Service · AI development
AI that ships to production. Not just to the demo.
Most AI projects look great in a demo and stall before real users touch them. Nythrex designs, builds and runs AI systems end-to-end — with evaluation, guardrails, integrations and cost control from the first week, running in your cloud and owned by you.
Your users
- Web & mobile apps
- Slack / Teams
- Support widget
- Internal tools
- API
AI layer
- Prompts & model routing
- Retrieval (RAG)
- Agents & tool calling
- Guardrails
- Evaluations
Integrations
- CRM
- ERP
- Helpdesk
- Document storage
Your cloud & data
- Azure / AWS / GCP
- Databases
- Vector index
- Logs & audit trail
What we build
AI assistants over your data
Answers from your documents, wiki, tickets and databases — with citations, access control and freshness.
AI agents that take actions
Agents that read, decide and act in your systems — with limits, approvals, logs and a kill switch.
AI inside the systems you run
CRM, ERP and helpdesk automations: classification, drafting, routing, summarisation.
Document AI
Extract structured data from invoices, contracts, waybills and scans — with human review where it matters.
Why AI pilots stall — and what we do differently
A demo proves that a model can answer. Production has to prove that it answers correctly, safely, affordably and every time, on messy real data, for users who will try things nobody planned for. That gap is where most AI budgets disappear. We covered the failure patterns in detail in Why AI pilots die before production.
A typical AI pilot
- Success is judged by watching a few demo questions
- Tested on clean sample documents
- No plan for wrong answers or abuse
- Token costs discovered after launch
- Runs in the vendor’s account with the vendor’s keys
- Nobody owns adoption on the business side
How Nythrex runs AI projects
- A written goal and a test set of real cases before building
- Evaluated on your real, messy data from the proof of concept
- Guardrails, fallbacks and human review designed in
- Cost per request modelled and monitored
- Runs in your cloud; you own code, prompts and data
- A named business owner and a rollout plan
How an AI project runs
Discovery
Goal · data · risks
Proof of concept
Test set · go / no-go
Build
Integrations · UX · guardrails
Hardening & launch
Load · security · rollout
Operate & improve
Monitoring · evals
- 1
Discovery: decide what “good” means
We agree on the business goal, the users, the data sources and the risks — and collect 30–100 real examples with expected answers. That test set becomes the scoreboard for the whole project.
- 2
Proof of concept: answer the risky question
The PoC exists to kill or confirm the idea cheaply. We build the thinnest end-to-end slice, run it against the test set and give you a go / no-go with numbers: accuracy, latency and cost per request.
- 3
Build: everything around the model
Integrations, permissions, UX, guardrails, human-review flows, monitoring and deployment in your cloud. This is usually most of the work — and exactly the part demos skip.
- 4
Launch and operate
Staged rollout, a feedback loop from real users, dashboards for quality and cost, and regular evaluation runs whenever prompts, models or data change.
What “production-ready” means to us
Our definition of done for an AI feature
0/8Model-agnostic by design
We are not tied to a model vendor. Depending on your requirements we work with OpenAI and Azure OpenAI, Anthropic Claude (directly or via AWS Bedrock and Google Vertex AI), Google Gemini, and open-weight models you can host yourself. The architecture keeps the model behind an interface, so switching providers is a configuration change plus an evaluation run — not a rewrite. See OpenAI vs Azure OpenAI vs Anthropic for how we choose.
| Requirement | What we typically recommend |
|---|---|
| Data must stay in the EU | A provider with an EU data-residency option, or a self-hosted open-weight model |
| Enterprise procurement already on Microsoft | Azure OpenAI inside your Azure tenant |
| Already on AWS or GCP | Claude via Bedrock or Vertex AI; Gemini on GCP |
| High volume, simple tasks | A small, fast model with routing to a larger one only when needed |
| Strict offline / on-premise | Open-weight models on your own infrastructure |
Not ready to talk yet? Start here
AI readiness assessment
Ten questions, five dimensions, one honest answer about where to start.
AI project effort estimator
Rough timeline, team and effort for your AI project in two minutes.
RAG, fine-tuning or prompting?
A decision tree that stops you paying for the wrong approach.
LLM API cost calculator
What your chatbot will cost per month — before the invoice tells you.
Frequently asked questions
Keep reading
Want a second opinion on your project?
Tell us what you’re building and where you’re stuck. We’ll reply within one business day with the most practical next step — even if that step isn’t us.