Every team running Odoo hits the same wall: the people who most need answers are the least equipped to build the views that hold them. A sales manager who wants "won opportunities by salesperson for the last two quarters, grouped by lead source" has to either learn Odoo's pivot and group-by mechanics, wait on a power user, or file yet another ad-hoc request that lands in someone's backlog. Odoo's native pivot table, graph view, and dashboards are genuinely capable, but they assume the user already knows which model to open, which stored fields to measure, which filters and group-bys reconstruct the question, and how to save that as a reusable favorite. The moment a question crosses models — an operational metric that spans sale.order, account.move, and stock.move — native tooling runs out of road and the request becomes a developer ticket.

The AI Chat-to-Report & Dashboard Assistant is a made-to-order Odoo module that ECOSIRE builds around your data model so business users can ask in natural language and get a real Odoo report back. We install a chat surface (an OWL component embedded in the web client, plus an optional Discuss/livechat entry point) where a user types a question. The assistant maps that request to your models and fields, then generates a structured, safe query — never free-form SQL against your database. Under the hood we translate intent into Odoo's own read_group/web_read_group and ORM search-domain layer, so results always respect ir.model.access.csv permissions and record rules: a user only ever sees rows their Odoo profile already permits. The answer is rendered as a native pivot, a graph (bar, line, pie), or a saved dashboard tile, and can be pinned, scheduled, or exported to a QWeb PDF/XLSX. Because we build the model-to-language mapping specifically for your instance — your custom fields, your compute methods, your terminology — the assistant understands "GMV," "net margin," or "aging buckets" the way your team actually uses them, not a generic vocabulary.

Technically, the module ships as a proper Odoo addon with a versioned __manifest__.py, its own models extending models.Model for query history, saved question templates, and dashboard definitions, computed fields via @api.depends where aggregates need caching, and a security layer of ir.model.access.csv records plus record rules so the assistant itself cannot become a privilege-escalation path. The LLM integration runs through a server-side connector you control: your own API key, a configurable model, request/response logging, and a guardrail layer that constrains the AI to a whitelist of readable models and aggregatable fields — the pattern proven in Odoo's sql.raw-free query building, so nothing the assistant emits can mutate data or reach beyond the sanctioned domain. Scheduled digests use ir.cron automated actions to push a "your Monday numbers" report to Discuss or email. For headless or cross-system use, the same generation endpoint is exposed over Odoo's XML-RPC/JSON-RPC API so another application can request a report programmatically. We support Odoo 17.0, 18.0, and 19.0, and adapt to the Community/Enterprise split — on Community we replicate dashboard tiles and the graph rendering that Enterprise ships natively; on Enterprise we integrate with the Spreadsheet and dashboard framework rather than reinventing it.

Delivery is build-to-order, not an instant download. After a short scoping call we confirm which models, KPIs, and user roles are in scope, then build against your requirements, validate on a staging copy of your database (UAT), and deploy to production with a rollback plan and a git repository handover. Typical delivery is one working week from confirmed scope, depending on how many models and bespoke metrics you need mapped and whether an on-prem or Odoo.sh LLM connectivity path is required. Pricing starts from $999 (indicative, single-company base scope); multi-company consolidation, deeper localization of metric vocabulary, additional data-source integrations, and the volume of custom models and KPIs to map increase the quoted scope. You receive a fixed quote after the scoping call — the from-price anchors the base build, not the final figure.