Service
AI & Machine Learning
Practical AI that does something useful.
We build AI features where they genuinely help: answering questions from your own documents, summarising long material, classifying incoming requests, or automating the first draft of repetitive work. Everything is designed with fallbacks, cost awareness and clear limits.
What it is used for
- Internal assistants over company documents
- Retrieval-augmented search (RAG)
- Summarising and classifying incoming content
- Draft generation inside an existing workflow
What you receive
- Working AI feature in your product
- Retrieval pipeline with your own data
- Prompt and evaluation notes
- Cost and fallback behaviour documented
Technologies
- Python
- LLM APIs
- RAG
- Ollama
- Node.js
- MongoDB
How we deliver it
Step 1
Find the real use case
We test whether AI is the right tool before building anything with it.
Step 2
Prepare the data
Chunking, embeddings and retrieval quality — where most AI projects are won.
Step 3
Build the feature
Model calls, guardrails, streaming responses and sensible error handling.
Step 4
Evaluate and tune
Test with realistic questions, measure quality, and document the limits.
Common questions
Not sure which service fits?
Describe the problem in plain words — we will map it to the right approach in the first call.
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