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datatruth Get your Truth Score

AI readiness for SaaS and technology companiesSaaS has more data than anyone. That isn’t data that agrees.

Your product logs every click, so the data looks rich. The AI plays on the table (churn, expansion, forecasting, a support agent) all depend on one thing: the account in the CRM, the subscription in billing and the workspace in the product being the same customer.

Get your Truth Score The AI plays, one by one

Starts with a 30-minute call. Commits you to nothing.

A Leverability read shows which AI ideas you can use now, after fixes, or not at all. Illustrative example, not a client.

The AI on the table

The AI plays proposed in SaaS and technology.

What vendors and plans put forward in SaaS and technology, what each play needs from the data, and the gap that holds it back. In a real Read, each call rests on your Truth Score™ (a score out of 100 for how far your data can be trusted) and the records behind it. The calls here are an illustrative read, not a benchmark.

  • Churn and renewal risk

    “Flag at-risk accounts before renewal”

    What customer churn prediction needs
    Needs work
    Needs
    One account ID across CRM, billing and product
    Held back by
    Workspaces that map to no paying subscription

  • ARR and revenue forecasting

    “Forecast ARR from the pipeline”

    What revenue forecasting needs
    Needs work
    Needs
    Won deals that match subscriptions and invoices
    Held back by
    ARR calculated three ways: CRM, billing and the board pack

  • Lead and expansion scoring

    “Rank leads and upsell candidates”

    What lead scoring needs
    Deploy now
    Needs
    Outcomes on closed deals, usage on accounts
    Watch for
    Product sign-ups never linked to CRM accounts

  • Support deflection agent

    “Resolve tickets with an AI agent”

    What customer service automation needs
    Needs work
    Needs
    Tickets tied to accounts and plans
    Held back by
    Support entitlements that don’t match the plan billed

  • Ask-your-data for the leadership team

    “Board metrics on demand, in plain English”

    What natural-language analytics needs
    Blocked
    Needs
    One definition of ARR, churn and active customer
    Held back by
    Metrics defined differently in the warehouse and the board pack

Illustrative example, not a client. Your calls depend on your records.

The systems estate

The systems behind the plays.

Written for subscription software and technology companies of roughly €20M to €500M revenue, founder-owned or investor-backed, running a CRM, a billing platform, a finance system and a product database or warehouse. Each system is built to be right about one thing. None was built to agree with the others, and the plays above all run across more than one.

Typical, not exhaustive. Anything that exports to CSV or Excel can be included.

The gaps

Every system looks clean on its own. The truth is in the gaps between them.

One record traced across your CRM, billing and product data: each system right about its own part, and the three still disagree. Then the gaps that hold the plays back. Which of them exist in your data is what a Truth Score Read finds out.

Illustrative example, not a client. The systems change by industry; the method doesn’t.

Where the plays sink

  • CRM ↔ Billing

    Closed Won deals with no subscription, and subscriptions with no deal

    SinksARR forecasting

  • Product ↔ Billing

    Workspaces and users that map to no paying account

    SinksChurn and expansion scoring

  • Support ↔ CRM

    Tickets from users the CRM doesn’t know

    SinksChurn signals and the support agent

  • Warehouse ↔ Finance

    ARR in the warehouse that doesn’t match recognised revenue

    SinksBoard reporting and ask-your-data tools

Gaps to test in the sector, not client findings.

Regulation

The rules that shape which plays you can run.

Kept short and honest. This is not legal advice: we evidence what your data can support; your counsel decides what the rules require.

  • GDPR, as a processor

    Much of your product data belongs to your customers. Whether you can use it to train or tune models depends on your contracts and privacy terms, not only on what the data can support.

  • Customer contracts

    Enterprise customers increasingly restrict AI use of their data in the contract. Those clauses decide which plays are available before the data does.

  • The EU AI Act

    Most SaaS AI features sit in the lower-risk tiers, with transparency duties for chatbots. Features used in hiring, credit or similar decisions about people can be high-risk.

Governance and compliance

Your plan, tested

Got a plan that names these plays?

Send it with your exports. A Truth Score Read tests the AI in it against your own records, within 5 working days of complete exports. Go further with an AI Investment Review: every bet ranked, your plan tested play by play, and a roadmap to hand to your own team.

  1. 01

    Send the plan, if there is one

    The business plan, growth strategy, value creation plan or the vendor proposal. We pull out the AI plays in it and the targets that lean on them.

  2. 02

    Send exports from two or more systems

    Usually CRM and billing and subscriptions to start. Spreadsheets count. Under NDA, using the field list we send.

  3. 03

    Test each play against the records

    Each play mapped to the data it needs. Our engine matches the customers, people, deals and invoices across your exports to see whether that data is there and agrees, and every red flag is checked by hand against sample records.

  4. 04

    Get the verdict, play by play

    A signed memo: your Truth Score, a Leverability read marking each play Deploy now, Needs work or Blocked with the reason in your own records, and a verdict: sign, fix first or walk away.

How the Truth Score Read works

What you get back

See what comes back: a real sample memo.

One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.

Real engine output on generated test data, not a client.

Questions

AI in SaaS and technology, answered plainly.

Anything else, ask on the 30-minute call. It commits you to nothing.

Book the call
Isn’t SaaS data already AI ready? It’s all digital.

Digital isn’t the same as joined. Product events, CRM accounts and billing subscriptions are created by different systems with different IDs. The plays that matter most, churn and ARR forecasting, need all three to agree on who the customer is.

Which AI play should a SaaS company test first?

The one already in a proposal or the board plan. If there isn’t one, churn is a good test: it touches CRM, billing, product and support at once, so it shows quickly whether the systems agree.

Our investors want an AI plan. Can you test it?

Yes. Send the plan or value creation plan with your exports. We pull out the AI plays, map each to the data it needs, and show play by play whether that data is there and can be trusted.

Do you need access to our warehouse?

No. We work from exports: CSV extracts from the CRM, billing and finance, and query results from the warehouse if usage matters. No credentials and nothing installed.

Before SaaS and technology AI gets funded, find out what your data can carry.

Exports from two or more systems, plus the plan or proposal if there is one. A signed verdict, play by play, within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.

Get your Truth Score See a real sample memo

Starts with a 30-minute call. Commits you to nothing.

Get your Truth Score