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Applied AI

AI & LLM Development

Custom AI features that turn your data into something users actually feel.

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AI is only useful when it ships inside a product and changes what a user can do. We are not a research lab writing papers; we build AI features that go to production and earn their place in your roadmap, whether that is a copilot, a search experience, an automation, or analysis that used to take a human hours.

Most teams do not need to train a model from scratch. They need the right approach to their problem: retrieval-augmented generation (RAG) over their own data, fine-tuning an open model on proprietary examples, or carefully engineered prompts and tools around a frontier model. We help you choose, then build it into your stack rather than bolting on a disconnected demo.

We handle the parts that make AI features hard in the real world: cleaning and chunking your data, building the retrieval layer, evaluating outputs so quality does not regress, controlling cost, and keeping latency low enough that the feature feels responsive. The result lands directly in your Next.js or mobile app, behind your auth, with your data staying where it belongs.

Because we also build the surrounding product, the AI is never an island. It connects to your database, your permissions, and your UI, and we instrument it so you can see what it is doing and improve it over time. You get a feature you can ship to customers, not a notebook that only runs on one laptop.

What's included

  • RAG over your data

    Retrieval pipelines so the model answers from your content, not its imagination.

  • Fine-tuning

    Tuning open models on your proprietary examples when it earns its keep.

  • Evaluation and guardrails

    Output checks and tests so quality holds as prompts and data change.

  • Production integration

    The feature wired into your app, auth, and data, with cost and latency under control.

Tech stack

  • OpenAI
  • PyTorch
  • TensorFlow
  • Vector databases
  • Python
  • Next.js
  • TypeScript

Questions

Do we need to train our own model?

Usually not. RAG or fine-tuning an open model is faster and cheaper for most problems. We recommend the lightest approach that solves yours.

Will our data stay private?

Yes. We design so your proprietary data stays in your infrastructure and is never used to train third-party models.

Can you add AI to an existing product?

Yes. We integrate into your current stack and ship the feature behind your existing auth and data.

Building something in ai & ml? Let's scope it.

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