Custom AI models trained on your data
General-purpose AI models don't know your products, processes or terminology. ColibriCode builds custom AI models that do — fine-tuned and evaluated on your data, measured against your real tasks, and deployed privately so your intellectual property never leaves your control.
What we build
Fine-tuned language models
Models that understand your domain vocabulary, documents and workflows.
Reinforcement-learning-tuned models
Improved on real usage data and expert feedback.
Small, fast specialist models
Replace expensive general models for high-volume tasks.
Classification and extraction models
For documents, tickets, claims and records.
Evaluation suites
Prove a model works on your tasks before and after every update.
How a custom model project works
- 1
Baseline (2–3 weeks)
We test leading off-the-shelf models on your real tasks. If one is good enough, you save the training cost.
- 2
Data preparation
Curate, clean and label training data with your experts; remove sensitive fields.
- 3
Training and evaluation
Fine-tune (LoRA/QLoRA, full fine-tuning or RL) and benchmark against the baseline.
- 4
Private deployment
Serve the model on dedicated GPUs in your cloud, ours or on-premises, with quotas and monitoring.
- 5
Continuous improvement
Retrain on new data and feedback under a managed plan.
Why ColibriCode for custom models
- We train, quantize and serve large models on H100-class GPUs for our own products
- Evaluation-first: no model ships without beating the baseline on your benchmarks
- You own the resulting model weights and training pipeline
- Open-weight foundations, so you are never locked to one AI vendor
Frequently asked questions
When is a custom model worth it versus RAG or prompting?
When the task is high-volume, highly specialized, latency-sensitive or must run privately. For knowledge questions over changing documents, RAG is usually better — and we often combine both.
How much data do we need?
Often less than expected: a few thousand high-quality examples can move a fine-tuned model significantly. The baseline phase tells you exactly.
Who owns the model?
You do, including weights, training code and evaluation sets.