Products / Data to AITM
Connect Claude or ChatGPT once and ask across Meta, Google Ads, GA4 or HubSpot. Your AI gets governed, well-described data instead of raw exports, so it uses fewer tokens and gives better answers.
What did a new lead cost us last week, by paid channel?
Paid social brought leads in at €44.71 each, just over half the cost of paid search at €81.22. Search spend rose 12% on the week while its leads fell.
| Channel | Spend | New leads | Cost/lead |
|---|---|---|---|
| Paid social Meta | €18,420 | 412 | €44.71 |
| Paid search Google Ads | €11,290 | 139 | €81.22 |
Leads counted by HubSpot's own original-source field. No ad was matched to a contact by guesswork.
Spend sits in the ad platforms, behaviour in GA4, leads and revenue in HubSpot. Data to AI™ puts all of them behind a single connection your AI already knows how to use.
What did a new lead cost us last week, by paid channel?
Spend from every ad platform, leads from HubSpot, one table. Each figure labelled with its source.
Facebook Ads
Google Ads
HubSpot
Did the Meta push on Tuesday show up in GA4 sessions and new HubSpot contacts?
Ad delivery, site behaviour and CRM sign-ups, lined up day by day.
Facebook Ads
Google Analytics 4
HubSpot
Draft the Monday recap for each client, across all their channels.
Every channel a client runs, in one summary, each number stamped with its dates and freshness.
Facebook Ads
Google Ads
LinkedIn Ads
Google Analytics 4
HubSpot
We asked three Claude models the same 21 business questions about a NetSuite company built from real-world data: once over a raw SQL connection, once as CSV exports and once through Data to AI™. Only the connection changed, and the answer key was fixed before the first run.
good outcomes with Opus 5.5 on Data to AI™, against 81% on a raw SQL connection
fewer confidently wrong answers with Sonnet 5 - 8 of 63 runs, against 19 on raw SQL
cheaper per question: Haiku 5.5 on Data to AI™ (87.3%) beat Opus 5.5 on raw SQL (81%)
attempts disclosed a sales rep's email address, against 9 of 9 on raw SQL
21 questions, 3 runs each. 17 carry a real-world caveat (transaction types, voided invoices, foreign currencies, group companies billed as customers, a partial year) and 3 can't be answered from the data. Ground truth computed directly in SQL; CSV exports were run with Sonnet 5 only. Run date: 26 October 2026.
Read the benchmark →Claude, Gemini, OpenAI and more connect to your governed data over the open Model Context Protocol - no proprietary lock-in and no custom pipeline per model.
Point your AI at the same CRM data - deals, tickets, contacts and companies - once as raw tables and once through Dataddo. The rows are identical. What changes is how much the model has to guess, and that decides how good, fast and affordable the answer is.
| Without Dataddo | With Dataddo | Outcome for you | |
|---|---|---|---|
| Business context & relations | The model sees raw tables and field names. It has to guess what each field means and how deals, tickets, contacts, and companies relate - often getting the joins wrong. | Every field ships with a plain-language definition and its relations across deals, tickets, contacts, and companies, so the model knows the schema before it reads a single row. | Answers grounded in your real business |
| Freshness & quality signals | No way to tell whether a record is current or reliable. Stale or incomplete data is treated the same as fresh, trustworthy data. | Each field carries freshness timestamps and quality metrics, so the model can weight or flag data instead of trusting everything equally. | Decisions on data you can trust |
| Token consumption | High. The model burns tokens exploring the schema, sampling rows, and retrying until it understands the data. | 50-90% lower. The context is supplied up front, so the model skips the exploration and goes straight to the answer. | 50-90% less token spend |
| Processing time | Slow. Most of the run is spent on discovery, data wrangling, and disambiguation before any real analysis begins. | Much faster. With the groundwork already done, the model spends its time answering, not exploring. | Answers in seconds, not minutes |
| Model tier & cost | Needs a frontier model to reason through raw, unlabeled data - the most expensive option per query. | Smaller, cheaper models handle the same questions, because the hard reasoning about structure is already solved. | Money saved on cheaper models |
| Answer quality | Prone to wrong joins, hallucinated fields, and ungrounded numbers that are hard to catch. | Grounded, consistent answers tied to defined fields and real relations. Harder to measure, but the difference shows. | Answers you can actually rely on |
Numbers your AI gives you end up in client reports. These rules keep them right when a question spans several tools.
Your ad platforms and CRM share no common ID. Cross-channel answers use the fields each tool actually records, never a guessed match between them.
Each figure says which tool it came from and when that tool last synced, so a stale number never reaches a client.
Ask for the best five and they're ranked by the measure you asked about, with the ranking stated.
If a source only holds some accounts or dates, the answer says so, so no total passes for more than it is.
Vendor connectors are good inside their own product. The moment a question spans two tools, your AI is left to stitch the answers together itself.
CRM, marketing, advertising, ERP, finance, support, databases, files
Salesforce
Dynamics 365
Pipedrive
HubSpot
Mailchimp
Klaviyo
Google Ads
Meta Ads
LinkedIn Ads
TikTok Ads
SAP
Oracle
NetSuite
Xero
PostgreSQL
MySQL
Snowflake
BigQuery
Databricks
Redshift
Google Sheets
Amazon S3
CSV / SFTP
Dataddo
Extracted data is landed, deduplicated and kept fresh - so agents query a stable store instead of hammering live business systems.
The model learns what a table means, not just what columns it has.
Connect over MCP - no custom pipeline per model
Claude
Gemini
OpenAI
Mistral
Perplexity
Grok
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
MCP (Model Context Protocol) is an open standard for connecting AI clients to external data and tools. LLMs and agents use it to retrieve governed data from Dataddo on demand - so you connect once and any MCP-capable model can query it, with no custom pipeline per model.
Keep it for working inside HubSpot. Use Data to AI™ when the question needs your ad platforms, GA4 or anything else alongside HubSpot, answered in one place with each number labelled.
Use them for quick questions about one tool. Most connectors expose each source separately and hand the model its raw fields, so it has to guess what each column means and join your sources inside the chat. Data to AI™ serves all the sources as one governed layer, with every field and metric defined up front, so the model doesn't improvise the business rules. You also decide exactly what the AI can see: a built-in connector usually reaches whatever your login can reach, while with Data to AI™ you publish only the datasets and fields you choose. Your pipeline data can be in scope while contact details stay out. In our NetSuite benchmark, every model tested gave fewer confidently wrong answers through Data to AI™ than over a raw SQL connection (3 to 8 of 63 runs, against 12 to 19), and asked for a sales rep's email address, it disclosed it in 0 of 9 attempts against 9 of 9. It's also read-only, so it can't change your campaigns or CRM records.
Claude, ChatGPT, Gemini, or any assistant that supports MCP connections.
If your sources already sync through Dataddo, you add one connection to your AI tool. No new pipelines, no SQL.
No. Dataddo serves data to your AI directly from SmartCache, so you can start without provisioning a warehouse or lake. If you later need one, the same platform delivers CDC mirrors to BigQuery, Snowflake, and Databricks - "no warehouse" becomes a choice, not a limitation.
No. It only reads the data Dataddo already syncs for you. It can't edit campaigns, budgets or CRM records.
Yes, completely. You define exactly which datasets and fields are exposed, and that definition is deterministic and 100% under your control. A model can only reach what you have explicitly published - nothing else is reachable - and PII can be excluded or hashed (md5 / xxh3) before data ever leaves Dataddo.
Dataddo is a data pipeline, not a model provider - it moves your data, it does not train on it. Whether the LLM you connect retains or trains on the data you send is governed by your agreement with its provider, so review their terms for specifics.
In two places, and you control what moves between them. Dataddo extracts your data and keeps it in SmartCache, in Dataddo's managed cloud in the region of your choice. Dataddo is an EU company headquartered in Prague, ISO 27001 and SOC 2 Type II certified. When you ask a question, the AI tool you connected receives only query results from the contexts you've created in Dataddo, and that AI tool processes them under its own terms. Personal data can be excluded or hashed before anything lands in the SmartCache. If your data has to stay inside your own cloud or an EU sovereign cloud, Dataddo supports those scenarios on Enterprise.
Raw tables force the model to infer what each column means, which rows count and how to aggregate - and a small slip produces a confident, plausible, wrong number. A governed layer supplies agreed definitions up front: which transaction types count, which currency applies, whether a period is complete. The model picks metrics and fields, and Dataddo builds the query. In our NetSuite benchmark - 21 business questions, three Claude models, an answer key fixed before the first run - Opus 5.5 reached 95.2% good outcomes through Data to AI™ against 81% over a raw SQL connection, and confidently wrong answers fell on every model (Sonnet 5: from 19 to 8 of 63 runs).
Yes. When the definitions carry the business rules, the model no longer has to rediscover them from raw tables on every question, so a smaller model can do the work. In our NetSuite benchmark, Haiku 5.5 on Data to AI™ reached 87.3% good outcomes at $0.0045 per question, while Opus 5.5 on a raw SQL connection reached 81% at $0.108 - 24 times the cost.
A context is one governed dataset, automatically synchronized and published over the open MCP standard to an LLM or agent.
400+ sources, including databases, cloud warehouses, and hundreds of SaaS APIs. If something you need is missing, you can request it.
Start a free trial to connect Claude, ChatGPT or Gemini to every source you already sync, and ask about your own campaigns.
