Resources
Tools for the AI decision on your desk.
Every AI proposal assumes your data is fine. These help you check the assumption before anyone sees your data: a real sample memo, templates, a calculator, a self-check and plain-English guides. When you want evidence from your own records, that’s what the Truth Score Read is for.
Start with the proof
See what comes back before you send anything.
A fictional B2B software company, seven exports from CRM, finance and HR, 72,837 records, run through the same engine a client’s exports go through. Real engine output on generated test data, not a client.
Sample memo · CRM, finance and HR exports
A real Truth Score Read, on test data
The memo you would receive: the Truth Score (a score out of 100 for how far your data can be trusted), the red flags between systems, the scope line, the verdict and the short fix list. Every finding points to records you could check yourself.
Read the sample memoTools
Work it out yourself. In minutes.
Both run in your browser and send nothing. Both are honest about what they are: your own estimates and beliefs, set out clearly, not a measurement.
Calculator
Cost of bad data calculator
What bad records, lost time and manual reconciliation cost you a year. Every assumption visible and yours to set; no hidden industry averages.
Run the calculatorSelf-check · 18 questions
AI readiness self-check
Three questions for each pillar of the Truth Score™. A self-reported indication per pillar, and a count of what nobody could answer. Not a Truth Score.
Start the self-checkTemplates
Take it into the room. On one page.
Both are on their pages in full: read them there, copy them as text, or print them. Nothing to sign up for.
Template · for CEOs and CFOs
AI board memo
Seven sections for any AI proposal: what is proposed, what data it needs, what you know, what you don’t, and the decision you are asking for. Blank, and with a filled example.
See the templateChecklist · for investors
Data diligence checklist
35 questions in eight sections, in the order a deal runs: from the AI plays in the management case to whether the target’s systems agree.
Open the checklistGlossary
The words behind the verdict, defined plainly.
15 terms, from the Truth Score™ and its six pillars to the method behind every memo. Written for a CEO, not a data engineer.
- The Truth Score Truth Score™ The Truth Score™ is DataTruth’s score out of 100 for how far a company’s data can be trusted, built from six weighted pillars and placed in one of four bands, A to D. Read the definition
- The Truth Score Leverability read The Leverability read says which AI ideas your data can use now, which need fixes first, and which it can’t support at all, with the reason in your own records. Read the definition
- Method Cross-system reconciliation Cross-system reconciliation is matching records between systems, such as CRM and billing, or HR and the CRM, to find where they disagree. Every system can look clean on its own; the truth is in the gaps between them. Read the definition
- Method Remediation roadmap A remediation roadmap is a prioritised list of data fixes, each with an owner, an effort estimate and a “done when” test, written to hand to your own team or any outside firm. Read the definition
Guides
Go deeper, by use case, system or industry.
- By AI use caseThirteen AI use casesWhat each one needs from your data, and what usually blocks it.Explore
- By systemSystem guidesSalesforce, HubSpot, NetSuite, SAP, Xero, Workday and more: the fields that matter and the gaps between them.Explore
- By industryIndustry guidesSeven sectors, the systems they run on, and where their data tends to disagree.Explore
- Industry profilesAll seven sectors on one pageWhere we look first in each industry. No invented sector averages.Explore
These show you the field. Your exports show you where you stand.
Exports from two or more systems, matched between them, every red flag checked by hand. Your Truth Score and a Leverability read in a signed memo, within 5 working days of complete exports.
Starts with a 30-minute call. Commits you to nothing.