From an LLM feature inside your SaaS to a forecasting model for your ops team, we take AI from prototype to production: data pipelines, evaluation, deployment and monitoring included.
LLM product features
Summaries, search, drafting and extraction inside your existing app.
Predictive analytics
Demand forecasting, churn prediction and scoring models on your historical data.
Document processing
Pull structured data out of invoices, contracts and forms.
Computer vision
Classification and detection for images and video.
What you get
- Data audit and feasibility check
- Prototype with measured accuracy on your data
- Production API and deployment
- Monitoring for drift and cost
Common questions
Do we need a lot of data?
Not always. Many LLM features work with a small amount of good examples. For predictive models we check what you have before promising anything.
Where does our data go?
Wherever your policies allow. We can deploy on your cloud account and use providers with zero data retention.
Further reading
All postsAI··1 min read
Before you build an AI agent, answer these five questions
Most agent projects that stall do so for boring reasons: unclear scope, messy data, nobody owning the edge cases. Here is the checklist we run first.
AI··1 min read
RAG or fine-tuning? A plain-English way to decide
Two ways to make a language model know about your business. They solve different problems, and picking the wrong one is expensive.
Tell us what you're building and we'll tell you how we'd approach it, whether that's AI, Web3 or just a really solid app.