AI/ML
Production NLP systems that extract structured data, classify documents, and surface insights from unstructured text at enterprise scale.
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What it is
Natural language processing (NLP) encompasses the techniques used to extract structured meaning from unstructured text — including classification, entity recognition, summarisation, semantic similarity, and information extraction — at scale.
What you get
Most enterprise data is locked in unstructured form — contracts, emails, support tickets, clinical notes, research papers. NLP is the set of techniques that extract structured, queryable information from that text, enabling downstream automation, analytics, and search.
We build custom NLP pipelines using transformer-based models from Hugging Face, spaCy, and fine-tuned BERT variants. The choice between a general-purpose model and a domain-specific one depends on your vocabulary, accuracy requirements, and the volume of labelled examples available — decisions we make during technical discovery.
Typical systems we deliver: contract analysis engines that extract clauses and obligations, customer feedback classifiers that route tickets and surface trends, document intelligence pipelines that process PDFs at scale, and semantic search systems that retrieve by meaning rather than keyword.
Key capabilities
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Text summarisation at scale
Information extraction from contracts and reports
Multi-language support with translation pipelines
Fine-tuned transformer models on domain-specific data
Streaming NLP pipelines for high-throughput ingestion
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
When you need high throughput, low cost, and deterministic output on a specific task — classification, entity extraction, summarisation — a fine-tuned NLP model is faster and cheaper than an LLM call. LLMs excel at open-ended reasoning; NLP models excel at structured extraction at scale.
For classification, 500–2,000 labelled examples per class are often sufficient. For NER on a custom domain, you may need 5,000–10,000 annotated sentences. We advise on minimum viable training sets during scoping and can accelerate annotation with active learning pipelines.
Yes — multilingual transformer models like XLM-RoBERTa support 100+ languages with a single model. For higher accuracy on specific language pairs, we fine-tune language-specific models. Your knowledge base or training data should be in the target language for best results.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
