Data to AI™ / ChatGPT/Salesforce
Work through pipeline and forecast questions in a ChatGPT chat, approving each Dataddo tool call before any Salesforce opportunity data is read.
Better answers, fewer tokens.
ChatGPT
Which open opportunities closing in September have not been touched for two weeks?
29 of the 85 open opportunities with a September CloseDate have not been modified in over 14 days. Qualification holds the most, with 17 stale deals.
| StageName | Opportunities | LastModifiedDate 14d+ | Stale share |
|---|---|---|---|
| Qualification Opportunities | 42 | 17 | 40% |
| Proposal Opportunities | 28 | 9 | 32% |
| Negotiation Opportunities | 15 | 3 | 20% |
Read from the replicated Opportunities object; stale means LastModifiedDate, the default replication date field, is over 14 days old.
Salesforce is a cloud CRM. Dataddo replicates the objects you choose, standard or custom, with the fields your org defines, or runs your own SOQL query.
Find open Opportunities whose CloseDate is already in the past. Show Name, StageName and CloseDate, oldest date first.
Before the weekly pipeline call, a sales ops manager approves each tool call and pastes the list into the agenda, so owners correct their dates before the meeting starts.
Check data_status for the Salesforce flow first, then count Opportunities with a CloseDate in September that are now won.
A finance analyst writing the monthly bookings note wants proof the data is from this morning. ChatGPT reports when the flow last ran, then gives the count.
Which Accounts have the most won Opportunities this year? Give the Account Name and number of deals, top ten only.
Account managers planning renewals ask in a developer-mode chat for the customers who sign most often, then agree who to call first without requesting a custom report.
Data to AI™ is priced per seat, like the AI assistants your team already uses, with ~25% off on annual billing. Need tailored contracts or volume pricing? Talk to us about Enterprise.
Serve governed business data to Claude, ChatGPT and other AI tools over MCP.
$10.49/month
per seat
3 seats minimum
Flexibly deploy Dataddo in any cloud or hybrid environment.
Custom
We have a payment model that works for you
| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Getting data into the chat | A CSV of the opportunity report is uploaded to ChatGPT, and it is out of date the moment a stage changes in Salesforce. | The Dataddo app in ChatGPT developer mode queries the replicated Salesforce objects when you ask, with no file to upload. | No uploads |
| Control over each lookup | An uploaded file is read in full, and there is no step where you see or approve what the model looks at. | ChatGPT asks you to confirm every Dataddo tool call, and individual tools can be switched off in the app settings. | You approve every read |
| Contact details | CRM exports carry contact emails and phone numbers into the conversation along with the pipeline figures you actually needed. | Untick personal fields such as email when you create the source, so no row that reaches ChatGPT carries them. | Pipeline without PII |
| Knowing how current it is | Nobody remembers which day the uploaded export was pulled, so the answer carries no date at all. | ChatGPT can call data_status and known_gaps, so it says when the Salesforce flow last ran before it gives you numbers. | Answers with a timestamp |
| Token consumption | ChatGPT spends tokens exploring raw Salesforce 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. | Fewer 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 Salesforce datasets finance and RevOps teams use most, with their real field names. ChatGPT queries them by name through the Dataddo semantic layer.
| Dataset | Metrics and attributes |
Ask ChatGPT
|
|---|---|---|
| Opportunities |
|
Which open opportunities have a CloseDate in the past and still sit in an early StageName? |
| Opportunity Line Items |
|
Which line items have a SystemModstamp newer than their LastModifiedDate? |
| Accounts |
|
Which account Name values appear more than once in Salesforce, suggesting duplicate records? |
| Leads |
|
How does the number of leads touched this month compare with last month, by LastModifiedDate? |
| Custom Objects |
|
Which Custom Fields on our Salesforce custom object are most often left empty? |
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 Salesforce objects such as Opportunities, Accounts and Opportunity Line Items with a Dataddo source and attach the flow to a ChatGPT destination or AI Model. In ChatGPT on the web, turn on developer mode, create an app with the MCP server URL https://headless.dataddo.com/mcp-data and OAuth, then pick it from the + menu.
Plans with developer mode: Pro, Plus, Business, Enterprise and Education, on the web only. On Business and Enterprise, a workspace admin may have to allow developer mode before you create the app.
ChatGPT confirms every tool call, so you see whether it runs describe_entity, query or another tool before opportunity data is read. You can also switch individual Dataddo tools off in the app settings.
That depends on the flow. For low volumes, extract everything on each run. For large volumes, the docs describe a second source that reads deleted records through the sObject Get Deleted endpoint.
An account with at least admin-level permissions. If you lack them, a teammate with the authorizer role can authenticate Salesforce for you. People chatting in ChatGPT only sign in to Dataddo, never to Salesforce.
As far as the flow holds. Each run reads a relative window, for example the last 30 days. To load older opportunities, run a full data re-sync with a wider range.
No. The model asks for entities and fields by name and Dataddo never accepts SQL from it. SOQL runs only inside a Custom SOQL Query source that you define yourself in Dataddo.
