Step 1
Audit
Find where it breaks, and why.
01 - service
AI Tuning
You shipped an AI feature and everyone liked it. Then real users showed up. Now it contradicts itself, invents facts, and answers in a voice that sounds nothing like you.
That gap between the demo and the daily reality is the whole problem. We close it, so the same question gets the same answer, the tone is yours, and your team trusts it enough to put in front of customers.
What you walk away with
An AI that gives the same answer twice, not a coin flip.
Outputs in your voice, grounded in your real knowledge.
A system your team trusts enough to actually ship.
How it works
Step 2
Engineer
RAG, fine-tuning, guardrails, and evals.
Step 3
Ship
Deployed, monitored, and handed over.
Proof
Zacky, a production RAG agent that builds courses and exam questions from raw PDFs.
90%question-writing time saved
An LLM that audits content and writes SEO metadata from inside the CMS.
6SEO dimensions audited in-editor
Under the hood
RAG and knowledge assistantsFine-tuningAgentic workflowsEvals and guardrailsSelf-hosted modelsMLOps deployment
Is your AI problem fixable?
Questions
Do you build on OpenAI, or self-hosted models?
Both. We choose based on your data sensitivity, cost, and latency.
How long until it is in production?
A scoped pilot ships in weeks, not quarters. The audit comes first.
Tell me what your AI keeps getting wrong.
That is a conversation worth having.