NLP development services: search, classification, extraction, and language intelligence grounded in your own data, not generic training knowledge.
Fluent is not the same as correct
Off-the-shelf NLP demos look impressive and then fall apart on your real data: domain jargon it's never seen, document formats it can't parse, edge cases nobody tested. The gap between a language model that sounds right and a system that is right is almost entirely data engineering which is where most NLP projects actually fail.
What we build
- Semantic search and retrieval over your documents, tickets, or knowledge base
- Text classification and routing (support tickets, documents, compliance flags)
- Entity and field extraction from unstructured text and scanned documents
- Summarization tuned to the format your team actually reads
- Evaluation sets built from your real data, so accuracy is measured, not assumed
How we work
Start from real examples of the text you need handled, not a generic benchmark
Ground retrieval and extraction in your own indexed data via RAG
Build an evaluation set before writing the pipeline, so you know when it works
Ship a narrow, monitored slice first; expand once it holds up in production
Typical stack
Frequently asked questions
It is building software that reads, classifies, extracts, or searches text as part of a real workflow routing a support ticket, pulling fields from a contract, answering a question from a knowledge base. The model is one component; the data pipeline feeding it clean, relevant text is usually the bigger engineering problem.
Most NLP problems are solved faster and more reliably with retrieval-augmented generation over a strong general model than with fine-tuning. We fine-tune when a narrow, high-volume task justifies the ongoing cost of maintaining a custom model see our LLM fine-tuning page for when that trade-off makes sense.
We build an evaluation set from real examples before writing the pipeline inputs paired with the correct output, graded automatically where possible. That number, not a demo, is what decides whether a system is ready for production.