Most Odoo teams sit on rich operational data — sales orders, invoices, inventory moves, timesheets, CRM pipeline — but getting a straight answer still means someone building a filtered list view, a pivot, or a custom QWeb report, then exporting to a spreadsheet. Non-technical staff wait on whoever knows the filter syntax and the domain expressions. Odoo's native search, group-by, and pivot views are powerful, but they assume the user already knows which model holds the answer and how to phrase the domain. There is no conversational layer that turns "how much did we invoice in EUR last quarter for the Berlin team, and how does that compare to the quarter before?" into a governed query and a chart.

The AI Data Chat Assistant is a made-to-order Odoo module we build for your database that adds exactly that layer. It embeds an OWL-based chat panel — with optional browser voice input via the Web Speech API — into the Odoo web client, backed by a server-side controller that translates natural language into a safe, parameterized ORM query plan. We do not hand raw SQL to the model or let it execute arbitrary code: the assistant is constrained to a whitelisted schema of models.Model records and fields you approve during scoping, it builds Odoo domains and read_group aggregations against those models, and every result passes through the requesting user's own access rights. ir.model.access.csv and record rules apply on every call, so a salesperson only ever sees the records they could already open, and multi-company rules are respected end to end. Answers come back as a short narrative plus a structured table or chart, with a "show me the records" action that opens the matching native list view so the numbers are always auditable.

Technically, the build centers on a new module (__manifest__.py declaring the OWL assets, controllers, and security), a query-planner service that maps intent to model + fields + domain + aggregation, and an LLM adapter that calls your chosen provider over an outbound API (or an on-prem/self-hosted model where data residency requires it). We add compute fields with @api.depends only where a question needs a derived metric that does not exist yet, register automated actions for any scheduled digest you want ("email me yesterday's numbers each morning"), and expose an optional JSON-RPC endpoint so the same assistant can answer from a portal or an external chat surface. Conversation history, the resolved query, and the row count are logged per user for governance. The module targets Odoo 17.0, 18.0, and 19.0; it runs on Community, with Enterprise-only niceties (such as the spreadsheet/dashboard handoff) wired in when you are on Enterprise.

Because this is build-to-order, nothing ships until we agree scope. After a short scoping call we confirm which models and metrics are in play, your provider and data-residency choice, and the deployment target, then build against a copy of your database, run UAT on staging, and install on production with a rollback plan. Typical delivery is 2–4 weeks from confirmed scope. Pricing starts from $249 (indicative, single-company base scope); broader model coverage, multi-company and multi-currency roll-ups, self-hosted or on-prem model deployment, and voice/portal channels increase the quoted scope.