AI that solves a business problem, not a demo.
LLM integration, retrieval-augmented knowledge systems, custom models, and the MLOps to keep them honest in production. We start with the question, then choose the model.
Problems we're usually brought in for
Pressure to 'do AI' without a plan
Leadership wants AI on the roadmap; nobody has defined what it should change.
Pilots that never reach production
The notebook worked. The integration, monitoring, and cost controls never got built.
Knowledge locked in documents
Answers exist somewhere in your PDFs, tickets, and wikis — nobody can find them fast.
AI & Machine Learning Solutions, end to end
- Use-case discovery: where AI pays off, where it doesn't, and what it will cost to run
- LLM integration with GPT, Claude, and Gemini — with guardrails, evaluation, and cost tracking
- Retrieval-augmented generation over your documents and data
- Custom model training and fine-tuning when off-the-shelf isn't enough
- Computer vision, NLP, forecasting and predictive analytics
- MLOps: deployment, monitoring, drift detection, and retraining
FAQ
Do we need a lot of data?
For LLM-based solutions, usually not — your existing documents and a clear process are enough. Custom models need more, and we'll assess that before proposing one.
How do you keep our data private?
Data stays in your accounts; we use providers' no-training API tiers, restrict access, and document exactly what is sent where.
What does running AI cost?
Typically cents per interaction for LLM features. We build cost caps and monitoring in from the start so there are no surprises — our own site assistant runs this way.
Build or buy?
Often buy the model and build the integration. We'll tell you when an existing product already does the job.
Can you prove it works before we commit?
Yes — most engagements begin with a short, fixed-scope proof of concept against your real data.
Tell us what you're trying to build.
We'll reply with a straight answer on fit, approach, and next steps — usually within a business day.
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