Language AI that reads, sorts and finds what matters

A focused classifier or entity extractor on a well-defined corpus typically moves from corpus audit to a production API in six to ten weeks. Semantic search over a large document set, or multilingual pipelines with human review, usually run ten to sixteen weeks. The timeline depends mostly on how much labelled data already exists and how many source systems we integrate with, and we confirm a schedule at the end of the corpus audit.

A representative sample of your text — a few thousand tickets, documents or reviews is usually enough to begin — and one or two subject-matter experts who can spend a couple of hours a week reviewing labels. We also need to know where the text lives, which languages it covers, and any privacy or data-residency rules that apply. If your data is unlabelled, we run the labelling with your experts as reviewers.

We choose per task rather than per vendor. Fine-tuned transformer encoders (BERT-family and multilingual variants) handle most classification and entity extraction; embedding models with vector databases such as pgvector, Qdrant or Pinecone power semantic search; hosted or open-weight large language models cover zero-shot and translation tasks where they win on your benchmarks. We deploy on AWS, Google Cloud, Azure or your own infrastructure, including Canadian data residency where it is required.

Every model is scored against a held-out gold set. For classification and extraction we report precision, recall and F1 per label; for search we report relevance metrics such as recall@k and NDCG from your own staff's judgements; for translation we report quality-estimation scores and human review rates. In production you get a dashboard showing accuracy on reviewed samples, confidence distribution, drift alerts and latency, summarised in a monthly report.

You do. Training data, labels, fine-tuned model weights, evaluation sets and source code are delivered into your own cloud accounts and repositories. Where a third-party hosted model is used, we document the dependency and design the pipeline so it can be swapped out. Your text is never used to train models for other clients.

As one team. We run a shared project board, a weekly review of model results with your subject-matter experts, and a labelling process your staff can continue after launch. Your developers receive documented APIs, evaluation notebooks and a handover session; if you have data scientists, we pair with them from the start so the system stays maintainable in-house.

Every engagement is scoped to your corpus, languages and integration needs, so we quote rather than publish a price list. Request a quote with a short description of your text sources and what you want the system to do, and we reply within one business day with a scoped plan and estimate.