AI Chat-to-Report & Dashboard Assistant for Odoo

Name: AI Chat-to-Report & Dashboard Assistant for Odoo
Brand: ECOSIRE
SKU: ai-chat-to-report-dashboard-assistant

Fonctionnalités clés

Natural-language query surface as an embedded OWL component in the Odoo web client, with an optional Discuss/livechat entry point for conversational report requests

Intent-to-ORM translation that maps questions to your models and emits safe `read_group`/`web_read_group` aggregations — never free-form or `sql.raw` SQL against the database

Whitelist guardrail restricting the assistant to sanctioned readable models and aggregatable fields, so it can never mutate data or query outside the approved domain

Full enforcement of `ir.model.access.csv` and record rules on every generated query — users see only rows their Odoo profile already permits

Native output rendering as pivot tables, graph views (bar/line/pie), and dashboard tiles rather than a bolted-on chart widget

Custom-vocabulary mapping so business terms like `GMV`, `net margin`, or `aging buckets` resolve to the correct stored/computed fields on your instance

Saved question templates and a query-history model (`models.Model`) so common asks become one-click reusable reports

Cross-model reporting that joins concepts spanning `sale.order`, `account.move`, and `stock.move` without a developer building each view by hand

Scheduled report digests via `ir.cron` automated actions, delivered to Discuss threads or email on your cadence

Server-side LLM connector using your own API key and configurable model, with full request/response logging for auditability

One-click export of any generated report to QWeb PDF and XLSX

Programmatic report generation exposed over Odoo's XML-RPC/JSON-RPC API for headless or cross-system use

Community/Enterprise-aware build — dashboard tiles and graph rendering replicated on Community, native Spreadsheet/dashboard framework integration on Enterprise

Compatible with and tested on Odoo 17.0, 18.0, and 19.0, packaged as a versioned addon via `__manifest__.py`

Fait sur commande, clé en main

Aucune configuration à faire vous-même — une app fonctionnelle conçue, installée et prise en charge par ECOSIRE.

Vous commandez

Commencez par un prix de développement unique. Nous cadrons le projet avec vous au lancement.

Nous développons et installons

ECOSIRE la développe, la configure et l'installe sur votre Odoo.

Mise en ligne + assistance

Vous êtes en ligne en 2–4 semaines environ, avec une période d’assistance après le lancement.

À propos de ce produit

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 2 to 4 weeks 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 $249 (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.

Ce que vous obtenez

Installable, version-pinned source code of your build for Odoo 17.0, 18.0, or 19.0 (whichever your instance runs), packaged as a proper addon

Installation and configuration on your environment (self-hosted, on-prem, or Odoo.sh), including the LLM connector, API key wiring, and model whitelist setup

Technical documentation: model/field mapping, security matrix (`ir.model.access.csv` + record rules), connector configuration, and the query-generation architecture

End-user guide plus a live training session for the business users who will ask questions and consume dashboards

Post-go-live support window for defect fixes and configuration adjustments

UAT on a staging copy of your database with a documented rollback plan before production cutover

Private git repository handover with full commit history so your team owns and can extend the code

Pour qui c'est

Operations & sales managers

Team leads who live in Odoo but aren't pivot-table experts. They need answers to cross-cutting questions — pipeline by source, margin by product line, aging by customer — without filing a ticket or waiting on a power user every time.

Finance & controlling teams

Controllers who need consistent, permission-safe reporting across `account.move` and sales/inventory data, with scheduled digests and clean PDF/XLSX exports, but who cannot risk a tool that bypasses record rules or exposes data outside a user's access.

Odoo administrators & internal IT

The people who own the instance and are tired of being the human report generator. They want a controlled, auditable AI layer with its own API key, request logging, and a strict model whitelist — plus a git handover so they can maintain it in-house.

Multi-company / group finance leads

Leaders consolidating figures across several Odoo companies who need the metric vocabulary and access scoping to respect company boundaries, and who value a build tailored to their consolidation logic over a generic off-the-shelf reporter.

Comment AI Chat-to-Report & Dashboard Assistant for Odoo se compare

Critère	ÉCOSIRE	Construction personnalisée	Concurrent
Natural-language querying	Built and tuned to your models, custom fields, and metric vocabulary	Possible, but you fund the full AI-to-ORM mapping from scratch	Generic vocabulary; may not know your custom fields or KPIs
Cross-model reports	Maps concepts across sale/account/stock models out of the box	Achievable but each join is bespoke developer work	Usually single-model; cross-app asks fall back to tickets
Security & access control	Enforces ir.model.access.csv + record rules on every query; whitelisted models only	Depends entirely on your team's discipline to get right	Varies; guardrails often opaque or not instance-specific
Query safety	Structured ORM read_group only — no sql.raw, no free-form SQL	Risk of unsafe SQL if not carefully engineered	Unknown internals; you can't audit the generation layer
Odoo version support	Built for your instance on 17.0, 18.0, or 19.0; Community/Enterprise-aware	Whatever you build and then maintain across upgrades	Listed versions only; may lag new releases
Delivery model	Build-to-order: scope, build, UAT on staging, rollback plan, git handover	Long in-house build cycle; you own all the risk	Instant download, but generic and unsupported for your data
Support & ownership	Post-go-live support window plus full git repo handover	You maintain it entirely in-house	Vendor-dependent; limited to their roadmap
Total cost of ownership	Indicative from $249, fixed quote after scoping — tailored, supported	Highest — full engineering plus ongoing maintenance	Low sticker price, but rework and gaps add hidden cost

Foire aux questions sur AI Chat-to-Report & Dashboard Assistant for Odoo

How long does delivery take?

This is a build-to-order module, not an instant download. After a short scoping call to confirm the models, KPIs, and user roles in scope, typical delivery is 2 to 4 weeks from confirmed scope. Timelines lengthen with the number of custom models and bespoke metrics to map, multi-company consolidation, or special LLM connectivity requirements for on-prem/Odoo.sh setups.

How does pricing and scoping work?

Pricing starts from $249 as an indicative from-price for a single-company base scope. The exact figure depends on how many models and KPIs we map, multi-company needs, localization of metric vocabulary, and any extra integrations. You get a fixed written quote after the scoping call — the from-price anchors the base build, not the final number. We never present a firm fixed price before understanding your data model.

What support and updates do I get after go-live?

Every build includes a post-go-live support window for defect fixes and configuration adjustments, plus the full git repository so your team can maintain and extend the code. We test and support Odoo 17.0, 18.0, and 19.0; version upgrades or new-model coverage after the support window can be scoped as a follow-on engagement.

Is it safe to let an AI query our Odoo database?

Yes, by design. The assistant never writes free-form SQL and never runs `sql.raw`. It generates structured queries through Odoo's own ORM (`read_group`/search domains), constrained to a whitelist of readable models and aggregatable fields. Every query still passes through `ir.model.access.csv` and record rules, so a user only ever sees rows their Odoo profile already permits — the assistant cannot escalate privileges or mutate data.

Does it work on Odoo Community or only Enterprise?

Both. We build Community/Enterprise-aware. On Enterprise we integrate with the native Spreadsheet and dashboard framework; on Community we replicate the dashboard tiles and graph rendering so business users get the same experience. The scoping call confirms which edition you run so we build the right rendering path.

Can it generate reports that span multiple Odoo apps?

Yes — that's a core reason to build this rather than rely on native views. We map concepts that cross models such as `sale.order`, `account.move`, and `stock.move`, so a single natural-language question can return a report that native pivot/graph views would require a developer to construct manually.

Can other systems request reports without opening Odoo?

Yes. The report-generation endpoint is exposed over Odoo's XML-RPC/JSON-RPC API, so another application or an internal script can request a generated report programmatically and receive the structured result, subject to the same access-control and whitelist guardrails.