What we build
Support agents that resolve — and know when not to.
Each capability is a production component — not a proof-of-concept — wired to your KB, helpdesk, and customer record, governed by your runbooks, and tuned continuously against CSAT and re-open rate.
Knowledge-base retrieval, not memorisation
Answers come from your live KB and product docs — never from the model's training data. New articles propagate the moment they ship; outdated answers vanish the moment the article is retired.
Customer-history awareness
Reads plan, prior tickets, account-level signals, and recent product activity before drafting a reply. Returning customers don't repeat themselves; the agent already knows.
Sentiment-aware response
Classifies tone — neutral, frustrated, satisfied, escalation-ready — on every message. Tone shapes the response and triggers escalation paths before a frustrated customer becomes a churn risk.
Tier-1 auto-resolution
Billing FAQs, password resets, integration scope mismatches, plan upgrades — resolved without a human touching the ticket. Solved-on-first-touch becomes the default, not the exception.
Escalation paths the moment it hits its edge
Off-script, multi-system, high-value enterprise, frustrated tone — every condition triggers documented escalation. Human agents inherit a warm thread with KB snapshots, prior tickets, and reasoning attached.
Audit trail on every action
Every reply, every KB lookup, every ticket update, every escalation — logged with timestamp, model, retrieved sources, and confidence. Support managers replay any conversation step-by-step.
Where support agents land
One reasoning engine, every channel.
Same KB, same runbooks, every channel. Help-centre chat, in-app, email, internal helpdesk — every conversation runs through the same retrieval and the same audit trail with channel-tuned tone and escalation thresholds.
Help-centre chat · web
Live chat on docs, pricing, and product pages. Agent qualifies the question, retrieves the KB article, drafts the answer, and closes the loop — handing off only when the issue is genuinely off-pattern.
In-app chat · authenticated
Authenticated in-app chat that knows the customer's plan, integration state, and recent activity. The agent runs setup diagnostics, answers from KB + customer context, and closes Tier-1 without a human.
Inbound email triage
Email tickets parsed, classified, and either resolved or routed to the right human. The reply lands in the customer's inbox in minutes, not hours — drafted with the right account context already attached.
Ticket back-and-forth
Multi-turn ticket conversations across days — the agent maintains context between messages, remembers prior workarounds, and re-evaluates for escalation if the issue persists or sentiment shifts.
Enterprise account-level support
Tier-1 questions from enterprise accounts handled with explicit awareness of contract terms, named CSMs, and SLA tier. Frustrated enterprise customers are escalated faster than self-serve plans, by design.
Internal helpdesk · IT / HR
Same engine, internal-facing — IT password resets, HR policy questions, software access requests. Same KB authority, same audit trail, same escalation discipline as customer-facing support.
Model families we deploy
No single model handles every ticket. So we compose.
KB retrieval, sentiment classification, resolution reasoning, and escalation decision each run on their own model — composed into one agent with version control at every step.
Hybrid retrieval over your KB articles, product docs, runbooks, and prior-ticket resolutions. Match score, source citation, and recency factored in — agent never replies from a stale or unverified article.
Tags every customer message with sentiment, urgency, and escalation-readiness. Tone weights the response style — concise for satisfied customers, careful for frustrated ones — and triggers Tier-2 routing on threshold breach.
Per-ticket-class prompts running on Claude Sonnet/Opus or GPT-5 — picked per use case based on context size, latency, and reasoning depth. Multi-model orchestration with automatic fallback.
Decides per-message whether the agent resolves alone, hands to a human, or pulls in an engineer. Trained on your historical escalation outcomes; tunable per ticket class, customer tier, and SLA band.
Sentiment shapes every reply
Resolve, escalate, or hold for human review — guided by tone.
Tier-1 questions answered from the KB with the right citation, customer history factored in, ticket closed with a CSAT prompt. Solo-resolution is the default for routine issues.
When the KB match is weak or the question is ambiguous, the agent drafts a reply but holds it for a senior rep to ship. Useful in regulated settings or during the trust-building phase.
Frustrated tone, multi-system issues, enterprise customers, off-script topics — all escalate to the right human with full thread, prior tickets, and reasoning trail attached. Never a cold inheritance.
Data sources wired into every reply
Every system that holds the answer — integrated.
Read KB, customer record, ticket history, and live integration state. Write back replies, status changes, and escalation routing. Pulled in parallel, normalised into a unified ticket context, and audit-logged alongside the runbook version that produced the response.
Explainability, not just outputs
Every reply carries its KB source. For the customer. For the manager.
Every message, every retrieval, every action is accompanied by the article it cited, the customer record it read, and the model used — surfaced per ticket for support management, compliance, and tuning.
- KB article cited on every reply
- Customer history snapshot per decision
- Sentiment + confidence on every action
- Escalation trail attached to human pickup
Frameworks we align to
Why Axccelerate for support
Not a deflection chatbot.
A support system.
Deflection bots optimise to get rid of you. Our agents optimise to actually solve the issue — KB-cited, sentiment-aware, customer-context-rich. And when they hit their edge, they hand off to a human with the trail attached. Not a wall, not a maze.
Pricing
Priced to your channels and your stack — not ticket volume.
Support-agent deployments are custom — we scope against your KB, helpdesk, channels, and review cadence before quoting.
Glossary
The vocabulary behind every resolved ticket.
A quick reference for the terms that show up in support-agent reports — the language your CS team, helpdesk leads, and product managers will use during deployment and review.
- Tier-1
- First-line support
The first wave of support — common questions, simple-fix tickets, and FAQ-style enquiries. The bulk of inbound volume; the agent's primary autonomous-resolution scope.
- Tier-2
- Specialist / engineering
Tickets that need a specialist — engineers, product team, account managers. The agent's job is recognising the boundary and routing cleanly without losing context.
- AHT
- Average Handle Time
How long a ticket stays open from first touch to resolution. The agent's autonomous resolutions usually run 30-90 seconds; human-handled tickets run 5-30 minutes.
- FCR
- First-Contact Resolution
The percentage of tickets resolved on the first touch — no back-and-forth, no escalation. Agents lift FCR significantly because they bring the right KB article + customer context to the first reply.
- CSAT
- Customer Satisfaction Score
The customer's rating of the resolution — usually 1-5 or thumbs-up/down. Surfaced post-ticket; CSAT signals feed back into agent tuning weekly.
- KB
- Knowledge Base
The authority repository for product answers — articles, runbooks, troubleshooting guides. The agent retrieves from this, never from the model's training data, so answers stay current.
- Escalation
- Routing to a human / specialist
When the agent decides the ticket is beyond its scope — off-pattern, frustrated tone, enterprise tier, multi-system issue — and routes to the right human with full context.
- Sentiment classifier
- Tone-detection model
A model that tags every message with neutral / positive / frustrated / escalation-ready. Tone weights both response style and escalation thresholds.
- Live diagnostics
- Real-time integration health-check
The agent calling product APIs, OAuth providers, or monitoring tools to verify the customer's actual integration state — instead of guessing from the message.
- Reasoning trail
- Per-ticket audit log
The full record of what the agent saw, what it considered, what KB it pulled, what it picked, and why. Available per ticket for support management, compliance, and tuning.
- Confidence band
- Reply-readiness indicator
How sure the agent is in its drafted reply — low confidence triggers escalation before sending. Tunable per ticket class and customer tier.
- Runbook
- Operational playbook
The codified steps for handling specific ticket classes — refund SOP, password reset path, integration fix tree. The agent follows the same runbook your senior reps use.
- Deflection rate
- % tickets resolved without human
The portion of inbound volume that the agent resolves end-to-end. The healthy band depends on product complexity — 50-75% is typical for SaaS.
- Ghost-agent mode
- Draft-only · human approves
An optional deployment mode where the agent drafts every reply but a human ships it. Useful in regulated settings or during the trust-building phase before full autonomy.
Resolve more tickets. With less drama.
30-minute scoping with a senior engineer and a support-ops specialist. You'll leave with a channel map, KB-readiness audit, and realistic timeline — not a sales pitch.