AI & LLM Engineering

AI systems built to ship — with evaluation, guardrails, and observability from day one.

Trodas designs, builds, and deploys AI systems that survive production traffic. Retrieval-augmented assistants grounded in your private data, document intelligence pipelines, and LLM workflows engineered with the same discipline as any other mission-critical software. Every engagement includes evaluation harnesses, prompt-regression tests, and operational dashboards — because AI quality is engineering, not luck.

60%

average reduction in manual reporting time

24/7

AI coverage replacing first-line manual workflows

2–4 wks

from kickoff to first working production prototype

What you get

Deliverables, not deliverable-shaped promises.

  • RAG assistants grounded in your internal documents and data
  • LLM workflow automation — summarisation, extraction, classification
  • AI agent systems with tool use and multi-step reasoning
  • Document intelligence — parse, extract, and structure unstructured files
  • Model evaluation suites and prompt-regression testing
  • FastAPI backends with streaming, auth, and rate limiting

Stack we typically use

LangChainLangSmithOpenAIClaude APIFastAPIPineconepgvectorPython

Not sure if this fits your problem? Bring it to a discovery call — we'll tell you honestly, including when the answer is “you don't need us for this.”

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FAQ

Common questions about this service.

Will the AI work with our existing data and tools?

Yes. Trodas integrates with your documents, databases, CRMs, and internal APIs. Most engagements begin by indexing what already exists — PDFs, Notion, SharePoint, support tickets, or product docs.

Which models does Trodas recommend?

Model-agnostic by design: OpenAI, Anthropic Claude, or open-source models on private infrastructure when data residency demands it. The recommendation depends on your accuracy, latency, and budget constraints.

How are hallucinations handled?

Grounded retrieval, mandatory citation, confidence thresholds, and evaluation suites that test against known-answer sets before every release. AI quality is treated as an engineering discipline.