Machine Learning Solutions

NLP & LLM engineering

RAG systems, document extraction, chat and agents, built evaluation-first so quality is measured before anything reaches users.

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Language systems that hold up

Most LLM demos take a weekend. Making one reliable enough to face customers or auditors is the real work: grounding answers in your sources, handling the documents that break parsers, and proving quality release after release. We build LLM applications, RAG pipelines, extraction services, chat and agent systems with that bar in mind from day one.

Delivery is evaluation-first. Before we tune prompts or choose a model, we build a test set from your real documents and questions, with pass criteria you agree to. Every change is scored against it, so hallucination rate, extraction accuracy and latency are numbers on a dashboard, not impressions from a demo.

You end up with a system your team can change safely: swap the model when a better one ships, edit prompts, extend the corpus, and the eval suite tells you within minutes whether quality held. Costs are tracked per query, so the decision to scale comes with a price tag attached, not a surprise invoice at month end.

What evaluation-first buys you

Grounded answers

RAG with citations back to your sources, so every answer can be checked and hallucination rate is measured, not guessed.

Evaluation before launch

A test set built from your real data gates every release. If quality drops, the deploy stops.

Model independence

OpenAI, Anthropic or open weights behind one interface. When a better or cheaper model ships, switching is a config change plus an eval run.

Cost per query, known

Token spend is tracked per feature and per customer, with caching and routing to keep the bill proportional to value.

Structured output you can build on

Extraction returns typed, validated JSON your systems consume directly, with confidence scores for review queues.

Machine Learning

From eval set to production

Build the eval set

We collect real documents, questions and edge cases from your domain and define pass criteria with your experts.

Prototype against it

First working pipeline in days: retrieval, prompts and model choice iterated against the eval set, not opinions.

Harden for production

Guardrails, fallbacks, rate limits, structured logging and cost controls around the happy path.

Ship and monitor

Live quality sampling, drift alerts on inputs and a feedback loop that turns user corrections into eval cases.

What you get

Evaluation suite built from your real documents

RAG or extraction pipeline deployed behind an API

Prompts and model configuration under version control

Cost and quality dashboard per query

Runbook for corpus updates and model swaps

Questions, answered

Which model will you use?

The one that passes your eval set at the lowest cost. We benchmark OpenAI, Anthropic and open-weight models on your actual tasks, and the architecture keeps the choice reversible.

How do you handle confidential documents?

Your data stays in your cloud or on EU-hosted endpoints, with no training on your inputs. Where policy requires it, we deploy open-weight models fully inside your infrastructure.

What about hallucinations in front of customers?

Retrieval grounds answers in your content, citations make them checkable, and guardrails catch off-policy output. We measure hallucination rate on the eval set and gate releases on it.