AI & machine learning
AI that does the work, not only chats
We start with a metric, not a model. We define what should get faster, cheaper or more accurate, prototype in a couple of weeks, measure quality, and only then scale.
What clients come with
- 01Take load off support and sales
- 02Find answers across thousands of documents in seconds
- 03Add AI features to an existing product
- 04Automate busywork that needs common sense
What we build
- LLM assistants and agents connected to your systems
- RAG: answers from your knowledge base with sources
- Classification, data extraction, moderation
- Voice and chat interfaces in natural language
- Data pipelines and model quality evaluation
- Model selection for the task and budget, including self-hosted
Rules & standards
- EU AI Act readiness
- GDPR
- Self-hosted models when data must stay in-house
We design with these requirements in mind. The exact set is defined during the brief.
Concept case
ConceptAI & machine learningE-commerce
An AI support assistant for an online store
- Client
- Electronics retailer, 3,000 requests a day
- Challenge
- Agents drown in repetitive questions about order status and returns.
- Solution
- An LLM assistant with access to orders and the knowledge base
- Hand-off of complex cases to an agent with full context
- Answers with links to sources
- An answer quality dashboard
- Goals
- Routine questions resolved without an agent
- Answers in seconds, 24/7
- Every answer can be verified
Stack
- Python
- LLM
- RAG
- pgvector
- React
Timeline8 weeks
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