Natural Language Processing Services
Turn text and speech into structured, searchable data.
Natural language processing services build software that reads and understands human language, such as emails, reviews, contracts and call transcripts. NetOfficials builds NLP systems that classify text, pull out key details, analyse sentiment and power search, so your team stops reading it all by hand. Text generation is covered by our generative AI development services.
- Building software since
- 2011
- Approved before the build
- Written scope
- Tracked in Jira
- Sprint demos
- Confirmed in the contract
- Your IP
What Is Included in Our Natural Language Processing Services
NLP is about understanding existing text. The deliverable is usually structured data your systems can act on.
Text Classification
Sort emails, tickets and documents by topic, urgency or intent.
Information Extraction
Pull names, dates, amounts and clauses out of contracts, forms and reports.
Sentiment and Topic Analysis
Find what customers are saying across reviews, surveys and support threads.
Semantic Search
Find documents by meaning, so a search for a problem finds the right answer.
Speech to Text
Turn calls and recordings into text that can be searched and analysed.
Language Handling
Support for several languages and for the way your industry writes.
Who Our Natural Language Processing Services Suit
NLP helps wherever a team reads large volumes of text to find or route something.
-
Support and operations teams
Route and prioritise text automatically
Sort incoming emails and tickets and send each to the right place.
- Topic and urgency labels
- Routing into your helpdesk
- Review of uncertain cases
-
Legal and finance teams
Extract facts from documents
Capture the key terms from contracts, invoices and filings without retyping them.
- Field and clause extraction
- Human check on low confidence
- Data sent to your systems
-
Research and insight teams
Understand feedback at scale
See themes and sentiment across thousands of comments and reviews.
- Topic grouping
- Trends over time
- Dashboards for your team
A Task Specific Model or a Large Language Model?
For understanding text, you can train a small model for one task, or prompt a large language model. Each has a place. We test both on a sample of your text before recommending one.
A task specific model is usually the better choice when
- You process very large volumes of text
- The task is narrow, such as one fixed set of labels
- Each document must be handled cheaply and quickly
- You hold many labelled examples already
| Consideration | Task specific model | Large language model |
|---|---|---|
| Cost per document | Low at high volume | Higher at high volume |
| Setup | Needs labelled examples | Works from instructions |
| Speed | Fast | Slower |
| Flexibility | One task | Many tasks and new labels |
| Consistency | Same result for same input | Can vary |
| Best for | High volume, fixed labels | Varied text and fast experiments |
How We Deliver Your NLP Solution
Every project moves from a written scope to agreed review points, so you always know what is happening and what comes next.
Review Your Text
Look at real samples, the languages involved and what the output must feed, in a scope you approve.
Signed scopeLabel a Sample
Mark up a sample of text so we can train and measure against your own judgement.
Labelled sampleBuild and Compare
Try task specific models and language models, and compare results on your text.
Chosen approachConnect to Your Systems
Send results into your helpdesk, database or search, with a demo each sprint.
Working pipelineReview and Improve
Check the uncertain cases, feed corrections back and improve over time.
Better each cycle
What Affects the Cost of Natural Language Processing
We do not quote from a price list, because text varies so much between businesses. Cost depends on the factors below, each of which we check against your real text in discovery.
Languages
Each extra language adds data, testing and sometimes a different model.
Labelling effort
Custom categories or fields need examples marked up by someone who knows the subject.
Text quality and format
Scanned pages, noisy chats and mixed layouts need cleaning before they can be read.
Domain vocabulary
Specialist terms and abbreviations take more tuning than everyday language.
Volume and speed
Large volumes or real time results change the design and the running cost.
Integration
Where the results must land and who reviews them shapes the build.
Why Choose NetOfficials for Natural Language Processing
The working terms below are part of every engagement, not extras.
One Cross-Functional Team
Design, development, testing and DevOps run through a single team, so decisions stay connected.
Senior From Day One
The senior engineer who scopes your work stays involved while it is delivered.
You Own Everything
IP assignment is confirmed in the contract, and you receive the full source code and documentation at handover.
Mutual NDA
We sign a mutual NDA before you share the details of your project.
Your Working Hours
We agree a communication window that suits your time zone at the start.
Visible Progress
Work is tracked in Jira, with a demo and your feedback at the end of every sprint.
How We Choose the Right Stack for NLP
We recommend technology for your project and your team's future, not for our convenience. Three questions guide every choice.
Will it handle your load?
We size the design for your expected users, data and growth, so it does not need rebuilding when the business grows.
Will it fit your systems?
We check how the stack connects with the tools, databases and partners you already rely on before we commit to it.
Can you support it later?
We favour well-supported technology with a wide talent pool, so your own team or any future partner can maintain it.
Technologies we work with
Python
Terraform
TensorFlow
PostgreSQL
MongoDB
GraphQL
Node.js
Java
.NET
Go
AWS
Azure
Google Cloud
DockerFlexible Ways to Work With Us
Pick the model that fits where your project is today. You can move between them as it grows.
Project Delivery
We deliver an agreed scope, timeline and price.
- Written discovery scope
- Milestone reviews
- Best for a clear first release
- For ongoing work
Dedicated Team
A team that works only on your project, led by a named delivery lead.
- Cross-functional team
- Sprint demos and planning in Jira
- Scale the team up or down
Team Extension
Specialists who join your existing team, tools and processes.
- Works in your process and tools
- Agreed overlap hours
- Best for adding a missing skill
More From Our AI Development Practice
This service is part of our ai development practice. These pages cover the services most often needed alongside it.
Natural Language Processing FAQs
What is natural language processing?
Natural language processing is the field of getting software to read and work with human language. It covers tasks such as classifying text, extracting details and analysing sentiment.
How is NLP different from a chatbot?
A chatbot holds a conversation. NLP is the wider set of skills for understanding text, and a chatbot is one product that can use them.
Can NLP read scanned documents?
Yes, after the pages are turned into text with optical character recognition. Quality of the scan affects accuracy, so we check samples in discovery.
Does it work in languages other than English?
Yes, though quality varies by language. We test your languages on real samples and report how well each one performs.
Do we need to label lots of data?
Usually a sample, not everything. We agree how much in discovery, and a large language model can reduce what is needed.
Who owns the NLP system?
You do. IP assignment is written into the contract before development starts, and the source code and documentation are handed over to you in full.
Ready to Put Your Text to Work?
Share a few details. A senior engineer reads every brief and replies with questions and a suggested next step.
- We review your briefAnd reply with questions
- We sign a mutual NDABefore you share the details
- You get a written scopeTeam, timeline and model, before any commitment