Models into your product
OpenAI, Claude, Groq or another provider, wired into an application you already run.
AI Wired Into Things That Already Work
Service 04
We are not an AI company and will not sell you a strategy. We wire language models into products that already exist, to do one defined job, and we have shipped two of them.
What we have actually built
Our publishing system ingests RSS feeds, rewrites each item through a language model and schedules the result automatically — the interesting engineering was never the API call, it was making ingestion idempotent so a flaky feed cannot create duplicate posts. Our desktop transcription tool turns recorded sessions into drafted notes on the practitioner's own machine, because those recordings are not allowed to leave it.
Where AI features go wrong
The model is the easy part; a weekend gets you a working demo. What breaks in production is everything around it: what happens when the API is down, what a wrong answer costs, who checks the output, how you cap spend, and whether the data is even allowed to leave the building. We build the boring parts, because those are the parts that decide whether the feature survives.
What's included
Everything in this service
Generation pipelines
Ingest, transform, review, publish — with a human checkpoint wherever one belongs.
Transcription and notes
Speech to text plus structured summaries. Locally when the data cannot be sent anywhere.
Extraction from files
Pulling structured fields out of PDFs, invoices and forms, with confidence handling.
Answering from your own content
A chat interface grounded in your documents, that says it does not know instead of inventing.
Spend you can predict
Caching, token limits and fallbacks, so the bill does not surprise anyone next month.
How it runs
From brief to handover
Step 01
Define the one job
We narrow it to a single task with a measurable output. Broad AI projects fail broadly.
Step 02
Check the data
Where it lives, whether it can leave, and who is allowed to see the output.
Step 03
Build small
The narrowest useful version first, then widen it once it holds up in real use.
Step 04
Instrument it
Logging, cost tracking and a way to tell when quality drops. Then you decide whether to expand.
Questions
Before you brief us
We can build one that answers from your documents and admits when it does not know. That is retrieval, not training, and it is almost always what people actually want.
Not necessarily. Our transcription tool runs entirely on the user's own machine for exactly that reason. Local models are weaker than hosted ones, and the trade-off is worth it when privacy is the constraint.
Whichever fits the job and the budget. We have shipped with Groq. We have no partnership with anyone and no reason to push one.
Yes, and often. A lot of what gets briefed as an AI feature is a database query, a form, or a better-written email template.
Have a Project Like This?
Send the brief. You get a written scope with a fixed price and a date — no call required to find out what it costs.