Applied AI
Generative AI & LLM Applications
Products built on top of frontier models: assistants, copilots, document intelligence, and structured extraction.
We build applications on top of the leading language models: Claude, GPT, and Gemini. That means the product around the model: the retrieval, the prompts under version control, the structured outputs your systems can actually consume, the streaming UI, the evaluation harness, and the deployment. We do not train foundation models; we make the good ones already available do something specific and valuable for your business.
What's included
- Assistants & copilots
- Structured extraction
- Streaming UX
- Multi-model routing
What you get out of it
- A working product in weeks, not a proof of concept that stalls at the demo
- Structured, validated model output your existing systems can consume directly
- Model-agnostic architecture, so you can swap or add a provider without a rewrite
The model is 10% of the product
Anyone can call an API. The work that decides whether a feature ships is everything around it: grounding answers in your data, forcing output into a schema your backend can trust, streaming tokens without janking the UI, handling refusals and timeouts gracefully, caching what is stable, and measuring quality on your own examples rather than a public benchmark. That is the part we build.
request: extract(contract.pdf, schema=ContractTerms)route: claude-opus (complex) · fallback gpt (429)context: 18 pages · cached prefix hit (72%)output: JSON validated against ContractTerms ✓confidence: 0.94 · 2 fields flagged for revieweval: 98.1% field accuracy on 240 golden docs
What we build with generative AI
Assistants & copilots
In-product assistants that know your domain, your user's context, and your data, with streaming responses, conversation memory, citations, and a fallback path when the model is unsure.
Document intelligence
Contracts, invoices, reports, and email turned into structured records. The model reads, classifies, and extracts; validation and confidence scoring decide what a human still needs to see.
Content & report generation
Pipelines that draft proposals, summaries, and technical documentation from your own source material, with tone and format constrained so the output is usable, not just plausible.
Classification & routing
High-volume triage across tickets, leads, and transactions, using small, cheap models where they are enough and escalating only the ambiguous cases to a larger one.
Safety & data handling
PII redaction before prompts leave your perimeter, provider agreements that keep your data out of training sets, and deployment options including private cloud when the data demands it.
Technologies we work with
- Anthropic Claude
- OpenAI
- Google Gemini
- Llama
- Python
- TypeScript
- FastAPI
- Next.js
- Pydantic
- Zod
Frequently asked questions
No. We build applications on top of the leading models from Anthropic, OpenAI, and Google, plus strong open-weight options. Where a project genuinely needs specialisation, we adapt an existing open model rather than pre-train one. And we will tell you honestly when retrieval or better prompting solves it more cheaply.
It depends on the task, the latency budget, and the cost per call. We usually route: a small fast model for classification and routing, a frontier model for reasoning-heavy steps. We build the routing layer so you are never locked to one vendor.
We build an evaluation set from your real examples before we build the feature, and score every change against it. Quality becomes a number you can see move, rather than a subjective impression from whoever tried it last.
Yes. We deploy to your cloud account, and for regulated data we can run open-weight models entirely inside your perimeter.
Next step
Need generative ai & llm applications?
Tell us what you're trying to build. We'll give you an honest read on the approach, the effort, and what it costs to run.