Connect AI to the Systems You Already Run
Most AI programs generate output no one trusts because the integration was built around the demo, not the operating environment. We build the API layer, orchestration logic, and deployment architecture that connects AI to your actual workflows, with the governance boundaries and observability your teams can maintain.
The Integration Failure Pattern
The pilot works. The demo is clean. Then the team tries to connect it to real data, real permissions, and real workflows. The project stalls for months. The issue is almost never the AI model. It is the missing API layer, the undocumented auth boundaries, the LLM outputs that don't map to downstream systems, and the absence of anyone who owns the integration architecture. Integration built for a demo is a liability when it reaches production.
What Structural Integration Requires
Technical integration is not just connecting endpoints. It requires deliberate decisions across five dimensions before a single line of production code is written.
API Architecture
Who owns the API contract? What versioning, auth, and rate-limiting standards apply? Without deliberate API design, every new model or vendor creates a new integration debt.
Security & Permissions Boundaries
Where can the AI touch sensitive data? What audit trails are required? Governance gaps at the integration layer become compliance exposures at scale.
LLM Deployment Design
Which model for which use case? Self-hosted, managed, or API-based? Model selection without deployment architecture design creates vendor lock-in and runaway costs.
Orchestration & Workflow Logic
How does AI output get routed into your existing systems? Orchestration is where most integration programs break. Outputs end up in dashboards no one checks.
Observability & Drift Detection
When the model degrades, how do you know? What does failure look like in your system? Production AI without monitoring is a liability, not a capability.
Specific Engagements
Each offering goes deep on one area of this service. Start where the need is clearest.
API Development & Orchestration
Design and build the API layer and orchestration logic that connects AI models to your existing systems, with auth, rate limiting, and observability built in.
Explore this serviceCustom AI Integration
Integration designed around your specific data model, governance requirements, and stack. Used when off-the-shelf connectors break on your edge cases.
Explore this serviceLLM Implementation & Deployment
Model selection, deployment architecture, prompt engineering, and cost governance for LLM deployments, before the vendor contract locks you in.
Explore this serviceHow an Integration Engagement Works
Four structured phases that build from architecture decisions to production deployment. Each phase has a defined deliverable before the next begins.
Integration Architecture Review
We assess your current stack, data flows, and governance constraints to define what integration is actually feasible, not what looks good in a vendor pitch. The output is a gap map and architecture brief.
API & Orchestration Design
We design the API layer, auth model, orchestration logic, and deployment pattern before writing production code. Decisions made in design prevent rebuilds in production.
Build & Integration
We build the integration on your infrastructure, connected to your systems, with full documentation. No black-box deployments. Your team should understand what they are maintaining.
Monitoring & Handoff
We configure observability, establish drift detection baselines, and hand off to your team with runbooks. The integration is yours, not ours to maintain indefinitely.
Who This Is For
We would rather say this clearly than waste your time.
This engagement is right for you if...
- Mid-market organizations with AI models or APIs that aren't connected to real workflows yet
- Teams where the pilot worked but production deployment has stalled for weeks or months
- Companies whose AI vendors delivered a model but not an integration
- Engineering teams who need integration architecture support, not a replacement for their developers
- Organizations where AI output lives in a dashboard that nobody checks
This is probably not the right fit if...
- Startups building their first product. This engagement is designed for organizations with existing systems to integrate.
- Companies wanting a managed AI service without owning the infrastructure
- Teams looking for a vendor to build and own the integration long-term
Integration That Lasts Past the Demo
Start with an architecture review to understand what your stack can actually support before writing production code.