Service — 03
AI development that works in production.
AI development services for companies that want working software instead of a proof of concept that dies in a notebook. noqyris builds LLM-powered features, retrieval pipelines, and AI automation that ship inside real products — with the evaluation and guardrails production demands.
Who this is for
- Product teams adding AI features customers will actually pay for
- Operations teams drowning in manual document, email, or data processing
- Companies that tried a chatbot wrapper and need something deeper
What you get
The work, itemized.
LLM-powered features
Summarization, extraction, generation, and copilot features built into your product with proper evaluation behind them.
Retrieval & knowledge systems
RAG pipelines over your documents and data, so answers stay grounded in your own knowledge.
AI automation
Agents and pipelines that process documents, triage inboxes, and handle the repetitive work your team shouldn't do by hand.
Production hardening
Evals, monitoring, cost controls, and fallbacks — the difference between a demo and a system you can put in front of customers.
Process
- 01 Scope
We identify where AI genuinely beats conventional code in your workflow — and say so plainly where it doesn't.
- 02 Prototype
A working pilot on your real data within weeks, with honest accuracy numbers attached.
- 03 Ship
Production integration with evaluation suites, monitoring, and cost ceilings built in from day one.
- 04 Support
Model updates, eval reruns, and tuning as providers evolve — offered per release, never as a forced retainer.
The full engagement — timelines, guarantees, handover — is public: read how an engagement works.
Pricing
Scoped, owned, guaranteed.
Every AI build starts with a pilot on your real data, scoped in the Roadmap Sprint, with measured accuracy numbers attached. Production builds are quoted per milestone after that.
The first step — low-risk & guaranteed
Roadmap Sprint.
5 days
Before committing to a full build, buy one week of focused product thinking. You bring the idea or the problem; the sprint turns it into a plan any competent team could execute — mine or anyone else's.
- Written product spec — The v1 cut in plain language: what ships, what waits, and why.
- Architecture & stack plan — Data model, integrations, and infrastructure — sized for where you'll be in a year, not a pitch deck.
- A quote & timeline — A real quote and a real date for the full build — no estimate ranges that double later.
- Risk register — The three things most likely to sink the project, and the plan for each.
“If the sprint doesn't end with a plan you could hand to any developer and build from, you don't pay.”
Book the sprintFAQ
How is pricing structured?
After the Roadmap Sprint, a scoped pilot on your real data comes before any larger commitment — measurable results first. Production builds are then quoted per milestone like any other build.
How do you keep our data safe?
Your data stays in your infrastructure wherever possible, provider data retention is turned off, and every pilot starts with a written data-handling agreement.
Which models do you work with?
Provider-agnostic: Claude, GPT, and open-weight models — chosen per task on quality, latency, and cost, with fallbacks so you're never locked to one vendor.
What if AI is the wrong tool for our problem?
Then you'll hear that in the first call. A scoped automation or a plain application is often cheaper and more reliable — and building the right thing is the whole point.
See what ships here — browse the products.
Have something to build?
You'll talk directly to Djordje Subotic, the person who builds it. You'll hear back within one business day — usually within a few hours.