Classification and routing
Messages categorised by intent, urgency and ownership against your taxonomy.
Text at scale
Natural Language Processing (NLP)
Classification, extraction and analysis across the text your business already generates.
What this is
Tickets, emails, reviews, transcripts and notes hold information nobody has time to read. We build pipelines that classify, extract and summarise that text reliably at volume, with structured output your systems and dashboards can actually consume.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- Thousands of tickets and reviews arrive monthly and nobody can read them all.
- Routing depends on someone reading each message and guessing the owner.
- Customer feedback themes are discovered months late, if at all.
- Key details live in free text and never reach a structured field.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- Text classification tuned to your categories, with measured accuracy per class
- Entity and field extraction into structured, validated output
- Sentiment and theme analysis with trends over time
- Summarisation of long threads, transcripts and documents
- Language detection and multilingual handling where needed
- Batch and streaming pipelines with throughput sized to your volume
- Review queue for low-confidence cases
Capabilities
What the system does
Entity and field extraction
Names, amounts, dates, identifiers and references pulled into validated structured fields.
Sentiment and theme analysis
Aggregate trends across feedback channels, with representative examples per theme.
Summarisation
Long threads and transcripts condensed to the decisions, actions and open questions.
Multilingual processing
Detection and handling across the languages your customers write in.
Confidence and review
Low-confidence outputs routed to a person instead of being accepted silently.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
Support ticket triage
Classifies and routes incoming tickets by intent and urgency on arrival.
02
Voice of customer analysis
Aggregates reviews, surveys and tickets into ranked themes with evidence.
03
Email and enquiry routing
Reads inbound mail and routes it to the right team with extracted details attached.
04
Meeting and call summarisation
Turns transcripts into decisions, owners and action items.
05
Compliance text screening
Flags communications containing defined risk patterns for review.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
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01
Text sources
Ticket systems, email, review platforms, call transcripts, forms and free-text database fields.
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02
Preprocessing
Normalisation, language detection, deduplication and personal-data handling.
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03
Processing layer
Classification, extraction and summarisation with schema-validated output.
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04
Confidence routing
Thresholds sending uncertain cases to a human review queue.
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05
Outputs
Structured records written to your systems, plus aggregates for reporting.
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06
Evaluation
Per-class accuracy against a labelled set, with ongoing sampling in production.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Personal data minimised or redacted before processing where the task does not require it.
- Per-class accuracy reported separately, so rare but important categories are not hidden by averages.
- Confidence thresholds routing uncertain cases to human review rather than silent acceptance.
- Retention limits on processed text, aligned to your privacy policy.
- Sampling in production to detect accuracy decay as language and topics shift.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
Digital sales & support agent
SIZH Virtual Agent
A virtual digital agent for sales, support and reception — trained on your business, live on WhatsApp, web, voice and email.
View product Mobility platformRideOn
A ride-hailing and fleet management platform, now with agentic dispatch, driver support and demand intelligence.
View product Multivendor marketplaceUniBooker
A configurable multivendor service-booking marketplace with agentic onboarding, matching and support.
View product
Questions
Natural Language Processing (NLP) — FAQs
The questions we are asked most often about this service.
Do you need labelled training data?
For measurement, yes: a few hundred labelled examples per category to validate accuracy. Modern models often classify well without task-specific training, but we cannot report accuracy without a labelled evaluation set.
How accurate is classification?
It varies by how distinct your categories are. Overlapping or ambiguous categories cap accuracy regardless of model, and part of our work is often tightening the taxonomy itself.
Can it handle our industry terminology?
Yes. Domain vocabulary, abbreviations and product names are supplied as context, and we test specifically on examples containing them.
What about low-confidence results?
They go to a review queue rather than into your systems. The threshold is yours to set based on the cost of an error.
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