AI Agent Support / Helpdesk — Support Health Score 0-100, SLA breaches, recurring topics and support cost under control (Léa)
8 specialized sub-agents analyze your Odoo data 24/7 and recommend concrete actions.
Without UpBoard, you lose time and money
Silent SLA breaches
SLA breaches are discovered at the monthly review, when the VIP client has already called twice. The revenue-at-risk is never quantified.
Rotting backlog
Tickets over 60 days sleep in the queue. No one knows how many, or whether they involve strategic accounts.
Same topics in loop
20% of tickets are the same 3 recurring topics. No FAQ, no product fix, the cost repeats every month.
Invisible support cost
Impossible to price the real cost of a ticket. Some customers consume 5x more support than they generate in revenue.
Satisfaction blind spot
No CSAT, no proxy. When a client leaves, no one had seen the weak signal (re-open, irritation).
AI Agent Support / Helpdesk — everything changes
Support Health Score
Composite 0-100 score + grade A-F across 6 axes (SLA, backlog, CSAT…)
SLA breaches
Tickets in breach sorted by revenue-at-risk, executive escalations
Backlog by age
Segmentation 0-7d / 7-14d / 14-30d / 30-60d / 60d+
Recurring topics
Top 10 themes (semantic LLM clustering) → FAQ / product
Problematic products
Top 10 products generating the most tickets
Costly-to-support customers
Top 10 unprofitable clients (support cost > revenue)
Cost per ticket
Average cost per ticket, by category, by agent, by month
Customer satisfaction
Direct CSAT or proxy (re-open + irritation)
Insight example in action
Léa — Support Agent
Real-time insight
Support Health Score: 54/100 (grade D). 3 tickets in SLA breach for €47k revenue-at-risk (incl. VIP client TechPro). Top recurring topic: "duplicate invoice" (18% of tickets). Shall I escalate the 3 breaches and draft the FAQ entry?
Concrete use cases
Continuous Support Health Score
Before
The support manager only knows whether the team is healthy at month M+1, through manual KPI consolidation.
After
Léa computes a composite 0-100 score + grade A-F (6 components: SLA, backlog, CSAT, re-open, recurring topics, cost/ticket) and alerts when it crosses a threshold.
Defusing SLA breaches
Before
VIP client TechPro (€47k/year) calls 3 times on the same SLA-breaching ticket. No one had prioritized it.
After
Léa lists in-breach tickets sorted by revenue-at-risk, escalates VIP accounts, prepares a template reply to approve.
Top 10 recurring topics
Before
18% of tickets are about "duplicate invoice" but no one sees it, and the team reprocesses it manually every week.
After
Léa runs a semantic LLM clustering on 90 days of tickets, surfaces the top 10 topics and their volume, suggests FAQ or product fix.
Costly-to-support customers
Before
Client "Small ABC" raises 12 tickets/month for €800/year in revenue. Support costs €4,200/year. Invisible until year-end.
After
Léa computes support cost / revenue ratio per client and lists the top 10 unprofitable accounts, with recommendation (renegotiate contract, paid support plan, archive).
FAQ — Agent Support / Helpdesk
Who is Léa?▾
Léa is UpBoard.ai's Support / Helpdesk agent. She runs 8 read-only analytical tools on the Odoo Helpdesk module: health score, SLA breaches, backlog by age, recurring topics (LLM clustering), problematic products, costly customers, cost per ticket, satisfaction distribution.
Can Léa close or reply to tickets?▾
No. Léa operates strictly read-only. She drafts template replies, escalation briefs and recommendations, but ticket closure or any customer-facing send always requires your approval (human-in-the-loop).
Which Odoo modules are required?▾
The Odoo Helpdesk module is mandatory. The Rating module (direct CSAT) and Sale module (support cost / customer revenue cross-check) are optional — if missing, Léa falls back to degraded mode and flags it in her outputs.
How do recurring topics work?▾
Léa applies a semantic LLM clustering on the subjects, descriptions and threads of your tickets over the chosen period. She returns the top 10 topics, their relative volume and suggests the matching FAQ entry or product fix.
Do I need CSAT to measure satisfaction?▾
Ideally yes (Odoo Rating module). Otherwise Léa computes a proxy based on the ticket re-open rate and irritation markers detected in customer messages.
See also
Ready to activate your Agent Support / Helpdesk?
Connect your Odoo in 5 minutes and get your first insights.