Software development
AI models trained, tuned and tested on your own data
Classification, document extraction, forecasting and fine-tuned language models, evaluated against your real examples before they go anywhere near production.
- Use cases
- Classification, extraction, forecasting, search
- Approach
- Evaluate first, then build
- Deployment
- Your cloud or a managed API
How we build AI models
Starts from your data
We audit what you have before promising what a model can do with it.
Measured before it ships
An evaluation set built from your real cases shows accuracy in plain numbers.
Right-sized
Sometimes a small model or a few rules beat a large language model. We pick what works.
Private when it must be
Models can run in your own cloud so sensitive data never leaves it.
Grounded answers
Retrieval over your documents so answers cite sources instead of guessing.
Watched in production
Monitoring flags when accuracy drifts as your data changes.
What you can expect
An honest accuracy report
Where the model is reliable, where it isn't, and what that means for use.
A production-ready API
A documented endpoint your applications can call.
A plan to improve it
Which data to collect next to make the model better over time.
What's included
Technologies we use
- Python
- PyTorch
- Hugging Face
- OpenAI
- Anthropic
- pgvector
- FastAPI
Data audit
Quality, volume and gaps in the data you have.
Evaluation set
Real examples labelled to measure performance.
Training or fine-tuning
Classic ML or language models, as fits.
Retrieval pipelines
Search and question-answering over documents.
API deployment
Hosted, secured and documented.
Monitoring
Dashboards for accuracy, latency and cost.
How it works
- Step 1
Audit
Look at the data and the decision the model will support.
- Step 2
Baseline
A simple first approach to measure against.
- Step 3
Iterate
Improve until the evaluation numbers justify launch.
- Step 4
Deploy and monitor
Ship behind an API and watch it in use.
Common questions
How is pricing decided?
Every model is scoped to you, so there's no fixed rate card. Message us on WhatsApp or email with a few details and we'll come back with a written proposal.
How much data do we need?
It depends on the task. The data audit answers this before you commit to a full build.
Can we use ChatGPT-style models with private data?
Yes, with the right setup: enterprise APIs with data controls, or open models hosted in your cloud.
What if the model isn't accurate enough?
You'll know at the evaluation stage, before paying for a production build, along with what would close the gap.
Let's talk about what you need
Tell us about your team, timeline and goals. We reply with a tailored proposal, not a rate card.