Can you build on our existing ChatGPT / Copilot prototype?

Yes. We typically start by writing a test set from real usage, measuring the prototype, and then rebuilding the parts that don’t meet the bar — keeping what works.

Which languages do your LLM apps support?

Any language the chosen model handles well. We routinely build and evaluate in English and Ukrainian, and test other languages on your own examples before committing.

Service · LLM applications

LLM applications people actually keep using.

Building a chat window on top of a model takes an afternoon. Building an LLM product that people trust, that handles edge cases, that stays affordable at scale and that you can improve safely takes engineering. We build the whole thing: UX, backend, AI layer, integrations and operations.

The loop that keeps an LLM product improving without breaking what already works.

What we build

Copilots for internal teams

Assistants embedded in the tools your people use, grounded in your data.

Customer-facing assistants

Support and product assistants with brand-safe answers and clean human hand-off.

Document processing

Extraction, classification and summarisation for invoices, contracts and forms.

AI features in SaaS

Smart search, generation and automation inside your product, priced so margins survive.

Agents & automations

Multi-step workflows across your systems with approvals and audit.

Evaluation & rescue

An LLM app already live but unreliable? We add measurement first, then fix.

LLM UX: the part most teams skip

Show sources so users can verify instead of trusting blindly.
Stream responses and show progress for multi-step work — perceived speed matters.
Make correction easy: edit, regenerate, thumbs down with a reason. Every correction is future training data.
Design the “I don’t know” state as carefully as the happy path.
Set expectations in the interface: what the assistant can and can’t do.

Typical stack

Frontend

React / Next.js
React Native
Streaming UI
Feedback capture

AI layer

Model gateway
Prompt registry
RAG
Tool calling
Guardrails
Evals in CI

Backend

.NET
Node.js
Python
Queues
Auth & permissions

Cloud & ops

Azure / AWS / GCP
Terraform
Observability
Cost dashboards

Frequently asked questions

ServiceRAG developmentRAG development: AI assistants that answer from your documents and data with citations, permissions and measured accuracy — built in your cloud.ServiceAI agentsAI agent development by Nythrex: agents that read, decide and act in your CRM, ERP and internal tools — with permissions, approvals, audit logs and cost limits.GuideLLM evals for non-ML teamsHow to evaluate an LLM feature without a data-science team: build a test set, choose metrics, use LLM-as-judge carefully, run evals in CI and read the results.Guide11 ways to cut your LLM billLLM cost optimisation: prompt caching, model routing, shorter context, better retrieval, batching and budgets — checked against evals so quality holds.ComparisonOpenAI vs Azure OpenAI vs AnthropicChoosing an LLM platform: OpenAI, Azure OpenAI, Anthropic (direct, Bedrock, Vertex AI), Gemini and open-weight — by data, procurement and quality.

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.

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