Technology. Automated. Simplified.

We build AI systems and then operate them. Eight products of our own are in production right now — the same team builds yours.

An AI assistant · a person confirms scope, timing and price

Eight products, all ours, all running

Designed, built and operated in-house. Open any of them — they are live sites, not case studies.

What we build for other people

The same engineering, pointed at your operation. Three of the six, shown running.

AI product developmentThe idea with nobody to build it

Tell us what it should do and we design it, build it, and run it after launch. Working software every week while we build, and the code is yours to own.

A white robotic hand holding a glowing chip marked AI
AI voice agentsThe calls nobody has time to answer

Agents that answer and place real phone calls in any language, on the number you already publish. Qualification, booking and support, around the clock.

Codnov's voice assistant mid-call: 94% of calls answered, twelve callbacks booked, a lead logged and a confirmation sent, with the transcript alongside
Workflow automationThe work your team repeats every day

We build the software that does it, then keep running it after launch. Not a prototype handed over at the end of a sprint — a system with our name still on it.

Codnov's IDE agent automating a website intake: it has created the HubSpot lead, booked the intro call and posted to Slack, and is proposing a code change for approval

We do not hand over a repository and leave. Every system we build, we also run.

Start from the problem

Six disciplines, written the way the person with the problem would say it. Find yours.

We also teach it

Eleven courses, live and hands-on. The engineers who build the products run the sessions.

Build8 chapters · work at your own pace

The LLM Course

Eight chapters on how language models actually work — and how to use, fine-tune and ship one yourself. Self-paced, with a live session per chapter.

Course details
Start here2 weeks · around 8 hours

Claude at Work

Two weeks on using Claude properly — projects, documents, data, code and repeatable workflows. No coding background needed.

Course details
Start here4 weeks · around 24 hours

AI Foundations

What these models actually are, how they really work, and how to build something with them. No prerequisites.

Course details
Applied10 weeks · around 50 hours

Data Science & Machine Learning

Ten weeks from a raw, broken dataset to a trained model you deployed and can defend. Python, statistics, machine learning and five projects you keep.

Course details
Build8 weeks · around 64 hours

Applied AI Engineering

Eight weeks on real GPU hardware. Fine-tune your own model, build retrieval that works, deploy it and run it.

Course details
AppliedOne day · sessions from 2 to 6 hours

AI in Real World

A one-day workshop for colleges and teams — what these tools really do, where they fail, and what that means for the work in front of you.

Course details
Start here3 weeks · around 12 hours

Prompt Engineering

Three weeks on writing instructions that work — structure, evaluation, and running prompts in production without them quietly degrading.

Course details
Build5 weeks · around 20 hours

Build a Project From Scratch

Five weeks taking one idea from a blank repository to a deployed, working project — using AI as the pair, not the author.

Course details
Build4 weeks · around 16 hours

RAG — Retrieval-Augmented Generation

Four weeks on giving a model your own knowledge properly — chunking, retrieval, grounding, citations, and evaluating whether any of it worked.

Course details
Applied4 weeks · around 16 hours

Introduction to AI Supercomputing

Four weeks on GPUs, accelerators and the machines AI runs on — what they do, how a job gets onto one, and what it costs.

Course details
For teams2 days to 8 weeks, per contract

Corporate & College Batches

Custom AI training for companies and campuses — two-day primer to a full GPU engineering track, built around your stack.

Course details

Tell us what is slowing you down

Describe the problem in plain words. You get a plan back, and a straight answer about whether it is worth building.