Grounded self-service
Answers from your help content with citations, abstaining when the answer is absent.
Resolution, not containment
AI for Customer Support
Support automation that resolves from approved content, assists agents and escalates with context.
What this is
Support automation should be judged on resolution, not containment. We build systems that answer from your approved help content, perform account actions where permitted, assist your agents on the tickets they keep, and escalate with a written summary instead of trapping the customer.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- The same questions consume most of the team capacity every week.
- First response times stretch at peak and collapse outside working hours.
- Agents search several systems to answer one question.
- Recurring issues are never fed back to the teams who could fix them.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- Customer-facing agent resolving defined request types from approved content
- Account actions such as status, bookings and detail updates through your APIs within permissions
- Agent assist inside your helpdesk suggesting the approved answer with its source
- Escalation with transcript, summary and recommended next action
- Ticket triage: classification, prioritisation and routing on arrival
- Reporting on resolution rate, content gaps and recurring root causes
Capabilities
What the system does
Account actions
Order status, rescheduling and detail updates performed under verified identity.
Agent assist
Suggested answers, sources and draft replies inside the existing ticket view.
Triage and routing
Incoming tickets classified by intent and urgency and routed to the right queue.
Escalation with context
Handover that carries everything, so the customer never repeats themselves.
Gap and root-cause reporting
Ranked unanswered questions and recurring issues, routed to content and product owners.
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
Repetitive query resolution
Handles the high-volume question set completely, at any hour.
02
Order and booking self-service
Status, changes and cancellations completed in conversation.
03
Agent productivity
Cuts the search time on every ticket the team continues to handle.
04
After-hours and peak cover
Maintains response quality when the team is unavailable or saturated.
05
Multilingual support
Extends coverage to languages your current team does not cover.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
-
01
Channels
Web chat, WhatsApp, email, in-app and your help centre.
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02
Knowledge layer
Retrieval over help content, policies and product documentation with freshness tracking.
-
03
Action layer
Order, booking and account APIs under scoped credentials and identity checks.
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04
Helpdesk integration
Ticket creation, updates, routing and agent-assist surfaces in your existing tool.
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05
Escalation
Routing with transcript and summary, respecting your priority and ownership rules.
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06
Analytics
Resolution rate, satisfaction signals, content gaps and recurring root causes.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Answers restricted to approved content; policy exceptions escalate to a person.
- Identity verification proportionate to the sensitivity of the requested action.
- Escalation to a human always available on request, with no forced containment.
- Clear disclosure that the customer is interacting with an automated agent.
- Transcript retention and personal-data handling aligned to your privacy policy.
- Accessibility maintained across every customer-facing surface.
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
AI for Customer Support — FAQs
The questions we are asked most often about this service.
How much volume can it actually resolve?
It depends on your ticket mix. Rather than quote a figure, we analyse a sample of your historical tickets and estimate the resolvable share before you commit, then report actual resolution once live.
Will it make customer experience worse?
It can, if it is built to contain rather than resolve. We measure resolution and satisfaction rather than containment, and keep escalation one request away.
Does it work with our helpdesk?
We integrate with Zendesk, Freshdesk, Intercom, Help Scout and custom helpdesks through their APIs, including agent-assist surfaces where supported.
What about questions it cannot answer?
It escalates with full context and logs the gap. The ranked gap report is one of the more useful outputs, since it tells your content team exactly what to write next.
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