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.
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Regional
Delivery considerations for your region
Data and risk discovery (Canada)
Privacy, security, residency, and regulatory requirements differ by workflow. We document the applicable data flows, roles, retention needs, and control owners before recommending an architecture.
The resulting proposal lists the controls and evidence that are actually in scope. It is not a generic compliance, certification, or legal-assurance promise.
- Map data sources, destinations, roles, and sensitive fields
- Record access, retention, logging, and deletion requirements
- Identify required security or procurement evidence before contracting
- Use an NDA or DPA only when the parties mutually execute it
Working model (Canada)
Exact live-overlap hours, response expectations, meeting windows, and escalation contacts are confirmed in the proposal for each engagement.
Written decisions, scoped milestones, and asynchronous updates reduce unnecessary meetings without implying an unagreed service level.
- Proposal-specific overlap and meeting windows
- Named owners for decisions and blockers
- Written scope, assumptions, and change decisions
- Milestone cadence agreed before kickoff
Commercial setup (Canada)
The contracting entity, proposal currency, invoicing cadence, payment terms, intellectual-property terms, and required vendor documents are agreed before work begins.
The Opportunity Sprint can establish the evidence needed to scope a production pilot; it does not pre-commit either party to a rollout.
- Contracting entity and currency confirmed in writing
- Milestones and acceptance criteria defined in the proposal
- Vendor-document requirements identified before signature
- Scope changes require an explicit written decision
Delivery controls (Canada)
Testing, observability, release, security, and handover controls are selected for the actual system risk rather than promised as a generic bundle.
Acceptance measures and production responsibilities are recorded before implementation so both teams know what evidence will support release.
- Risk-based testing and acceptance measures
- Release, rollback, and observability responsibilities
- Security controls tied to the agreed threat model
- Handover artifacts defined in the signed scope
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.