© 2026 — Craxinno Technologies
AI that reaches production, not just a demo. LLM integrations, retrieval over your own data, agents and automation — built with the evaluations and guardrails that survive real users.
We start with the workflow, not the model — where AI genuinely saves time, and where a plain script would do the job better.
A narrow working slice against your real data, so you can judge quality before committing to a full build.
Evaluations, guardrails, fallbacks and cost controls — the difference between a demo and something you can put in front of customers.
Integrated into your product, monitored in production, and tuned as prompts, models and pricing keep moving.
Don’t see what you’re looking for? Email us — we reply within 6 hours.
Almost always an API is enough. Fine-tuning and self-hosting cost far more and are rarely the reason a product succeeds. We use OpenAI or Claude with good retrieval and prompting, and only look at anything heavier when there is a concrete reason.
Retrieval-Augmented Generation means the model answers using your documents rather than only what it was trained on. If you want answers grounded in your own content — policies, catalogues, support history — you need it. If you only need general language ability, you do not.
Grounding answers in retrieved sources, constraining what the model is allowed to do, adding evaluations that run on every change, and designing the UI to show its working. Hallucination is reduced by engineering, not by a better prompt alone.
Model usage is priced per token and adds up quietly at scale. We estimate running costs during the prototype, then reduce them with caching, smaller models for easy steps, and prompt trimming — so the bill is known before launch, not after.
