Production enterprise AI, engineered end-to-end.
We design, build, and deploy intelligent agents, custom models, and automation systems that fit inside your existing stack. Every engagement ends with a system running in production, owned by your team.
The problem
Most AI pilots never leave the pilot phase.
Demos look great. Then the reality of integration, security, data quality, latency, and cost kicks in, and the project stalls. The model works. The system around it doesn't.
Trelnox builds the system. Our work is in the integrations, the evaluation harnesses, the monitoring, and the day-two operations that determine whether AI becomes a tool your team actually uses.
What we build
Every layer of a working AI system.
Agentic systems
Multi-step agents that operate across your existing tools — ticketing, ERP, CRM, internal APIs — with guardrails, observability, and human-in-the-loop control.
Custom models & fine-tuning
Domain-tuned LLMs, embedding models, and classifiers trained on your data and evaluated against outcomes that actually matter to the business.
Intelligent workflows
AI-driven automation for document processing, routing, enrichment, and approvals — integrated with the systems your team already uses.
Retrieval & knowledge systems
Production-grade RAG pipelines and knowledge bases that make internal documents, tickets, and code instantly searchable and actionable.
Evaluation & governance
Test suites, eval harnesses, and monitoring so you can deploy AI with confidence and see exactly how it performs on your real workload.
AI-powered product features
Copilots, assistants, and intelligent interfaces built directly into your customer-facing products and internal tools.
Our approach
A four-phase path from idea to production.
Problem framing
We start with the business outcome, not the model. What decision gets made faster? What work gets automated? What does success look like in numbers?
Prototype with evals
A working prototype inside your environment, backed by an evaluation harness we build alongside it. You see actual performance on your data — not vendor benchmarks.
Production hardening
Reliability, latency, cost, monitoring, guardrails, and the integrations required to make the system part of day-to-day operations.
Handover & iteration
Your team operates the system. We support, extend, and iterate — or step back entirely. Either way, nothing is locked to us.
FAQ
Common questions.
- Do you build with a specific AI provider?
- We work with whichever provider fits — OpenAI, Anthropic, open-weight models you host yourself, or a mix. Selection is driven by cost, latency, data governance, and task performance on your actual data.
- Can AI agents work inside our existing systems?
- Yes. Most of our work involves integrating agents with CRMs, ticketing systems, internal APIs, ERPs, and custom databases. Integration is the work — the model is the easy part.
- How do you prevent AI from making costly mistakes?
- Through guardrails, evaluation suites, human-in-the-loop controls, and monitoring. We measure AI the same way we measure any production system: observable metrics, alerting, and clear fallback behavior.
- What does a typical engagement cost?
- Engagements are scoped per project. Pilots start small and tightly scoped so you can see value before committing to a larger rollout. We discuss numbers on the first call.
Have an AI problem worth engineering?
Tell us what you're trying to build. We'll tell you what it takes to ship it.