What to Reorder
Which items need restocking, and when, before a stockout turns into a lost order.
Manufacturing · Distribution · Logistics
An AI Agent that understands
your ERP data.
Order, inventory, purchasing, production, and settlement data run deep. We design AI Agents that work on top of it.
precision_manufacturing
The clues live in your ERP.
The judgment shouldn't be manual.
Your team opens menu after menu to compare data. An Odoo AI Agent surfaces the issue first and suggests the next move.
Shipment Risk
Which orders are likely to ship late, while there is still time to act.
Support Priority
Which customer tickets deserve attention first, by urgency and impact.
Stock Imbalance
Which items are over- or under-stocked across your warehouses right now.
The decisions are already in the data. The Agent just brings them to the surface.
Linkup Infotech
One agent per role,
fluent in your operations.
trending_up
Sales Agent
Customer history summaries, draft quotes, and lead prioritization.
inventory_2
Inventory Agent
Stockout risk, overstock, and inbound and outbound anomaly detection.
shopping_cart
Purchase Agent
Reorder suggestions and supplier lead-time risk analysis.
precision_manufacturing
Manufacturing Agent
Production delay risk and material shortage alerts.
support_agent
Helpdesk Agent
Ticket classification, draft replies, and urgency assessment.
account_balance
Accounting Agent
Receivables, settlement gaps, and month-end checks.
Built for your industry.
precision_manufacturing
Manufacturing
- Production planning by demand, stock, and lead time
- Material shortage risk from production orders
- Quality issue prediction from defect history
- Early alerts for orders at risk of delay
storefront
Distribution
- Purchase history summaries and repurchase likelihood
- Draft quotes from past quotes and sales
- Per-item stockout risk and overstock candidates
- Anomaly detection across sales, collection, and returns
local_shipping
Logistics
- Inbound delays, quantity gaps, and inspection issues
- Alerts for orders likely to ship late
- Picking priority and bottleneck detection
- Stock mismatch, long-held stock, and low turnover
Start small. Scale on results.
1. Use Case Selection
We pick repetitive, data-heavy tasks to start with.
2. Data Check
We verify the data quality of Odoo or your existing system.
3. Agent Role Definition
We define whether the agent reads, summarizes, suggests, or alerts.
4. PoC Application
We apply it to one team or task at a small scale.
5. KPI Measurement
We compare processing time, errors, omissions, and response time.
6. Operational Scale
We expand to other teams and tasks once results are proven.
Frequently asked questions.
Does the AI Agent actually act inside Odoo?
It starts with reading, summarizing, suggesting, and alerting. Proven tasks can extend to automatic execution through an approval step.
Manufacturing, distribution, or logistics, which comes first?
Start where data is well accumulated and work is repetitive: inventory, support, quoting, purchasing, and settlement.
Can we adopt agents without already using Odoo?
Yes, but scattered data should be organized first. Odoo serves as the foundation that unifies your operational data.
Does adopting an AI Agent require heavy development?
Starting small with a PoC works best. Begin with fast-impact tasks like ticket summaries, stock alerts, and draft quotes.
Can sensitive data be connected to AI?
Permissions, access scope, logging, and network boundaries must be designed. Sensitive data can stay off external LLMs or run on an internal or hybrid setup.
Find where an AI Agent
delivers first.
We find the area in your operations where an AI Agent can show results fastest.