● SOLUTION
Customer Support Resolution
Tickets are classified, matched against your knowledge base, and resolved automatically when confidence is high. Low-confidence cases route to the right specialist with a draft response ready — not a blank reply box.
Available as SaaS or on-premise. Your KB stays where it is.
~50%
Tickets auto-deflected
−40%
Average handle time
+18%
CSAT lift
Support Pipeline · SISAV
TICKET CYCLE
S
Signal
Email, chat, call, web form
I
Interpretation
Intent · sentiment · urgency
S
Solution
KB match + confidence score
A
Action
Auto-reply or route + draft
V
Verification
CSAT · FCR · accuracy audit
Not “tickets touched”
“52% deflected, AHT 4m 12s, CSAT 4.6”
The support resolution problem
Volume goes up, headcount can’t. Tier-1 burns out repeating itself.
60%
of tickets are repeat questions already answered in the knowledge base.
5+
tools an agent toggles per ticket: helpdesk, KB, CRM, billing, internal wiki.
38%
first-contact resolution at most teams. The rest bounce between queues.
0
closed-loop visibility into which KB articles actually deflect tickets.
How it works — SISAV cycle
Five phases. Every ticket goes through the same closed loop.
Signal
Capture every channel
Tickets land from anywhere. The pipeline normalizes them into a single queue with full conversation history attached.
Email, helpdesk, web chat, in-app messenger
Voice transcripts (IVR, contact center)
Telegram, WhatsApp, Messenger, social DMs
Webhooks from Zendesk, Freshdesk, Intercom, Jira SM
📧
💬
Web chat
📞
Voice
📱
🎫
Helpdesk
🔗
API
All channels → Unified ticket queue
TICKET #38291
Intent
Refund · double charge
Sentiment
Frustrated
Urgency
High
Customer tier
Pro · 18 mo
Routing confidence
94%
Interpretation
Classify, score, enrich
Each ticket is parsed and stamped with structured attributes. Customer history, product context, and prior tickets are pulled in automatically.
Intent & category classification (your taxonomy)
Sentiment & urgency scoring
Language detection & translation
CRM enrichment (plan, tenure, prior issues, account value)
PII masking before any external LLM call
Solution
Match against grounded knowledge
Retrieval over your KB, prior resolved tickets, runbooks, and product docs. Every candidate answer comes with citations and a confidence score.
RAG over your existing KB — no migration
Confidence threshold per category, set by you
Citations attached to every answer
No hallucinated promises — if the KB doesn’t cover it, it gets escalated
TOP KB MATCHES
Refund · duplicate charge
96%
KB-882 · last edited 2 weeks ago
Billing · failed retry
63%
KB-401
Plan change · proration
34%
KB-220
Confidence ≥ 90%
Auto-resolve
Refund issued, customer notified, ticket closed.
Below threshold
Route + draft
Specialist opens the ticket with a draft + cited KB.
You set the threshold per category
Action
Auto-resolve or route with a draft
Above the confidence threshold, AI replies, executes the resolution (refund, plan change, password reset), and closes the ticket. Below it, the right specialist gets the case with a drafted answer they can edit and send.
Backend actions (refund, reset, ship replacement) via your existing APIs
Skill- and language-aware routing
Drafted reply with KB citations attached
Brand-voice tuning per channel and customer tier
Verification
Closed-loop quality
Every resolution is checked. CSAT surveys, follow-up reopen rate, and audit sampling feed back into thresholds and the KB itself — gaps surface as suggested articles.
CSAT, FCR, AHT, deflection rate
Reopen rate & reopen reason
QA sampling with reviewer notes
KB gap detection — new articles drafted from missed cases
THIS WEEK
Tickets handled
8,412
Auto-resolved
4,366 (51.9%)
Avg. handle time
4m 12s
CSAT
4.6 / 5
Cost / resolution
−43%
Everything support resolution needs
In one platform. No bot, no plug-in glue.
Grounded answers
RAG over your existing KB, runbooks, and prior tickets. Citations on every reply.
Confidence-gated automation
You set the threshold. Below it, a human takes over — with a draft.
Backend actions
Refund, reset, swap, ship — AI executes via your APIs, not just answers.
Multi-language
Detect, translate, respond natively in 50+ languages.
Skill routing
Right team, right specialist, right shift — based on category, language, account value.
Voice + chat parity
Same intent model on transcripts, web chat, email, and messengers.
PII masking
Sensitive fields scrubbed before any external LLM call. Policy-as-code.
KB gap detection
Missed cases become drafted articles for your KB owners to publish.
Helpdesk-native
Bidirectional sync with Zendesk, Freshdesk, Intercom, Salesforce SC, ServiceNow, Jira SM.
Deploy your way
Customer Support Resolution runs wherever your data lives.
S
SaaS
Hosted by us. Up in days. Enterprise isolation, SOC 2 infrastructure.
Days to first ticket auto-resolved
Automatic updates & scaling
99.9% SLA uptime
P
On-Premise
Your data center or private cloud. KB, transcripts, and PII never leave the perimeter. Air-gap compatible.
Kubernetes + Helm
Local LLMs (Llama, Mistral) or private endpoints
GDPR · HIPAA · SOX ready
H
Hybrid
Sensitive context on-prem, frontier models in cloud. Policy-as-code routes data per step.
PII stays on-prem, AI in cloud
Mix local + cloud per step
Gradual migration path
Who uses it
Any team where ticket volume outruns headcount.
B2B SaaS
Tier-1 deflection on billing, plan changes, integrations. Engineers get the rest with full context.
E-commerce
Order status, returns, refunds resolved end-to-end via your OMS APIs.
Telco & Utilities
High-volume billing and outage queries. On-prem deployment for regulated data.
Financial services
Card disputes, statement questions, KYC follow-ups. Air-gap deployment, full audit trail.
Travel & hospitality
Booking changes, cancellation policies, loyalty queries — multi-language, 24/7.
Healthcare
Appointment, prescription refill, and benefits questions. HIPAA-ready on-prem.
Resolve more, escalate less
Tell us about your ticket volume and stack — we’ll show you the deflection ceiling.
Or reach us at info@aglium.com · Telegram
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