Data to AI™ / Copilot/Odoo
Sales, operations and finance staff ask a Teams agent about Odoo invoices and orders from your own instance, while finance controls which models the agent can reach.
Less tokens, better answers.
Microsoft 365 Copilot
On which day last week did our invoice records change the most?
Monday 21 Sep was the busiest day, with 64 invoice records Created On and 118 Last Updated On. Updates outnumbered new records on each day shown.
| Day | Records by Created On | Records by Last Updated On |
|---|---|---|
| Mon 21 Sep Odoo Table Replication | 64 | 118 |
| Tue 22 Sep Odoo Table Replication | 41 | 87 |
| Wed 23 Sep Odoo Table Replication | 38 | 72 |
Counted on one replicated model filtered by Last Updated On, the date range field the docs recommend against Odoo pagination duplicates.
Odoo is an open-source ERP with modules for accounting, inventory, sales, CRM and HR. Dataddo replicates whole Odoo models: you pick a model from your instance and its columns, including custom fields, so the fields match your setup.
@Odoo Ledger Which invoice records for the customer I name were Last Updated On this week, and what is their monetary total?
Account managers mention the Odoo agent in Teams before a customer call, so finance stops answering the same invoice status questions one message at a time.
@Odoo Ledger Total the monetary amount of invoice records per month this year in a table I can set against our forecast.
The FP&A lead asks the agent in Microsoft 365 Copilot for invoiced totals by month and discusses the gaps to forecast with budget owners in the same thread.
Use the dataddo tools to describe the Odoo sales order entity and generate a TypeScript interface with each field and its type.
A developer building finance reports asks GitHub Copilot in Agent mode for the real fields of this instance, so the code covers custom fields too.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Invoice questions in Teams | Account managers ask finance in chat whether a customer was invoiced, and someone looks it up in Odoo by hand. | A Copilot Studio agent published to Teams answers from the replicated invoice model when it is mentioned with @. | Status answered in the channel |
| Which models the agent reaches | Sharing Odoo exports in a Teams chat gives everyone in it every column of every model in the file. | The agent only sees the models and columns finance attached, and the server description says when to call Dataddo. | Finance sets the scope |
| Group companies | Each company is exported separately after switching company in Odoo, and the agent never sees the group picture. | A custom filter on the source pulls more than one company, so the agent can answer per company or for the group. | One agent for the group |
| Developer access to fields | Developers ask for sample exports to learn which datetime and many2one fields a model holds in this instance. | GitHub Copilot in VS Code describes the replicated entities through .vscode/mcp.json, with every field type included. | Types from the real schema |
| Token consumption | Copilot spends tokens exploring raw Odoo columns, sampling rows and retrying until it understands the data. | Field definitions and relations are supplied up front, so the model skips the exploration and goes straight to the answer. | Less tokens per answer |
| Answer quality | Prone to wrong joins, invented fields and numbers that are hard to check. | Answers grounded in defined fields. In the Dataddo benchmark, 78.5% of questions were answered correctly, against 65.5% with plain CSV files. | Better answers |
The Odoo datasets finance and RevOps teams use most, with their real field names. Copilot queries them by name through the Dataddo semantic layer.
Runs on your schedule. Each run reads a relative window on the date field you choose, such as Last Updated On, and a full data re-sync loads older records.
Odoo connector →We asked 43 pre-registered questions of three real datasets - Google Search Console, HubSpot deals and Google Ads - and delivered the same rows to the same model in four ways. Only the data layer changed.
of questions answered correctly - the best of the four delivery methods tested
fewer confidently wrong answers - 14.0% of runs against 24.0% and 26.3%
more correct answers on messy CRM data - better on 8 questions, worse on none
Same model, prompts and rows in every condition; 759 graded runs, ground truth frozen before any run. On Google Search Console alone, documented CSV files did as well as Data to AI™ - the advantage comes from messier data such as HubSpot deals.
Read the benchmark →Replicate the Odoo models you need, for example invoices and sales orders, as Dataddo sources and attach the flows to a Copilot destination or AI Model. In Copilot Studio, add a Model Context Protocol tool with https://headless.dataddo.com/mcp-data, choose OAuth 2.0 with Dynamic discovery and publish the agent to Teams and Microsoft 365 Copilot.
Through a Copilot Studio agent with Dataddo Data Access as its MCP tool, published via Channels > Teams and Microsoft Copilot. The agent reads replicated Odoo data, never Odoo itself.
Name the Odoo models you replicate, for example invoices and sales orders from Odoo. The agent uses the description to decide when to call Dataddo instead of answering from its own knowledge.
Yes. Put the Dataddo URL in .vscode/mcp.json with type http, start the server and sign in, then use Agent mode. On Copilot Business and Enterprise an admin must enable the MCP servers in Copilot policy.
Odoo fields depend on your instance and the columns you pick. The agent can call describe_entity to learn them, so custom fields need no extra setup in Copilot Studio.
Odoo's pagination can cause duplicates. Set the date range field on the source to Last Updated On and add dataddo_hash as a natural key, or ask the Dataddo Solutions Team for help.
Each run reads the relative window you set on the date range field. For older records, run a full data re-sync with a wider range, then ask the agent again.