AI where it actually amplifies.
We help companies adopt LLMs to get real value — not “a neural net for the press release.” From process audit and strategy to production agents, RAG systems and team training.
We live in Cursor, Claude, GPT, Gemini and dozens of niche tools every day. We know where AI saves hours — and where it only gets in the way. We help skip the obvious pitfalls.
Engagement formats
AI strategy
Process audit, opportunity map, ROI, roadmap for 6–12 months.
RAG systems
Corporate knowledge bases retrieved through LLMs. Precise answers, with citations to the source.
AI agents
For support, sales and operations. Not chatty bots — doers: tool use, memory, guardrails.
Team training
Workshops on productive use of Claude / GPT / Cursor. AI rituals, prompt libraries, automation templates.
Fine-tuning
SFT, DPO, distillation, quality evaluation, safe rollout. When a general model stops being enough.
Evaluation & safety
Eval loops, prompt regression tests, hallucination monitoring, prompt-injection defense.
Toolkit
We pick the model and orchestration for the task. Open models when they're strong enough; frontier when the task demands it.
How we run a project
Starter audit
1–2 weeks: we review processes, map opportunities, estimate ROI. No obligation to continue.
Pilot project
1–2 months: one real case (e.g. an AI support copilot). Production launch with metrics.
AI resident
3+ months: long-term partnership, growth, training. We become your AI department.
Answers to the things people ask most
Where should LLM adoption start in a company?
Start with a short audit: 1–2 weeks to review your processes, map opportunities and estimate ROI. You're not committed to continue.
Which models do you use?
Claude Sonnet 4.6 and Opus 4.8, GPT-5.5, Gemini 3.1 Pro and Flash, and open models Llama 4, Qwen3, DeepSeek V4, Kimi K2.6 and MiniMax M3. We choose by quality, cost, latency and on-prem requirements.
How is RAG different from fine-tuning?
RAG injects relevant documents into the prompt at query time — great for current, frequently changing knowledge. Fine-tuning updates the model weights — better for style, format and durable patterns. The two are often combined.
How do you measure AI quality?
We collect real user queries and run automated checks before every release: compare answers to the previous version, track errors and made-up facts. We decide from the numbers, not from “feels better, ship it.”
Where could AI amplify your business?
An hour of free consultation: we'll look at your processes and find 3–5 places where LLMs will pay back in a quarter.