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.
The practice covers retrieval augmented generation (RAG) for grounded answers with citations, fine tuning of open and proprietary models on domain data, conversational agents that hand off to humans gracefully, document understanding for contracts, claims, and clinical notes, and classification, sentiment, and translation systems at petabyte scale.
Every Gen AI system ships with an evaluation harness, a prompt injection defense layer, an audit trail for every output, and a cost and 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, reranking 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.
Multiturn 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 modeling, 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 summarize 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 multipage 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 defense against prompt injection, runaway cost. These five rules keep our work on the right side of that line.
We don't ship vibes grade chatbots. Production Gen AI from us means retrieval augmented generation with citations on every claim, an evaluation harness with adversarial test cases, prompt injection defenses, and an audit trail of prompt to retrieved context to output for every interaction. If the model doesn't know, it says so.
Both, depending on the problem. For most enterprise tasks (document understanding, summarization, Q&A over your data), a tuned retrieval pipeline over Claude, GPT-4, or an open model wins on cost and latency. For specialized domains with proprietary terminology, a fine tuned smaller model can be cheaper to run and more accurate.
For sensitive data we deploy open models (Llama, Mistral, Qwen) inside your VPC so nothing leaves the boundary. For nonsensitive data we use Anthropic and OpenAI under their zero retention enterprise terms. We never use vendor APIs that train on your inputs.
A reproducible test suite that runs the model against curated examples, including adversarial ones, and reports accuracy, hallucination rate, latency, and cost. Every Gen AI system we ship gets one before launch and it runs on a schedule afterwards.
We benchmark on your actual workload: a sample of representative queries, the same evaluation harness, real latency and cost numbers. The right answer depends on volume, sensitivity of data, and latency budget. We present the tradeoffs in a one page comparison, not a vendor preference.
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.