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The engineering under the AI

Any model, on the direct APIs, wired into a product that already has users.

This is the layer underneath the demos, and it is deliberately model-agnostic. Claude, GPT, Gemini, Llama or a self-hosted model — chosen per task on cost, quality and data-residency rules, integrated on the direct provider APIs or through Bedrock and Vertex where procurement prefers it. Token cost measured per action and capped. Evaluation harnesses so quality is proven rather than asserted. Fallbacks for when the model is confidently wrong.

  • Anthropic API
  • OpenAI API
  • Google AI API
  • AWS Bedrock
  • Vertex AI
  • ONNX Runtime
What you get

Deliverables

Integration on direct provider APIs — Anthropic, OpenAI, Google — or via Bedrock and Vertex
Retrieval and grounding over your own content
Per-action cost measurement, attribution and caps
Evaluation harness with regression tracking
Graceful degradation when the model fails
On-device inference where data residency demands it
Why us for this

Proof from shipped work

Not case studies we admire. Systems we built, running now.

01

Model-agnostic integration layer — Claude, GPT, Gemini or self-hosted, swapped by configuration

02

Built knowledge-base ingestion parsing PDF and Word documents for a live chat product

03

Ships on-device inference with ONNX Runtime where data cannot leave the device

Also relevant

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Next step

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AI Integration & Engineering — PaperByte