AI Integration Services

Cost tracks with how many systems are involved, how accessible they are, and the integration pattern. A single secure connection to one well-documented system typically runs $15,000–$35,000; a multi-system integration with data pipelines, legacy wrappers, and full security hardening runs $40,000–$120,000+. We usually start with a fixed-price PoC (typically $12,000–$20,000) that connects one live system end-to-end before you commit to the full build, and every engagement opens with a free 30-minute discovery call.

A single secure integration typically takes 4–8 weeks; a production rollout across multiple systems with pipelines and security hardening runs 10–14 weeks; a scoped PoC connecting one live system lands in about 6 weeks. The biggest variable is accessibility — well-documented APIs are quick, while undocumented legacy systems add time for wrapper development, which we scope honestly upfront rather than discover mid-project.

Yes — it's one of our specialities, and no, you won't have to migrate anything. For a system with no usable API, we build a structured wrapper or an MCP server that exposes exactly the capabilities AI needs while the underlying system stays untouched and running. Integrations are additive: AI connects alongside your systems of record through their own interfaces, so your CRM, ERP, and document stores stay exactly as they are. If a vendor tells you to rip-and-replace first, get a second opinion.

Security is engineered in from day one, which is exactly why our integrations tend to clear review on the first pass. We use identity-first access (SSO/OAuth with short-lived tokens), least-privilege scopes per connection, no persistent credentials in the runtime, encryption in transit, data-residency boundaries, and a complete audit trail of every call. We also produce the documentation your security team needs, so the review becomes a confirmation of controls already in place rather than an investigation. This satisfies most enterprise security questionnaires and aligns with ISO 27001 controls.

Both — with a deliberate boundary. Read access is straightforward; write access (creating a record, updating a field, triggering an action) is scoped tightly, gated where the action is high-consequence, and fully logged, with validation and rollback paths so an AI mistake can't corrupt a system of record. High-stakes writes can require human confirmation before they execute, and where the risk isn't worth it we keep the AI read-only and route changes through a person.

Yes — and it's the single biggest driver of adoption. We surface AI inside Outlook, Microsoft Teams, Slack, SharePoint, and your CRM, so assistance shows up where people already work and there's nothing new to learn or open. The same backend powers it whether it's embedded in a host tool, built into your own product, or run standalone — only the surface changes, so one investment reaches everyone.

It comes down to scale. For a single, simple connection, point-to-point is the pragmatic choice and a hub would be over-engineering. The moment you have several systems and multiple AI use cases, a hub — a thin integration layer — is far better: each system is connected once and every use case draws on it, so adding a model or changing a system is a contained change instead of a web of rewrites. We recommend the architecture that fits where you'll be in two years, not just where you are today.

We build for failure, not just the happy path: retries with backoff, fallbacks, queuing, and graceful degradation, so one system being down doesn't take your whole AI offline. Integrations sit behind stable internal interfaces with versioning and automated contract tests, so when a vendor changes an API the test catches it before your users do and the fix is contained to one adapter. For data that has to be current we use event-driven syncs (webhooks, queues, streaming); for slower-moving data, scheduled batch — matched to need so you never serve silently stale answers.

Yes — we frequently integrate AI that another team or vendor built, or an off-the-shelf AI product, into a client's systems. Integration is a distinct layer; it doesn't matter who built the model. We connect it to your data and tools securely and reliably, and we'll tell you honestly where the AI itself needs hardening to be production-ready before it's wired into live systems.

Yes — and it matters here, because integrations break when the systems around them change. Every engagement includes 90 days of post-launch support (monitoring, incident response, adjustments). Beyond that, retainers cover uptime monitoring, API-change maintenance as vendors update their systems, new connections, and performance tuning — so your integrations keep working as your stack evolves rather than quietly failing one weekend.

Completely. All connectors, pipelines, wrappers, and documentation are yours, unconditionally. We sign NDAs before any discovery conversation, retain no client data after a project concludes, and hand over the runbook so your team can operate and extend the integrations without us. For stricter requirements we work entirely within your cloud environment so we never hold your production data at all.