AI Development
NLP / Text Analytics Development
We build NLP and text analytics features that turn messy text into structured signals—classification, extraction, summarisation, and search—integrated into your product with measurable quality.
Overview
What this service is
This service designs a text analytics workflow: input sources, taxonomy, model approach (LLM or classic), and an implementation that handles real-world text variability.
We implement pipelines for batch or real-time processing, integrate outputs into your systems, and build evaluation so quality is visible and improvable.
You get a maintainable setup with monitoring and guidance on improving prompts/models and expanding coverage over time.
Benefits
What you get
Automate manual triage work
Classify and route tickets, requests, or messages without human bottlenecks.
Extract structured fields reliably
Turn free-text into JSON fields your systems can use for workflows and reporting.
Better search and discovery
Improve search relevance and content grouping with embeddings and text signals.
Quality you can measure
Evaluation harness so improvements are tracked instead of subjective.
Flexible model strategy
Choose LLM or lighter models based on cost, latency, and accuracy needs.
Production-ready integration
Pipelines and APIs designed to run reliably in real workloads.
Features
What we deliver
Taxonomy + labeling strategy
Define categories, entities, and fields—and plan data labeling or prompt strategy.
Classification and routing
Models or prompt workflows for classifying text into meaningful operational categories.
Entity and field extraction
Extract structured fields with validation and confidence handling for production use.
Summarisation and insights
Generate concise summaries, key points, and action items aligned to your team’s needs.
Embedding-based search (optional)
Semantic search and clustering for better discovery and grouping of unstructured content.
Evaluation + monitoring
Quality tracking, drift checks, and monitoring hooks so the system improves over time.
Process
How we work
Discovery
We review sample text, define outcomes, and choose an approach based on constraints and accuracy needs.
Design
We define taxonomy, extraction fields, and evaluation approach before building pipelines.
Build
We implement classification/extraction pipelines and integrate outputs into your systems.
Evaluation
We test against representative cases, measure quality, and iterate until results are stable.
Launch + Handoff
We ship monitoring and documentation so the system can improve and expand over time.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
Support ticket classification
Auto-label and route tickets to the right queue with summaries for faster response.
CRM enrichment from emails
Extract contacts, intents, and key details from inbound communication into structured CRM fields.
Compliance text processing
Flag policy violations and extract evidence from documents and conversations.
Document summarisation workflows
Summarise contracts, proposals, and reports into structured brief outputs.
Semantic search for knowledge bases
Improve discovery of relevant docs and topics with embedding-based search patterns.
FAQ
Frequently asked questions
It depends on accuracy, latency, and cost constraints. We often start with LLM-based extraction and evaluate whether lighter models can meet requirements at scale.
Yes. We can scope privacy constraints and implement access control and data handling rules aligned to your requirements.
Yes. We build an evaluation harness so quality is measurable and improvements are tracked.
Yes. We can support real-time APIs or batch pipelines depending on volume and latency needs.
Yes. We integrate results into CRMs, ticketing, dashboards, or data stores using APIs and webhooks.
Related Services
You might also need
Need insights from text at scale?
Share sample data and outcomes you want (labels, fields, summaries). We’ll propose an NLP approach and delivery plan.
Evaluation + integration guidance included.