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 less 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 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 →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 free while in private pre-release. We onboard teams in waves and shape the product with their feedback. At launch, it will be priced like the AI assistants your team already uses. Need tailored contracts or volume pricing? Talk to us about Enterprise.
Serve governed business data to Claude, ChatGPT and other AI tools over MCP.
Free during pre-release
Flexibly deploy Dataddo in any cloud or hybrid environment.
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.
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.
Raw files force the model to infer what each column means, which file to use, and how to aggregate - and a small methodological slip produces a confident, plausible, wrong number. A governed layer ships plain-language field definitions, consistent metric definitions, and explicit relationships up front, so the model spends its effort on the question instead of reconstructing structure. In our benchmark - 43 pre-registered questions across three real datasets - Data to AI™ answered 78.5% correctly, the best of four delivery methods tested, and roughly halved the answers that were confidently wrong (14.0% versus 24.0% and 26.3% for the baselines).
Yes. When the governed layer carries the model to the correct answer, you no longer need the largest frontier model to reason through raw, unlabeled data. In our benchmark, serving data through Data to AI™ was 39% cheaper per question than maintaining documented exports, while still producing the most correct answers - so you can use a smaller, lower-cost model for the same work.
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.
Data to AI™ is in private pre-release. Request access to connect Claude, ChatGPT or Gemini to every source you already sync, and ask about your own campaigns.
