Helpdesk agent
From ticket noise to prioritised actions, with revenue at risk in plain euros.
At a glance#
The Helpdesk agent (Léa) plugs into your Odoo helpdesk module and monitors three signals, four times a day: SLA compliance, backlog ageing, and customer satisfaction. Every morning it surfaces the tickets that put revenue at risk and the support patterns that erode your margin.
It blends eight backend tools, health score, SLA breaches, backlog ageing, recurrent topics, problem products, costly customers, cost per ticket, and satisfaction, into one weekly brief in plain language.
What the agent detects#
- SLA breaches in progress with revenue at risk per customer (12-month CA)
- Backlog ageing segmented 0-7d / 7-14d / 14-30d / 30-60d / 60d+
- Recurrent topics with deflection potential to FAQ or articles
- Problem products generating disproportionate ticket volume vs sales
- Costly customers whose support cost outweighs the revenue they generate
- Cost per ticket broken down by team, type, agent and month
- Customer satisfaction via CSAT (Odoo
rating) or a proxy score (re-openings + irritation signals)
Detailed capabilities
62 ready-to-use actions across 18 categories. Identical to the prompt library available in the application.
Priorities & Brief
Examples:
- Monday top 5 (HITL), H15, 5 activities in the manager's calendar
- Daily brief, Daily condensed summary
- Weekly brief (HITL), H14, exec email recap
Health Score
Examples:
- Global score, 0-100 score with A-F grade
- Components detail, SLA, speed, CSAT, backlog, FCR, balance
- Score evolution, Trend and inflection points
SLA Compliance
Examples:
- SLA breaches, List sorted by revenue at risk
- Top revenue at risk, VIP customers in red zone
- SLA evolution, Compliance rate by month
Backlog & Support Debt
Examples:
- Backlog by age, 0-7d / 7-14d / 14-30d / 30-60d / 60d+
- Forgotten tickets, To handle or close
- Oldest tickets, Top 10 by age
Recurrent Topics & Root Cause
Examples:
- Top 10 topics, H4, automatic clustering of subjects
- FAQ candidates, High volume, standardisable answer
- Deflection potential, Estimated support time savings
+ 2 more actions in this category
Products & Channels
Examples:
- Top 10 products, H5, ranking by ticket volume
- Defect rate, Tickets ÷ sales per reference
- Pareto channels, H25, disproportionately expensive channels
+ 1 more action in this category
Costly Customers
Examples:
- Costly customers, Support cost > customer margin
- Profitability ratio, Top loss-making customers
- VIP overview, Tickets, SLA and CSAT for key accounts
Cost & ROI
Examples:
- Cost per ticket, H7, payroll ÷ ticket volume
- Cost by category, Cost distribution by topic and agent
- Financial ROI, H24, observed retention 6-9 months
+ 1 more action in this category
Customer Satisfaction
Examples:
- Global CSAT, H8, 30-day average satisfaction
- Dissatisfied list, Low CSAT or re-openings
- CSAT distribution, Rating histogram
Sentiment & Emotion
Examples:
- Ticket sentiment, H19, Haiku batch + LRU cache
- Angry customers, Strong-emotion detection
- Emotionally urgent tickets, Emotional triage (≠ Odoo priority)
Resolution & FCR
Examples:
- Resolution speed, Health component (H1)
- FCR rate, H20, global + per agent / team / type
- Worst non-FCR, Top 10 re-openings
Team Performance
Examples:
- Workload per agent, Open tickets per team member (H1)
- Agent scorecards, H22, score 0-100 + recommendations
- Team balance, Workload standard deviation
+ 1 more action in this category
Churn Risk
Examples:
- At-risk customers, H18, score 0-100 (volume/CSAT/speed/recency)
- Weak churn signals, Early detection
- VIP alert (HITL), H13, sales activities + exec email
Forecast & Capacity
Examples:
- 30-day volume forecast, H17, Holt-Winters numpy (weekly seasonality)
- Volume peaks, Predicted spike detection
Customer Actions (HITL)
Examples:
- AI reply draft, H9, Sonnet → internal mail.message
- Ticket escalation, H10, priority=3 + manager activity
Tagging, KB & Templates (HITL)
Examples:
- Tag a cluster, H11, tag + cross-link on all cluster tickets
- KB article, H12, Sonnet → knowledge.article
- Reply templates, H26, Sonnet → mail.template
Brief & Reports
Examples:
- Weekly brief, Backlog, SLA and urgent tickets for the exec team
- Monthly report, Health, cost per ticket and satisfaction of the month
- Support costs & ROI, Costly customers, channels and return on investment
+ 2 more actions in this category
Multi-domain
Examples:
- Cash impact of late supplier, Crosses finance (cash) and purchasing (delays) in one query
- Stock out vs open quotes, Crosses inventory (availability) with sales (pipeline)
- Customer 360° view, Sales + finance + helpdesk for the same customer
+ 3 more actions in this category
What the agent quantifies for you#
What it measures
SLA breach ticket by ticket, handling cost per ticket, and the twelve-month revenue of the customer behind each breach.
On which data
Your Helpdesk tickets and teams in Odoo, your sales when the module is installed, your CSAT ratings when the rating module is. Your records, never a market average.
At which cadence
Four scans a day, plus an on-demand scan before a team stand-up or a customer review.
The decision it enables
Knowing which ticket to handle first because the customer behind it weighs heavily, and which recurring cause deserves a fix at the source.
A worked example
The starting hypotheses
- 4 tickets breaching their SLA over the month
- €22,000 of twelve-month revenue on average for the customers concerned
The arithmetic
4 × €22,000
Result of these hypotheses
€88,000
of customer revenue exposed, so these four tickets come before the rest.
That amount is the product of the hypotheses above. It is not a result observed at a customer, and we publish no typical range by company size.
Your own figure is computed on your own Odoo: the free scan produces it in a few minutes.
The amount itself is yours. We publish no typical range by ticket volume: it would say nothing about your customer base, and it would read as a promise. The free scan computes your figure from your own tickets, in a few minutes.
Demo in four steps#
- 1
Connect to your Odoo
Secure authentication via dedicated API key. The agent detects whether the rating and sale modules are installed and adapts its outputs.
2 min
- 2
Initial helpdesk scan
The agent computes a 0 to 100 health score across 6 weighted components: SLA compliance, resolution speed, FCR, CSAT, backlog pressure, agent balance.
30 sec
- 3
Revenue-at-risk prioritisation
Each open ticket is scored by SLA breach severity x customer 12-month revenue. Top 10 tickets are surfaced with VIP and product context.
15 sec
- 4
Brief, recommendations, validation
You get a weekly executive brief with concrete actions (re-route, FAQ candidates, agent re-balancing). HITL: nothing is changed in Odoo without you.
your call
Required Odoo data#
Required modules
- Helpdeskhelpdesk
Optional modules
- ratingrating
- Salessale
The agent detects optional modules at runtime. Without 'rating' it falls back on a proxy CSAT score (re-openings + client irritation). Without 'sale' it skips the customer revenue-at-risk views.
Supported versions: Odoo 15 to 20, Enterprise edition only: the Helpdesk app is not part of Odoo Community.
Human validation#
Agent
The agent prepares
You
You approve
Agent
The agent executes
● No action is sent to your Odoo without your explicit approval.
In practice, the Helpdesk agent can:
- Surface SLA-breach tickets with revenue at risk → you decide which one to escalate
- Suggest ticket re-routing between teams or agents → you validate before any change
- Propose FAQ / article candidates based on recurrent topics → your knowledge team writes
- Compute the cost per ticket by team / agent / month → input for your team plan
The agent never automatically:
- Closes or merges tickets
- Reassigns tickets without your confirmation
- Sends communications to customers
Frequently asked questions#
Do I need the Odoo rating module to use the agent?
No. If rating is installed, the agent uses real CSAT scores. Otherwise it computes a proxy satisfaction score from re-openings and client message irritation signals, and clearly flags the result as 'degraded'.
How is revenue-at-risk computed?
For each open or breached ticket, we look up the customer's 12-month revenue from your sale module (when available). The score blends SLA severity × revenue exposure, so a 2-hour breach on a customer who weighs heavily in your revenue outranks a 3-day breach on a marginal account.
Does the agent re-route tickets automatically?
Never. It proposes re-routings (between teams or agents) with the rationale, and you validate. Even at the highest plan tier, all ticket-state changes are HITL, no client communication or status change is performed without you.
Does my support data leave my Odoo?
The agent reads your data via the Odoo API. Analysis runs on our infrastructure hosted with Infomaniak in Switzerland. No data is used to train a model. Each customer has an isolated tenant. More in our security page.
Can the agent draft a customer reply?
Reply drafting is on the roadmap and out of scope for the v1 Helpdesk agent. Today the agent focuses on prioritisation, root-cause patterns and team-level efficiency.
Read next
See this agent working on your Odoo data
A few minutes, read-only, no card. Then a trial on your real data: 14 days, or until the trial credit is used up.
Rather talk it through? Book a demo