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Applied AI

AI Engineering & Integration built into real products.

We bring AI into products that ship — retrieval-augmented assistants grounded in your data, reliable LLM pipelines with evals and guardrails, and computer-vision features that hold up in production. Not demos: measured, monitored, and maintainable AI.

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RAG Grounded in your data
Evals Quality you can measure
Guardrails Safe, observable output
What we build

AI Engineering & Integration, end to end.

RAG assistants & chat

Assistants grounded in your own documents and data — accurate, cited, and current.

LLM pipelines

Structured extraction, classification, and generation with evals, retries and guardrails.

Computer vision

Detection, OCR, and quality-control models moved from notebook to production.

AI feature integration

Dropping AI into an existing app the right way — cost-controlled and observable.

How we deliver

A clear path from
idea to launch.

01

Frame the problem

We define the task, the success metric, and whether AI is even the right tool.

02

Prototype & evaluate

A working prototype scored against an eval set — not vibes.

03

Harden for production

Guardrails, fallbacks, caching, cost controls, and observability.

04

Monitor & improve

Track quality and cost in the wild and tune against real usage.

The toolkit

Proven tools, chosen for fit.

We pick technology for your problem, not our comfort — mature, well-supported tools your team can hire for and maintain long after launch.

Claude / OpenAI Python Vector DBs LangChain / custom PyTorch ONNX Evals & tracing Laravel / Node APIs
FAQ

AI Engineering & Integration questions.

Can't find your answer? A 30-minute call sorts it out fast.

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Yes. We design for data control — using providers and configurations that keep your data private, with the option of self-hosted or region-locked models where compliance requires it.

We treat AI like any other system: an evaluation set to measure quality, guardrails and validation on output, fallbacks for failures, and observability so you can see what the model is doing in production.

That is one of the most common engagements — integrating a grounded assistant, search, or automation into an app you already run, with cost controls from day one.

Caching, right-sizing models per task, and monitoring token usage. We build cost visibility in so spend never surprises you.

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Ready to build your ai engineering & integration?

Tell us what you're building. We reply within 24 hours — and every engagement starts with a fixed-scope discovery sprint.

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