AI & Data
LLM assistants, ML and analytics
We bring AI into products: LLM assistants and agents, document search (RAG), recommendation systems, computer vision and analytics. From prototype to production with answer-quality controls.
The difference between a demo and an AI product is control: answers have to be verifiable, cost per request predictable and model behaviour reproducible. We ship LLM assistants, document search, recommendation systems and computer vision so they hold up under a real flow of customers, not only in a presentation.
Who it’s for
Companies with high request volume
Support, sales and internal questions — anywhere an assistant takes routine work off people.
Businesses sitting on documents
Contracts, policies, knowledge bases: search that answers from your documents and cites the source.
Products that need recommendations
Matching products, content or plans based on user behaviour.
What’s included
LLM assistants and agents
Chatbots, copilots and agents tailored to your business.
RAG knowledge search
Answers from your documents with links to sources.
ML and recommendations
Forecasting, scoring, recommendation systems, anti-fraud.
Computer vision and analytics
Recognition, image processing, data dashboards.
How we work
- 01
Discovery
We define what counts as a correct answer and which data the model will work on — and whether AI is the right tool at all.
You getQuality criteria and an estimate
- 02
Prototype
We build a working prototype on your data and measure answer quality on real examples.
You getA prototype with quality numbers
- 03
Engineering
We build the pipeline: data preparation, document search, model selection, guardrails and protection against hallucinations.
You getIntegration into the product
- 04
Launch
We ship with every request logged, watch cost and answer quality, and add human review where a mistake is expensive.
You getAI in production with metrics
- 05
Improvement
We collect feedback and hard cases, refine prompts and rules, and drive the cost per request down.
You getMeasured gains in quality
Timeline and cost
An AI product starts at $45,000. Cost follows the volume and state of your data, the accuracy required and whether you need your own model or an integration with an existing one.
Build an estimate in the calculator →How we do it
We start with a fast prototype on real data, measure quality, and build a pipeline with evaluation and monitoring. We work with leading Claude models and pick the best fit for the task and budget.
Technology
Products of this class
Reference points so you know what we mean — not our work. Yours is built around your processes and brand.
What you get
- 01
An AI feature in production, not a demo
- 02
Control over answer quality and cost
- 03
Measurable impact on product metrics
Frequently asked questions
How much does it cost to add AI to a product?
A full AI product starts at $45,000; integrating an assistant into an existing product starts at $12,000. The main cost driver is not the model but the data: collecting, cleaning and labelling it usually takes longer than the integration itself.
How do you deal with hallucinations?
Answers are grounded in your documents and must cite a source — with no source, the assistant says it doesn’t know. On critical paths we add human review. Quality is measured on a fixed set of real questions before and after every change.
Will our data leak into someone else’s model?
No. We agree this up front: we can work through providers that don’t train on your requests, and for sensitive data deploy a model inside your own environment.
What will it cost to run?
We calculate cost per request at the prototype stage and project it onto your volume. From there we bring it down with caching, the right model for each task and by removing unnecessary calls.
Let’s talk about your project?
Describe your task — within 24 hours we’ll come back with an estimate, timeline and plan.