Machine Learning Solutions

NLP & LLM engineering

Assistants that answer from your own documents, data extraction, chat and automation, built so quality is measured before anything reaches users.

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Language systems that hold up

Most language-model demos take a weekend. Making one reliable enough to face customers or auditors is the real work: making sure every answer comes from your own sources rather than the model's imagination, handling the documents that break parsers, and proving quality release after release. We build assistants, retrieval pipelines (the pattern usually called RAG), extraction services, chat and automation 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

Answers drawn from your own sources with a citation back to each one, so every answer can be checked and the rate of made-up answers 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

Limits on what the system may output, 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

Answering 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?

Answers are drawn from your content rather than the model's memory, citations make them checkable, and a limit layer catches output that breaks your rules. We measure how often the system makes something up on a fixed test set and gate releases on that number.