NLP services put the text your business never reads to work

Every business past a certain size runs on text nobody reads. Support tickets sit in a shared inbox until someone triages them by hand. PDFs — invoices, intake forms, signed agreements — get retyped into a spreadsheet, field by field. Lead emails are answered in the order they arrived, not the order they matter. The information is all there. It's just locked in prose.

NLP is how you put it to work: pipelines that read that text — extracting the fields, routing the requests, flagging the customer who's about to leave — with an audit trail.

What NLP means in our hands

Three things that shape how we work:

Pipelines, not demos. Anyone can make a model summarise one document in a meeting. The hard part is thousands of documents a month at a known, measured accuracy. Before we write a line of pipeline code, we build a labelled evaluation set with your team — real examples, agreed correct answers — so "it works" is a number, not a vibe.

Human-in-the-loop by default. No classifier is right 100% of the time, and we won't pretend ours is. Every pipeline ships with confidence thresholds and a review queue: high-confidence items flow straight through, low-confidence items land in front of a person inside your admin. You set the threshold; the system earns autonomy as the numbers prove out.

It lands inside the systems you already run. Not another SaaS tab with another login. We build on the same stack as our webapps: outputs arrive as structured rows in your database, review queues live in your admin, routing goes to the inbox or CRM your team already works in.

Stack and approach

• Postgres + pgvector for embeddings and semantic search — no separate vector database to operate
• Hosted frontier models where the task demands them; smaller, cheaper models where the eval numbers say they're enough
• Structured outputs validated against a schema — a pipeline that returns malformed data retries, then escalates; it never silently writes garbage
• TypeScript end-to-end, running in the same Next.js + Vercel infrastructure as the rest of your site
• Multilingual by design — embeddings and models chosen for the languages your customers actually write in, tested per language rather than assumed

What this looks like in practice

Two typical engagement shapes:

Document understanding. A business receives a steady stream of PDFs — supplier invoices, client intake forms, contracts. A pipeline reads each one, extracts the agreed fields into structured records, and files anything below the confidence threshold into a review queue. The team stops retyping and starts verifying, which is a much faster job.

Inbox triage and routing. A shared support or sales inbox where everything arrives flat. A classifier tags each message by type and urgency, sentiment analysis flags the messages that read like a relationship at risk, and routing rules deliver each one to the right person — with a weekly digest summarising what came in and what's trending.

Pricing

NLP engagements are scoped per project. The honest price depends on how varied your documents are, how many languages you handle, what accuracy target the process needs, and how deep the integration goes — a number quoted before discovery would be a guess, and we don't guess. Contact us and we'll scope it properly: a 30-minute call, then a fixed-price proposal.