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AI-ready data for every AI use case

Is your data ready for AI?

AI only pays off when it knows your business and runs on data you can trust.

Start your free trialHow it works
Monthly subscriptionNothing to replaceSOC 2 Type II certified
Your AI use cases
Sales and marketing Pricing and quoting More Accounts receivable Reporting and analysis
AI-ready, trusted data
  1. Step 1All your data, connected
  2. Step 2Quality checked
  3. Step 3Duplicates resolved
  4. Step 4Mapped to each AI use case
Common pain points

What holds AI back

Takes too long and costs too much
0%of AI project time can go to data prep1
AI spend that doesn’t pay off
0%of CFOs don’t see real AI value yet2
Numbers you can’t trust
0%of CFOs don’t fully trust their data2
AI answers you have to double-check
0%of finance time is spent checking AI3
Trusted, AI-ready data in days, not months

The foundation for every AI use case

Daysto AI-ready data4
90%+right first time5
100%traced to source

Internal and external data, linked and cleaned.

InternalERPCRMSpreadsheets
One connected view
ExternalPublic dataPartner data
Automatically mapped to each AI use case

Getting raw data into the shape each AI use case needs is hard

Raw data must be joined, parsed, aggregated, derived, converted and mapped to reference data, field by field and system by system.

Axyo does this automatically. Experts review the exceptions.

Illustrative example
Your raw dataTransformationTarget data model
Time and cost, per AI use case

The typical approach takes too long and costs too much

And it starts over with every new AI use case. Axyo gets each one ready in days, on one low monthly subscription.

Typical approachFor each AI use case
MonthsTime
$$$$Cost
Bought or built separatelyBusiness data catalogData quality and mapping reviewsA tool to chat with your business data

Starts over for every new AI use case

With AxyoFor each AI use case
DaysTime
$$$$Cost
IncludedFull business data catalogData quality and mapping reviews by expertsAsk Axyo: chat with your AI-ready business data

One low monthly cost for multiple AI use cases

Included in every subscription

OCRD.U_LdTmSupplier lead timeOITM.OnHandQuantity on handOCRD.E_MailContact emailMasked

Full business data catalog

Every field named and defined in your business terms, with its source, quality score and privacy tag.

Usually bought separately
Unit price 10 times the usualFixedLead time in days, 74% matchApprovedHalden Group Co.Merged

Data quality and mapping reviews by experts

Axyo’s experts and yours review the exceptions, so every fix, mapping and merge is checked before it is published.

Usually staffed separately
Who is our largest customer by revenue this year?Halden Group, at $1.02M, across its 3 customer codes.From your AI-ready data

Ask Axyo

Chat with your business data once it is AI-ready, so every answer comes from clean, connected records.

Usually bought separately
Next: The problem
Why it matters

Same question, two answers

Same records, same question. Only the data underneath changes.6

AI on your raw dataThree systems, as they are today
AI on AI-ready DataThe same records, made ready
Illustrative example
Each problem, and how we fix it

Why most data isn’t ready for AI

Some problems sit inside each system. Others only show up when you put systems together. Here is each one, and how we fix it.6

Next: How it works
How it works

The Axyo automated data pipeline

Fully automated, from your raw data to AI-ready, trusted data. You see what comes out of every step, and experts review only the exceptions.

Sample run on a mid-market manufacturer’s ERP data
Next: Get started
Get started

Get started in days

See your AI readiness score within days, on your own data.4

Day oneStart a free trial

Share files from your ERP, CRM or spreadsheets.

Within daysSee your readiness score

What AI can use now, and what needs fixing.

ThenYour first AI use case, ready

Your data, mapped and published for it.

Start your free trial
Data

What we need from you, and what we bring

From youFiles first, then read-only connections
What we bringStandard models and external data
Personal data masked before any AI model sees itRead-only access to your systemsSOC 2 Type II certifiedYour data trains only your models
Who benefits, and how it is measured

Value for every team, measured on every run

Owner and CEO
  • AI in use this quarter, not in two years
  • No long hiring plan before AI can start
Measured byTime to a new AI use caseDown
CFO and controller
  • Numbers that agree across systems
  • One low monthly cost for multiple AI use cases
Measured byData quality scoreUp
Operations and supply chain
  • One record per supplier, customer and item
  • AI that quotes, bills and collects on the right records
Measured byDuplicate recordsDown
Sales and customer service
  • Answers about customers and orders from Ask Axyo
  • Current prices on every quote
Measured byAI readiness scoreUp
IT
  • Works with your ERP and data warehouse, with nothing to replace
  • Personal data masked, every decision logged
Measured byExpert review timeDown

Where most companies stand

5%of U.S. AI decision-makers say their data is largely AI-ready9
37%paused, scaled back or abandoned an AI initiative in the past year9
63%of organizations lack, or are unsure they have, the right data practices for AI10
Questions

Questions owners ask

Try it free.

Share files from your ERP, CRM or spreadsheets, and see your AI readiness score within days.

Founders with deep data and AI experience at a Fortune 100 payments company30+ data and AI patentsComputed, not generated: every answer traces to its recordsSOC 2 Type II certified
Request a demoStart your free trialHow it works
Sources and notes
  1. 1
    Gartner, as cited in industry research on AI implementation timelines: data assessment and preparation takes 40% to 60% of the total time of an AI project. Source
  2. 2
    RGP, The AI Foundational Divide: From Ambition to Readiness (December 12, 2025; 200 U.S. CFOs): only 14% report meaningful value from AI today, and only 10% fully trust their enterprise data. Source
  3. 3
    Datarails 2026 CFO Sentiments Survey (October 6, 2026; 270 CFOs and finance leaders at U.S. companies with more than 1,000 employees and $100 million or more in revenue): finance teams spend 26% of their working week verifying or correcting output from AI tools. Source
  4. 4
    Measured from when files or read-only access are shared, and including your team’s inputs and validation. The time depends on the number of sources and records, and on how quickly your experts review.
  5. 5
    Sample Axyo run on a mid-market manufacturer’s ERP data, directional: 1.2 million records in 39 files, made AI-ready in about 3 hours end to end on the first run. AI time was minutes per step; most of the time was expert review, estimated. Later runs on refreshed data take minutes, because approved definitions, mappings and merges are reused. More than 90% of fields were mapped correctly on the first suggestion, and 1,381 supplier codes became 647 after duplicates were merged. Results depend on the data and on how quickly your experts review.
  6. 6
    The sample business, its suppliers, records, questions and answers are illustrative. Every company name and number in them is fictional.
  7. 7
    Quality checks are grouped by the six data quality dimensions defined by DAMA UK and adopted by the UK Government Data Quality Framework: completeness, uniqueness, consistency, timeliness, validity and accuracy. Source
  8. 8
    Standards the matching attributes follow: IRS employer identification numbers (EIN); the Legal Entity Identifier, ISO 17442; USPS Publication 28 for address standardization; ITU-T E.164 for phone numbers; GS1 GTIN for items; UNSPSC for categories; and the Nacha ACH record formats for the company ID on payments. EIN · ISO 17442 · USPS Pub. 28 · E.164 · GTIN · Nacha
  9. 9
    Survey of 515 U.S. AI decision-makers, reported by IT Brief (October 26, 2026): 5% say their data is largely AI-ready; 37% paused, scaled back or abandoned an underperforming AI initiative in the past 12 months, with data quality the top reason, at 24%. Source
  10. 10
    Gartner (February 26, 2025): through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data; 63% of organizations either do not have or are unsure whether they have the right data management practices for AI. Source
Illustrative examples6