test data and an undocumented API is the line item that moves the number. The assessment exists to tell you which one you have before you sign anything.
How do you integrate AI with an ERP or CRM that has a bad API?
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. First, 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. Second, 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. Third, 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.
What governance does enterprise system integration need?
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, 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.
What do the rollout phases look like for a complex workflow?
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.
Start your deployment.
Talk directly to a principal engineer.
No sales team.
No discovery workshops.
No procurement circus.
We scope, build and ship.
- Reply within 24h
- Engineer-led assessment
- Written proposal
- Portugal / EU timezone
No commitment. Just an engineer.