From transformer-grade language models to retrieval-augmented chatbots that cite their sources, production NLP and Gen AI systems built for the regulated industries where "the model said so" isn't an answer.
The hard part of NLP and Generative AI isn't getting a model to produce fluent text, that's free. The hard part is getting it to produce accurate, grounded, auditable text in a domain where being confidently wrong has consequences. Healthcare, finance, defense, and the public sector can't ship "vibes-grade" chatbots, and we don't build them.
Our practice covers the full spectrum: retrieval-augmented generation (RAG) for grounded answers with citations, fine-tuning open and proprietary models on your domain data, conversational agents that hand off to humans gracefully, document understanding for contracts, claims, and clinical notes, and classification, sentiment, and translation systems that scale from kilobytes to petabytes of text.
Every Gen AI system we ship has an evaluation harness, a prompt-injection defence layer, an audit trail for every output, and a cost & latency budget that holds at production traffic. The model is the easy part; the engineering around it is where the value lives.
A complete language-AI stack, from token to deployed agent, with grounding and evaluation built into every layer.
Grounded LLM answers backed by your documents, runbooks, contracts, and knowledge bases. Vector retrieval, hybrid keyword + semantic ranking, re-ranking with cross-encoders, and a citation layer that points back to the source on every output. Hallucinations stop being a feature.
LoRA, QLoRA, and full-parameter fine-tuning on your domain corpora, medical, legal, financial, technical. We curate the training data, run the experiments, evaluate against held-out tasks, and ship a model that speaks your domain better than a generalist ever could.
Multi-turn agents with tool use, structured output, memory, fallback to human, and an SLA on accuracy. Built on Anthropic, OpenAI, or open-weight backends, with a routing layer that picks the right model for the right task at the right cost.
Extracting structured data from unstructured documents at scale, named entities, key clauses, line items, dosages, addresses, dates. Hybrid pipelines that combine traditional NLP, layout-aware models, and LLMs only where they add value.
Topic modelling, sentiment, intent classification, content moderation. From transformer-encoder fine-tunes for high-throughput classification to LLM-based zero-shot pipelines for cold-start tasks, chosen by what fits the data, the volume, and the budget.
Translation, multilingual classification, cross-lingual retrieval. Especially useful when your customers, regulators, or operators don't all read the same language, with quality controls that catch the kind of mistakes a confident bilingual reviewer would.
Three representative engagements, what was hand-cranked before, and what the language-AI system replaced it with.
A state agency was triaging thousands of inbound requests per day across benefits, permits, and complaints, with a backlog and an audit obligation. We built a RAG pipeline that drafts responses against the relevant policy documents, cites the source on every output, and routes to a human reviewer with full context attached.
A clinical AI team wanted LLMs to summarise patient context for admitting physicians without leaking PHI or hallucinating diagnoses. We built a retrieval pipeline against EHR notes with strict access control, an evaluation harness for medical accuracy, and an audit trail that satisfied legal review.
A carrier was paying staff to extract coverage limits, dates, and named entities from claim PDFs at high volume. We built a layout-aware extraction pipeline that handles tables, signatures, and multi-page forms, flagging low-confidence extractions for human review and routing the rest straight through.
Most LLM projects die in pilot for the same handful of reasons: ungrounded outputs, no eval, no defence against prompt injection, runaway cost. These five rules keep our work on the right side of that line.
Tell us the language task you'd like to take off your team's plate, and the standard you can't compromise on. We'll come back with a one-page architecture: model, retrieval, eval, cost, and the path to production.