We connect Claude directly to your Odoo instance.
Most Odoo partners use AI the way everyone does — as a coding assistant that suggests snippets a developer copies over by hand. We do something different. Using an MCP server, we connect Claude directly to a live Odoo instance, so it reads your actual data model and builds against it — customizations in a fraction of the usual time, and advanced cross-module reports standard Odoo can't produce. Nothing reaches production without human review.
A connected instance, not a code generator.
The distinction matters. A code assistant produces text a developer reviews, adapts, and deploys manually — the AI never touches your system. Our approach connects Claude to a live Odoo instance through an MCP server, with database credentials scoped to the engagement.
Claude reads the live data model and builds against it. It knows your fields, your modules, your existing customizations — because it can see them. The output isn't a generic snippet you have to fit to your schema. It's a customization built for your instance specifically, deployed to staging for review.
Invoice and report customization, in under an hour.
A customer hands us the invoice PDF they use today and asks us to match it in Odoo. Traditionally, this is 8–15 hours of QWeb template work — more if there are tricky layout or data requirements.
With Claude connected to the instance, we feed it the PDF, it generates a matching QWeb report against the live data model, and deploys it to staging. The first version is usually ready in 30 to 60 minutes. When there's added complexity, maybe two hours. Then a human reviews it before it goes live.
We've used the same approach for real engineering, not just templates — the pharma field-force position management module was built this way, then reviewed and hardened before deployment.
What this is good for — and what it isn't.
The connected-instance approach is genuinely transformative for some work and irrelevant for other work. We're specific about which is which, because overpromising here would be the fastest way to lose your trust.
Where it delivers real speed
- Report & invoice customization QWeb templates matched to a customer's existing format — the clearest win.
- Module scaffolding New custom modules built against the live data model, then reviewed and hardened.
- Data model changes New fields, computed fields, constraints, and relationships built to fit the existing schema.
- Configuration at scale Repetitive setup across many records or modules that would take hours by hand.
- Rapid iteration When a requirement changes mid-build, the turnaround is minutes rather than another work cycle.
Where it doesn't replace engineering judgment
- Complex external integrations Negotiating third-party APIs, settlement reconciliation, and edge-case handling still need an engineer leading.
- Deep domain logic Business rules that require understanding your operation before they can be built correctly.
- Architecture decisions How a system should be structured is a human judgment, not something to delegate.
- Anything unreviewed No AI-built change reaches production without an engineer validating it first. That's non-negotiable.
Reports and dashboards standard Odoo can't produce.
Standard Odoo reporting is functional but bounded — it shows you what's in a module, one module at a time. With Claude connected to the instance, management and leadership teams can ask for analysis that spans modules, combines data sources, and surfaces insight that doesn't exist in any stock report. You ask in plain language; the analysis is built against your live data.
Executive KPI dashboards
The metrics leadership actually watches, pulled together across modules into one real-time view.
Profitability & revenue analysis
Margin by product, customer, channel, or location — combining sales, inventory, and cost data.
Multi-company MIS dashboards
Consolidated management reporting across multiple entities, currencies, and operations.
Inventory & procurement optimization
Stock health, reorder signals, and procurement efficiency drawn from live inventory data.
Manufacturing efficiency tracking
Throughput, downtime, and yield analysis combining work orders, BOMs, and stock movements.
Project profitability
Real margin per project, combining timesheets, costs, and billing into one picture.
Employee productivity analysis
Output and utilization views drawn from the modules your team already works in.
Quality root-cause analysis
Pattern analysis across quality issues to surface where problems actually originate.
Automated management summaries
Plain-language summaries of what changed and why, generated on demand or on schedule.
Sales & cash flow forecasting
Forward projections built on your historical data. As reliable as the data behind them — we're honest about that.
Customer churn analysis
Patterns in customer behavior that flag retention risk before it shows up in revenue.
Anomaly detection & recommendations
Surfacing the outliers worth a look, with strategic context — a starting point for judgment, not a replacement for it.
We separate these deliberately. The operational and financial reports are descriptive — they tell you what's true about your business, reliably, from your own data. The predictive analyses are genuinely useful but only as good as the data behind them, so we frame them as decision support, not crystal balls.
Marketplace margin dropped 4 points this quarter while volume rose — likely fee structure or discount mix. Worth reviewing the channel's promotion settings against the margin data.
Illustrative example. Figures shown are sample data.
Speed never comes at the cost of an unreviewed change.
Connecting an AI to a live ERP raises an obvious question: what stops it from breaking something? The answer is a workflow built around the assumption that the AI is fast but fallible, and a human is the final gate.
Staging first, always.
AI-built changes deploy to a staging instance, never directly to production. The live system your business runs on is never the target of an unreviewed change. Production deployment is a separate, deliberate, human-initiated step.
An engineer reviews every change.
No customization moves from staging to production without an engineer reviewing the code, testing the behavior, and validating it against the requirement. The speed comes from the build, not from skipping the review.
Scoped credentials, not standing access.
The MCP connection uses database credentials scoped to the engagement. Access is provisioned for the work and managed deliberately — not a permanent open channel into your system.
Everything is logged and reversible.
Changes are tracked, and because the work goes through staging with version control, anything can be rolled back. If a review catches a problem, it never made it past staging in the first place.
The benefit isn't the technology. It's what it costs you.
Faster delivery, lower cost. When a customization that took 8–15 hours takes under one, the savings flow to you — in both the timeline and the bill. We're not charging you for hours we no longer spend.
Faster iteration when requirements change. Most of the frustration in ERP customization comes from the round-trip. You ask for a change, wait days for the next version, realize it's not quite right, wait again. When the build cycle is minutes, that frustration largely disappears.
The same engineering rigor, applied faster. This isn't about replacing engineers with AI. It's about engineers using a tool that removes the slow, mechanical parts of the work so they can focus on judgment, review, and the hard problems. The review step is where 25 years of product engineering experience still does the work that matters.