AI Customer Service & Resolution
- Claim submittedReceived and logged
- Documents verifiedProof of purchase accepted
- AssessmentIN PROGRESSAssessor visit scheduled
- Replacement dispatchedOnce the assessment clears
What actually happens when you do?
Under the Hood
Configure a scenario and run it. The same process graph every scenario shares executes step by step — solid paths taken, dashed paths available but not walked — with the reason behind every decision one click away.
Choose a scenario to begin. Configure it below, then run it — nothing executes until you do.
Why is it built this way?
Architect’s Desk
Five questions a technical evaluator asks first, each with the alternative we considered and the trade-off we accepted by choosing it instead.
The system never sends anything to a customer without a human clicking approve.
A draft is cheap to get wrong; a sent message is not. Routing every outbound action through a person keeps the cost of a mistake bounded to a draft, never a message the customer has already read.
Alternative consideredAuto-send whenever the system’s confidence is high enough.
Trade-off acceptedSlower time-to-resolution on the cases that were already safe to automate, in exchange for zero unsupervised customer-facing actions.
When the system is not sure enough to act, it stops and hands the case to a person instead of guessing.
A wrong guess costs more to unwind than an escalation costs to route. Confidence is judged in named tiers, not a percentage, so “not sure” is a defined state rather than a threshold someone tuned once and forgot.
Alternative consideredAlways produce a best-effort answer and let the customer correct it if it is wrong.
Trade-off acceptedMore cases reach a human than a fully-automated pitch would suggest, in exchange for never presenting a guess as a fact.
Identity verification is judged as its own signal, independently of what the customer is asking for.
Knowing who someone is and knowing what they want are different questions with different failure modes. Collapsing them means a well-phrased request could stand in for proof of identity.
Alternative consideredInfer identity confidence from how plausible or well-formed the request sounds.
Trade-off acceptedSome genuine requests wait on a verification step that can feel redundant, in exchange for intent never being able to substitute for identity.
When a system this process depends on is unavailable, the run stops at that step and says so — it does not proceed on stale or assumed data.
Proceeding without the data a decision actually depends on turns a temporary outage into a wrong decision that looks like a normal one.
Alternative consideredFall back to a cached or assumed value and keep the run moving.
Trade-off acceptedA dependency outage becomes a visible pause instead of an invisible one, in exchange for never acting on data the system does not actually have.
Every step of a run is recorded, not only its final outcome.
An outcome-only log can show a case closed correctly while hiding that it took an unsafe path to get there. A step-by-step record is what makes a run reviewable after the fact, not just trustworthy in the moment.
Alternative consideredLog the final decision and result only, to keep the trail short.
Trade-off acceptedA longer audit trail to store and review, in exchange for every decision point being independently checkable.
Architecture
Six layers describe the reference pattern this kind of system is built from. What a specific engagement actually builds — buy, build, or something between — is decided with the client, layer by layer.
- 01
Intent & Language Understanding
Reads what the customer actually asked for and classifies it against the set of requests this process can handle.
- language model
- intent classification
- entity extraction
- 02
Identity & Confidence Layer
Judges how sure the system can be about who it is talking to — a signal kept independent of what they are asking for.
- identity verification
- confidence scoring
- session context
- 03
Policy Engine
Checks the request against the rules that actually govern it before any action is proposed.
- rules engine
- policy lookup
- business logic
- 04
Integration & Orchestration Layer
Coordinates calls out to the systems of record this process depends on and sequences the steps of a run.
- workflow orchestration
- API gateway
- message queue
- 05
Audit & Event Store
Records every step of a run, not only its outcome, as a permanent and independently reviewable trail.
- event log
- append-only store
- audit trail
- 06
Human Approval UI
The interface a person uses to review a drafted action and approve, edit, or decline it before anything reaches the customer.
- review queue
- approval workflow
- notification
A request enters through the intent layer, is checked against identity and policy at the same time, is drafted or escalated by the orchestration layer, and every step — including the human decision — is written to the audit store before the run closes.
Security
Four controls this architecture puts in place around customer data and AI decisions, each stated as the risk it addresses and the control that addresses it.
- Threat
Someone without a legitimate reason to see a customer's personal data seeing it anyway.
MitigationSee it run in Under the HoodAccess to customer PII is scoped by role — a person or system can only read the fields their job actually requires.
- Threat
An AI decision or a human approval that cannot be reconstructed after the fact.
MitigationEvery AI decision and every human approval is written to an audit log that cannot be edited or deleted after the fact.
- Threat
The system taking an action a customer can see without a person reviewing it first.
MitigationNo output reaches a customer without passing through the human-approval gate — this is structural, enforced by the process itself, not a setting that can be turned off.
- Threat
More of a customer's data reaching the reasoning layer than the decision in front of it actually needs.
MitigationOnly the fields a given step needs are passed into the reasoning layer, minimising what any single AI call ever sees of a customer record.
This demo runs entirely in your browser: there is no backend and no database behind it, so what you're seeing illustrates the production posture rather than enforcing it live. MindPolar builds to GDPR-by-design, and does not claim a compliance certification it has not earned.
Case Study
Illustrative example. Anchorpoint Mutual is not a real client — every figure below is constructed to show the shape of an outcome, not to report one.
Challenge
Anchorpoint Mutual's contact centre handled every policy question and claim update through the same queue, regardless of how simple or how sensitive the request was. Straightforward status checks waited behind identity-sensitive claims, and every reply — simple or not — required a person to open the policy record by hand before answering.
Approach
Incoming messages were routed through an intent and identity check first, so straightforward, low-risk requests could be drafted immediately, while anything touching a live claim or a policy change was held for a person to approve before it reached the customer. Nothing was sent automatically; every draft still needed a human decision.
Outcome
In the illustrative quarter after rollout, deflection of routine status questions to same-session resolution rose from 31% to 58%, median first-reply time fell from six hours to four minutes, and CSAT on resolved conversations moved from 3.6 to 4.1 — while every claim-adjustment and refund decision above policy thresholds still required a human approval before anything reached the customer.
FAQ
The questions a skeptical buyer asks before trusting an AI with their customers.
Nothing reaches a customer without a person seeing it first. Every outbound response is drafted, not sent — a person reviews and approves it (see Experience above). When the system is not confident enough to act, or a request falls outside policy, it stops itself and hands the case to a person instead of guessing. Every step of a run is written to the audit trail, so a wrong turn is visible after the fact, not hidden.
Yes, at two points you can see directly on this page. A drafted response can be declined instead of approved, which routes the case to a person instead of sending it. And any run that is not confident enough, or falls outside policy, stops itself and hands off before anything is sent — see Under the Hood for how that decision gets made. Nothing here reaches a customer without a person having the chance to stop it first.
This demo shows the resolution process itself, not deployment controls. Whether and how a system like this gets paused, staged, or disabled is a question about how it is wired into your own support stack — not one a browser demo can answer on your behalf.
In this demo, nowhere — every customer, claim, and message on this page is authored and simulated, and nothing you do here is sent anywhere or stored past your browser session. In a real deployment, that answer depends entirely on the systems you connect it to; that is a question for your own architecture review, not one this preview can answer honestly on your behalf.
No. Every customer, claim, message, and outcome on this page is authored for this demonstration — see the disclosure in the bar above. Nothing here is a recording, and nothing here is a real customer.
No number here would be honest. Cost depends on your existing systems, your volume, and how much of the process you automate versus keep human-reviewed. See Cost Estimate above for the shape of what drives it, and Blueprint below for a directional preview built from your own answers.
If the question you came with is not here, it is a better question than the ones that are. Ask it.
How would this be built for you?
Blueprint
Answer four questions about your own organization and see the shape of what a Polaris Blueprint would cover for a process like this one — a directional preview, not the real diagnostic.
Preview of a Polaris Blueprint™
What industry are you in?
Integrations
The system categories a production build like this would connect to, named honestly by how close each one is to something you can see running in this demo.
CRM / helpdesk platform
reference architectureProtocolREST, JSON over a webhook
DirectionBidirectional
Sample payload shape{ ticketId, customerId, status, lastUpdatedAt }Order management system
reference architectureProtocolREST, JSON
DirectionInbound
Sample payload shape{ orderId, status, items, fulfillmentEta }Policy / claims administration system
reference architectureProtocolREST, JSON
DirectionInbound
Sample payload shape{ policyId, clauseId, coverageStatus, effectiveDates }Identity verification service
requires custom implementationProtocolVendor-specific API
DirectionOutbound
Sample payload shape{ verificationRequestId, matchTier, checkedAt }Messaging channel
supported in principleProtocolChannel-specific webhook or socket
DirectionBidirectional
Sample payload shape{ conversationId, channel, messageId, direction }
Production Path
A real deployment runs through Polaris Deliver, preceded by Discover, Diagnose and Design. This demo is built entirely from synthetic, client-local data — it cannot be put into production as it stands.
- Discover
A Polaris Discovery establishes the real business problem this process would solve for your organisation — not just the pattern this demo illustrates.
- Diagnose
Your existing systems, data quality, and approval requirements are assessed against what a production build like this actually needs.
- Design
The reference architecture is adapted into a specific design: which systems it integrates, what a human approves, and where data may be processed and stored.
- Deliver
Polaris Deliver builds and integrates the real system against your systems of record — this demo's synthetic data plays no part in it.
- Evolve
Once live, the system is measured and extended the same way this demo's audit trail makes visible: every decision recorded, every change deliberate.
See how a real Polaris engagement starts: Explore Polaris.