Text arrives faster than people can read it, and the important parts are buried inside long messages and documents.
12 / NLP Development
NLP Development
Turn emails, tickets, contracts and messages into structured, searchable information.
Language processing for classification, extraction, search, summarisation and sentiment.
We read a few hundred of your real documents or messages first, define the labels and fields together, and build the simplest approach that hits the accuracy you need. Often that is a small classifier or an extraction prompt rather than a large model.
What the build includes
A complete production workflow.
Label scheme and annotated sample
A written definition of each category or field, applied to a sample your team has checked.
Text processing service
An API that classifies, extracts or summarises text and returns structured results with confidence scores.
Search over your text
Keyword and meaning-based search across emails, tickets or documents, with filters.
Accuracy dashboard
Precision and recall per label, plus a list of the most common mistakes to fix next.
Where it applies
Typical nlp development work.
- →Sorting incoming email by intent
- →Pulling names, dates and amounts from contracts
- →Summarising long case notes
- →Tracking sentiment in survey comments
How a project runs
Four steps, no surprises.
- 01
Discovery call
A short call about the goal and the first workflow or feature worth building.
- 02
Written scope
What is built, which tools it touches, what a person approves, and the timeline, agreed before work starts.
- 03
Build and review
Short iterations connected to your tools, reviewed with your team before anything ships.
- 04
Launch and handoff
Go-live with documentation and an owner on your side. You own what we build.
Technology and fit
Built with the right tools, not every tool.
The exact stack follows the workflow, security requirements, existing systems, and deployment environment.
Technologies
PythonspaCyHugging FaceOpenAIClaude
Industries
Expected outcomes
- →Text sorted without manual triage
- →Key facts pulled out of long documents
- →Searches that find meaning, not just words
- →Accuracy you can measure per category
- →Fewer repeated reading tasks
Common questions
Before we scope it.
Does it work on languages other than English?
Many models handle major languages well. We test on your own samples in each language and report results separately before promising anything.
Can it handle messy, scanned or informal text?
Yes, with a cleaning step in front, such as OCR for scans. We include noisy examples in the test set so the accuracy numbers reflect reality.
How do we fix mistakes later?
Corrections go into a review queue and become new training or test examples. You can add a label or adjust a field without rebuilding the system.
Keep exploring
Related services and technologies.
Related services
Free Audit
Get my free Audit ↗Know what to build first.
We review the workflow, recommend the first build, and give you a rough price range and timeline before you commit.