Telecom & Communications
Support Automation Intelligence API
Most support tickets are a version of something already answered. The difficulty is that nobody can find the right previous answer fast enough to matter.
- Helpdesk and ticketing platforms
- Telecom and utility customer operations
- SaaS support teams
- BPO operators
- Field service organisations
What your customers would see
example interfaceMockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.
The problem this solves
Support volume concentrates heavily. A few dozen issue shapes account for the large majority of tickets, and the remainder is a genuine long tail that needs a person. Routing and resolution are mostly a retrieval problem — which category, which team, which previous resolution applies — and most help desks solve it with a manual triage queue and institutional memory held by whoever has been there longest.
The costs of getting it wrong compound quietly. A misrouted ticket adds a day. A missing category means reporting cannot show what is actually driving contact volume. An escalation that was predictable from the first message becomes a complaint on day four. None of these appear as a single large number in a budget, which is partly why they persist.
Automation here has an obvious failure mode worth naming early. A classifier that routes confidently and wrongly is worse than no classifier, and a suggested response that is plausible and incorrect will be sent by a tired agent at the end of a shift. The design question is not how much to automate but where the confidence threshold sits and what happens below it.
Who buys this
Helpdesk and ticketing platforms, Telecom and utility customer operations, SaaS support teams, BPO operators, Field service organisations
What the API returns
What comes back
- Ticket classification into your own category taxonomy with confidence
- Routing recommendations to team, queue or skill group
- Suggested responses grounded in your own resolved tickets and knowledge base
- Source citations for every suggestion, so an agent can verify before sending
- Escalation and complaint-risk prediction from the first message onward
- Duplicate and related-ticket detection across channels
- Priority estimation from content, account context and historical outcomes
- Explicit no-confident-answer outcomes rather than a low-quality guess
Endpoints
| Method | Path | Returns |
|---|---|---|
| POST | /v1/tickets/classify | Classify and route an incoming ticket |
| POST | /v1/tickets/suggest | Suggested responses with citations |
| POST | /v1/tickets/escalation-risk | Escalation and complaint risk for a ticket |
| POST | /v1/tickets/duplicates | Related and duplicate tickets |
| POST | /v1/taxonomy | Define or update the category taxonomy |
| POST | /v1/outcome | Report the resolution and whether a suggestion was used |
Request and response
illustrativeRequest
{
"ticket": {
"id": "tkt_4410229",
"channel": "email",
"subject": "Still no service after engineer visit",
"body": "An engineer came on Tuesday and said the line was fixed but we have had no broadband since. This is the fourth time I have contacted you.",
"account_id": "acct_90218"
},
"outputs": ["classification", "routing", "suggestion", "escalation_risk"],
"min_confidence": 0.7
}Response
{
"ticket_id": "tkt_4410229",
"classification": {
"category": "fault/broadband/post_engineer_unresolved",
"confidence": 0.89,
"alternatives": [
{ "category": "fault/broadband/intermittent", "confidence": 0.07 }
]
},
"routing": { "queue": "faults_tier2", "skill": "fixed_line",
"basis": "Repeat contact after engineer visit routes to tier 2 by policy" },
"suggestion": {
"available": true,
"confidence": 0.74,
"draft": "I can see an engineer attended on Tuesday and that the fault remains open. I am escalating this to our tier 2 faults team and arranging a priority revisit.",
"citations": [
{ "source": "resolved_ticket", "id": "tkt_4091882", "similarity": 0.81 },
{ "source": "kb_article", "id": "kb_fault_revisit", "section": "Repeat visits" }
],
"review_required": true
},
"escalation_risk": {
"score": 0.81, "band": "high",
"drivers": ["fourth_contact", "unresolved_after_visit", "service_fully_down"]
},
"duplicates": [
{ "ticket_id": "tkt_4408117", "similarity": 0.93, "status": "open" }
]
}Where the data comes from
| Your resolved ticket history | Past tickets with their resolutions and categories — the most valuable input and one every help desk already has |
| Your knowledge base | Articles and internal process documentation, which is what grounds a suggestion in something verifiable |
| Your category taxonomy | The categories your reporting and routing actually use, including the messy historical ones |
| Account and service context | Products held, open faults and contact history, which changes both priority and the right answer |
| Escalation and complaint records | Confirmed escalations and complaints, which is what escalation prediction is trained against |
| Agent feedback | Whether a suggestion was used, edited or discarded — the signal that keeps the suggestion quality honest |
The intelligence layer
The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.
- Classification trained on your own resolved tickets rather than a generic taxonomy, because support categories are organisation-specific to the point of being untransferable
- Retrieval-grounded suggestions with mandatory citations, so an agent can check the source before sending and an unsupported draft is never produced
- Confidence thresholds with explicit abstention — below threshold the API returns no suggestion rather than a plausible guess
- Escalation prediction trained on confirmed escalations and complaints, validated on held-out periods
- Duplicate detection across channels, since the same customer emails and calls about one problem and creates two tickets
- Taxonomy maintenance support: detection of categories that have drifted, merged in practice, or stopped being used
- Per-category accuracy reporting rather than one headline figure, because a classifier can be excellent overall and useless on the categories that matter
- Monitoring of suggestion acceptance and edit rates by category, which detects quality decay faster than any accuracy metric
What it changes for the business
Triage time
Classification and routing happen on arrival rather than in a manual queue.
Handling time
Grounded drafts with citations shorten the research step without removing the agent's judgement.
Earlier escalation handling
Risk scoring on the first message gives you days of warning instead of a complaint.
Reporting quality
Consistent categorisation across every ticket makes contact-driver reporting actually usable.
How it fits into your product
How it embeds
- Called on ticket creation for classification and routing, with suggestions fetched when an agent opens the ticket
- Suggestions presented as editable drafts inside your agent interface, never auto-sent without a policy decision to do so
- Citations surfaced in the agent view so verification is one click rather than a search
- Outcome endpoint capturing resolution and suggestion usage, which is what keeps the models current
- Deployment inside your own infrastructure where ticket content includes customer data that cannot leave
- Taxonomy versioning so reporting remains comparable when categories change
Included in every build
- Authentication, scoped API keys and per-customer rate limiting
- Input validation with predictable, documented error responses
- Versioned endpoints so your integration never breaks without notice
- Request and response logging for audit and dispute resolution
- Latency, error-rate and usage monitoring with alerting
- OpenAPI specification, sandbox environment and client examples
- Backups, failover design and a documented recovery procedure
- An agreed support arrangement once the service is live
How we would build it
about 12 weeksDiscovery and data review
Weeks 1–2
We agree the exact question the API answers, review the sources you already hold, check what has to be licensed, and write down the response contract your developers will code against.
Architecture and prototype
Weeks 3–4
You get a written architecture plus a working endpoint on real data — narrow, but genuinely callable — so integration can be tested against something concrete rather than a document.
Production build
Weeks 5–10
Pipelines, models, storage, authentication, rate limiting, logging, tests and documentation. Security and observability are part of this phase, not a later one.
Integration and handover
Weeks 11–12
We support your engineers through their integration, run load tests at your expected volume, and agree how the service is monitored and supported once it is live.
Commercial models
There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.
Fixed-scope prototype
A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.
Fixed-price build
An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.
Build then retainer
We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.
White-label product
The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.
Questions buyers ask
Can suggested responses be sent automatically?
For a narrow set of high-confidence, low-risk categories, with your agreement, yes. For most traffic we would advise against it. A grounded suggestion is still wrong sometimes, and a confidently wrong answer sent automatically costs more trust than it saves handling time. The default design puts a draft in front of an agent with its citations attached.
How much ticket history is needed?
For classification, a few thousand resolved tickets per meaningful category. For escalation prediction, enough confirmed escalations to learn from — typically a year of history. Categories with thin history will classify poorly, and we report per-category accuracy so you can see which ones rather than trusting an overall number.
Will it invent answers?
The design is specifically built to prevent that. Suggestions are retrieval-grounded with mandatory citations, and a draft that cannot be supported by a resolved ticket or knowledge base article is withheld rather than generated. An unanswerable ticket should return no suggestion, and the API has an explicit outcome for that.
What happens to tickets it cannot classify?
They go to your existing manual triage with the uncertainty stated, which is the correct outcome. A classifier forced to always answer will route the hard tickets wrongly, and those are usually the ones where a misroute costs the most. Abstention is a feature here, not a gap.
Does it replace support agents?
It removes the retrieval and triage work. The long tail, the angry customer and anything requiring judgement or authority still need a person, and those are the contacts where handling quality actually affects whether the customer stays. Teams that use the time saved on those cases get more out of this than teams that use it to reduce headcount by the same percentage.
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Want this built for your platform?
Tell us what your product does today and what you want another system to be able to ask it. We will come back with an approach, a scope and a price.