AiGanakA model of your own, proven in stages before you commit.
We build domain-specific small and large language models with the training pipeline, data tooling and evaluation methods developed for AiGanak-SLM-1B. You see results at each stage before deciding on the next.
AiGanak builds domain-specific small and large language models in stages: a proof of concept on sample data with an evaluation report, an MVP for a pilot team, then private production deployment. The work uses the training pipeline built for AiGanak-SLM-1B.
Problems this solves
General models miss your domain
Your documents use codes, formats and vocabulary that large general models handle poorly.
High volume, narrow task
Millions of similar extraction or classification calls are too expensive on a large API.
Data sovereignty
You need a model you control, running on your hardware.
Deliverables
- Proof of concept on your sample data, with an evaluation report
- MVP behind an API or chat interface for a pilot team
- Model card with training data summary and evaluation results
- Quantised builds for your target hardware
- Private production deployment and monitoring
Engagement steps
Proof of concept
A small model or fine-tune on sample data, scored in Model Lab. Usually two to four weeks.
MVP
A working model for a pilot team, with feedback and error analysis.
Production
Private deployment, monitoring and evaluation loops.
Often paired with
AiGanak-SLM-1B
AiGanak-SLM-1B is AiGanak's in-house 1-billion-parameter decoder-only small language model with a 128k-token context window, built first for logistics and supply-chain documents. It is in Phase 2 convergence and has not been released.
Learn more →Private and on-premise LLM deployment
AiGanak deploys and optimises open-weight language models in a client's own cloud account or on-premise, including offline setups, so enterprise data never reaches public AI APIs.
Learn more →Model Lab
Model Lab benchmarks fine-tuned and candidate models against production baselines on your own examples, scoring accuracy, drift, hallucination and latency, and emails a performance report.
Learn more →Questions people ask
Can't find it? Press ⌘K and ask.
What is a small language model?
A small language model has roughly 0.5 to 7 billion parameters. It can run on a single server or edge device, so data stays in-house, and it handles focused tasks such as extraction, classification and document question answering well.
How does a custom model project start?
With a proof of concept on your sample data and an evaluation report, usually in two to four weeks. You decide on an MVP only after seeing results.
Tell us what you're building.
AiGanak Agent drafts a scoped proposal in a few minutes. An engineer reviews it with you.