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Python
Anthropic
PyTorch
PostgreSQL
AWSWhat NLP can do with your text
Natural language processing (NLP) lets software understand written language. Businesses generate huge amounts of text — support tickets, emails, reviews, chat logs, contracts, call transcripts — and most of it is read once by a person and then forgotten. NLP turns that text into structured data and actions: a ticket is categorised and routed, a contract clause is extracted and flagged, a thousand reviews become a clear list of top complaints.
Large language models have made NLP far more accessible: many tasks that once needed months of model training can now be solved with well-designed prompts and a few examples. For high volumes or strict accuracy needs, smaller fine-tuned models remain faster and cheaper, and we choose between the two for each task.
Problems we solve
- Support tickets triaged and routed by hand
- Important details buried in long emails and documents
- No systematic view of what customers are saying in reviews and surveys
- Compliance teams reading contracts line by line
- Search that fails unless users type the exact keyword
What’s included
Classification & routing
Categorise tickets, emails and documents and send them to the right team.
Entity & data extraction
Pull names, dates, amounts, clauses and product codes into structured fields.
Sentiment & topic analysis
Understand trends in reviews, surveys and social mentions.
Summarisation
Short summaries of calls, threads and long documents.
Semantic search
Search by meaning across knowledge bases and archives.
Translation & multilingual support
Process text in many languages with consistent outputs.
How we build NLP solutions
We begin with a labelled sample of your real text and agree what correct output looks like. We then compare approaches — prompted LLMs, fine-tuned smaller models and rule-based methods — on accuracy, speed and cost. The winning approach is integrated into your help desk, CRM or document workflow, with confidence scores so uncertain cases go to a person.
- Evaluation sets built from your data, not generic benchmarks
- Personal data masked before processing where required
- Human-in-the-loop review for low-confidence outputs
- Monitoring of accuracy, volume and cost after launch
LLMs or classic NLP models?
| Approach | Strengths | Watch out for |
|---|---|---|
| Prompted LLM | Fast to build, handles nuance and many tasks | Per-request cost at high volume |
| Fine-tuned small model | Fast, cheap per request, private | Needs labelled training data |
| Rules & patterns | Predictable, transparent | Brittle with varied language |
Many production systems combine all three: rules for clear-cut cases, a small model for the bulk and an LLM for the hardest examples.
Common NLP use cases and how they are built
| Use case | Typical approach |
|---|---|
| Ticket and email routing | Text classification, often a fine-tuned small model or LLM with examples |
| Extracting fields from documents | Named entity recognition or LLM extraction with schema validation |
| Sentiment and topic analysis of reviews | Classification plus clustering, summarised in dashboards |
| Search across documents | Semantic search with embeddings, combined with keyword search |
| Summarisation | LLMs with length and format controls and source references |
Measuring quality
Language tasks need careful evaluation because “sounds right” is not the same as correct. We build a labelled evaluation set from your real text, measure precision and recall for classification and extraction, and use human review rubrics for summaries and generated text. Results are broken down by category, language and document type so weaknesses are visible rather than hidden in an average.
- Agree acceptable error rates with the people who will rely on the output
- Route low-confidence predictions to human review
- Re-run the evaluation set on every model or prompt change
Handling sensitive text
Emails, chats and contracts often contain personal or confidential information. We can redact personal data before processing, choose providers and regions that meet your compliance needs, disable training on your data with API providers, and keep logs free of raw content where possible.
Extracting structured data from documents
Many organisations receive information as free text — emails, contracts, claims, reports, résumés. NLP can turn that text into structured records: names, dates, amounts, clauses, categories and relationships. Modern language models make extraction far more flexible than older template-based tools, especially when documents vary in layout and wording.
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Define the schema
List the fields to extract, their formats and validation rules.
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Prepare examples
Collect and label representative documents, including difficult ones.
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Extract and validate
Use models to fill the schema, then check values with code — formats, totals, cross-references.
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Review exceptions
Low-confidence fields go to a reviewer; corrections improve the system.
Search that understands meaning
Keyword search fails when users describe things differently from how documents are written. Semantic search uses embeddings to find text with similar meaning, and hybrid search combines it with traditional keyword matching for the best of both. We add filters, permissions and ranking tuned on real queries, so employees or customers find the right answer quickly.
| Search type | Strength | Weakness |
|---|---|---|
| Keyword | Exact terms, codes and names | Misses synonyms and paraphrasing |
| Semantic | Understands meaning and intent | Can miss exact identifiers |
| Hybrid | Combines both; usually the best default | Slightly more engineering to tune |
Choosing between hosted and self-hosted models
Hosted models from major providers offer the strongest general capability with no infrastructure to manage, and enterprise agreements typically exclude your data from training. Self-hosted open models give more control over data location and cost at very high volume, but require GPU infrastructure and more engineering. Smaller task-specific models can be cheap and fast for classification or entity extraction. We compare options on your data, accuracy needs and budget rather than defaulting to one.
- Data residency and privacy requirements
- Expected volume and cost per request
- Latency requirements
- Languages and domain vocabulary
- In-house skills to operate the chosen option
Rolling out NLP safely
- Start with assistive use — suggestions people accept or edit — before full automation
- Monitor accuracy on a weekly sample of real items
- Keep humans responsible for high-impact decisions
- Track drift as language, products and policies change
Related: RAG development, generative AI and AI agents.
Typical NLP project scope
| Scope | Typical timeline |
|---|---|
| Classification or routing for one text source | 4–8 weeks |
| Document extraction for a few document types | 6–12 weeks |
| Semantic search across a knowledge base | 6–10 weeks |
| Multi-language platform with several NLP features | 3–6 months |
Typical ranges; the written estimate follows a review of your data.
Most projects begin with a short data review to confirm feasibility and agree on target accuracy before the main build starts.
Questions to ask before starting
- Which text sources matter most, and how much volume arrives each day?
- Which mistakes are costly, and which are harmless?
- Who will review uncertain results, and in which tool?
- Which languages and specialist vocabulary must be supported?
These answers shape the model choice, the review process and the budget.
We can start with a short feasibility review on a sample of your own text, so you see realistic accuracy before committing to a full build.
Related services
NLP is part of our AI services. For assistants over your documents, see RAG development; for workflows that act on text, see AI agents.
This page is part of AI Development & Data Engineering Services — see the full overview for related services, approach and pricing.