Below is exactly how we build each kind of system: the problem you have, the path we take, the techniques we use, what can go wrong, and what you receive at the end. No black boxes.
General chatbots know a little about everything and nothing about your business. A custom model speaks your terminology, follows your rules and runs where you decide — even fully offline.
Runs on a laptop, phone or a single small server. Classification, routing, extraction, short answers. Near-zero running cost, works without internet.
The sweet spot for most companies: strong reasoning in your domain, fast responses, runs on one dedicated GPU server. Support, drafting, analysis.
Complex reasoning, long documents, multi-step decisions. For legal, medical, engineering and financial work where precision matters most.
"We can't send our data to a public AI." — The model runs in your environment; your data never leaves.
"It answers confidently but wrong." — We train on your verified material and measure accuracy before launch.
"Monthly API bills keep growing." — A right-sized model you own has a fixed, predictable running cost.
"It doesn't understand our jargon." — Your terminology, formats and tone are learned directly.
We agree what the model must do and write an evaluation set of 200–1,000 real questions with correct answers. This "exam" decides success — not opinions.
Documents, tickets, emails, manuals, databases. We remove duplicates and personal data, fix formatting and keep only what is accurate.
We turn raw material into thousands of instruction–answer pairs, reviewed by your experts where it matters. Quality beats quantity.
We start from a proven open-weight foundation of the right size and licence for commercial use. Bigger is not always better — we pick the smallest model that passes the exam.
Supervised fine-tuning with parameter-efficient methods (adapters), then preference tuning so answers follow your style and refuse what they should refuse.
Facts that change (prices, policies, stock) are not baked into the model; they are retrieved at answer time from your sources, with citations.
We run the exam, compare against general-purpose AI, and red-team the model with tricky and adversarial questions. You see the scores.
Quantization makes the model smaller and faster with minimal quality loss. We deploy it on your server, private cloud or device, behind an API your systems can call.
Every answer can be rated. Weak spots become new training data. The model gets better every month.
Typical timeline: pilot 3–4 weeks · production 6–10 weeks.
Most voice bots feel robotic: you wait, they talk over you, they misunderstand. We build agents you can interrupt, that answer while still thinking, and that admit when they didn't hear you.
People hang up after two seconds of silence. We stream everything: speech is transcribed as you speak, the answer is generated word by word, and audio starts before the full sentence exists.
When the caller speaks, the agent stops instantly, discards the stale answer and listens — without mistaking its own voice or background TV for the caller.
Noise, accents and names cause errors. We filter recognition artefacts, check confidence and ask again when unsure instead of guessing.
Written text sounds wrong when spoken. Answers are shaped for the ear: short sentences, numbers read naturally, no symbols or lists spoken aloud.
Microphone → voice activity detection → streaming speech recognition → reasoning with your knowledge and tools → sentence-by-sentence speech synthesis → playback with interruption control. Every stage is measured in milliseconds.
An agent reads a request, plans the steps, uses your tools, checks its own work and reports back. The difference between a toy and a production agent is control.
The request is split into small, verifiable steps before anything is touched.
Each agent has a tool budget, a time budget and a list of allowed actions. It cannot wander or loop forever.
A separate checker reviews the output; failures are retried with a different strategy, not repeated blindly.
Anything that changes data, sends messages or spends money waits for human approval.
If a worker crashes, the task is picked up again exactly once — no duplicates, no lost work.
Every step, tool call and decision is logged and replayable.
We run a fleet of such agents on our own infrastructure every day — that operational experience is what we bring to your project.
Describe it in a few lines. We'll reply with the path we'd take, the size of model or system it needs, and an honest estimate.