Services
The three parts of an agent that holds up
Work out which agent pays for itself, give it your own knowledge to answer from, and score what it gets wrong before anyone relies on it. Each is also a service you can buy on its own, and each lists the projects that back it up.
01
Agent Build
Two weeks, one task, one working agent on your own data. The first days settle which task pays for itself and end on a go/no-go — if the answer is no, there is nothing to pay. The rest builds the winner against a test set of your real cases. Code, test set and numbers are yours.
How the build works →Proven by
- Catalogue-to-Offer Agent for a Uniform Manufacturer
A production agent that generates only from real catalogue items, shows its plan before spending, and logs the provenance of every image it delivers.
- Country Explorer: Location Intelligence for Restaurants
A team of agents reads location, footfall and demographic data and writes sourced expansion briefs a human can check against the figures behind them.
- LetAI: Nutrition Estimation Agent in Production Chat
The agent only reached production chat after its evaluation pipeline was expanded, with new datasets and broader case coverage added before release.
- Catalogue-to-Offer Agent for a Uniform Manufacturer
02
Knowledge Base (RAG)
Your own documents start answering questions, and every answer links back to where it came from. We agree on a test set up front and show you the quality numbers before handover.
How we build it →Proven by
- Expert Blockchain Chatbot
Production RAG with quality we measured. Retrieval goes past plain vectors (SQL, entity lookup, real-time data), and an evaluation framework (RAGAS) scores the answers.
- AI Stylist: Fashion Attributes from Expert Video
Expert video turned into structured, queryable data, backed by the product's first labeled garment-image dataset.
- Jazion: An AI Copilot for Language Tutors
Turns recorded lessons into structured, queryable knowledge: a summary, lesson card, Q&A, and quizzes delivered to students automatically.
- AI Book Recommendation Assistant
Answers come from a live product database. Hybrid retrieval (RecSys, vector search, and LLMs) does the work, so the model isn't guessing from memory.
- Expert Blockchain Chatbot
03
Evals / QA
We build test sets from your real cases and score your AI the same way on every release, so a drop in quality shows up before your users run into it.
How evals work →Proven by
- Expert Blockchain Chatbot
Production RAG with quality we measured. Retrieval goes past plain vectors (SQL, entity lookup, real-time data), and an evaluation framework (RAGAS) scores the answers.
- LetAI: Nutrition Estimation Agent in Production Chat
The agent only reached production chat after its evaluation pipeline was expanded, with new datasets and broader case coverage added before release.
- Market Analyst for a Chemical Manufacturer
Recommends what to produce next on evidence a buyer can re-check: every figure traced to its source row and cleared by named quality gates.
- zebra_simple: Zebra Puzzle Test for LLMs
Our published LLM reasoning benchmark. It's open evidence, and anyone can check it.
- Expert Blockchain Chatbot
Built for one industry
Offer sheets for uniform makers
A one-line brief in chat comes back as a branded offer sheet. It uses the manufacturer's own garments and the client's brand colours, in their sheet format, in Serbian or English.
It has been running on their real client work since July 2026.
See what it does →
Not sure which one you need?
Tell us what you're trying to solve. If an agent is the wrong tool for it, we'll tell you that too.