The Real Failure Point
Most AI projects fail for a reason no one looked at before they started — not the model, but the data underneath it.
Don't build AI on data you haven't checked. Most AI initiatives don't fail at the model — they stall on the data underneath it: incomplete, siloed, or unlabeled, discovered months and a budget cycle too late.
Cheap now. Costly later.
A data problem costs almost nothing to fix before you build on it — and a fortune to fix after. The audit catches it while it's still the cheap kind.
Five Signals Decide Whether Your Data Is AI-Ready
Our data & AI readiness assessment works through each signal, then turns the findings into a documented gap analysis and a roadmap - not vague maturity colors, but specifics you can take to a budget meeting.
Completeness
"Do we have enough of the data, with the gaps accounted for?"
Coverage and volume across the records and fields that matter. Whether the data exists in usable quantity — and where the missing values and blind spots are.
Quality
"Is it clean enough to learn from?"
Accuracy, consistency, and labeling. The difference between data you can train on and data that quietly poisons every model built on it.
Structure
"Is it organized in a way a model can use?"
How the data is formatted, typed, and described. Whether it's structured and documented, or a sprawl of free text and ad-hoc formats no pipeline can read cleanly.
Connectivity
"Does it join up, or live in silos?"
How well datasets link across systems. Whether records from different sources can be tied into one coherent view — including data from existing IoT and operational systems — or sit disconnected and un-joinable.
AI/ML suitability
"Can this data actually support the AI you want?"
The signal that ties the other four to a result. Whether your data — as it stands — can support the specific analytics, forecasting, and AI/ML use cases you're targeting, and exactly what's missing if it can't.
Fixed Scope, Fixed Price, and an Honest Answer at the End
A defined engagement, not an open-ended consulting relationship. Here's what working with us on the audit actually looks like
SCOPE & PRICE
You get the scope, timeline, and price up front. No open-ended discovery, no meter running — a defined engagement with a defined end.
YOUR TEAM
A few interviews with your data, engineering, and business leads, plus read access or representative samples of the data in scope. We take it from there.
METHOD
Our engineers profile your actual data and systems — sampling, mapping, pressure-testing. The findings come from the data itself, not a self-assessment form.
OUTCOME
You get a clear go / fix-first / not-yet recommendation. "Not yet" is a valid result — and it saves you a far larger build budget.
Book your audit and get a free 1-hour call with our data expert
A full hour on the phone — your goals, your data, and exactly what to do next.
Three Phases, Each With a Document You Can Act On
Not a slide of green dots — real reports, data maps, and a roadmap you can hand to your data team, your CFO, or your board
Business processes & data context
How decisions get made today, which systems and data sources feed them, and what's blocking your business goals.
Map of processes, data & decision points
One picture of how data moves through the business — and where the leverage is.
AI/ML use-case scenarios, tied to your KPIs
Candidate use cases mapped to business goals and key metrics, plus the hypotheses we'll test.
Data audit report with full data mapping
The structure of your data described and mapped, end to end.
Quality, completeness & connectivity analysis
Whether your data can actually support analytics, forecasting, and AI/ML — assessed, not guessed.
Gaps, risks & limitations — with fixes
Where the data falls short, and concrete recommendations to improve how you collect, store, and manage it.
Feasibility & expected-impact assessment
For each candidate AI initiative — what's realistic, what it's worth, and any extra data you'd need.
Conceptual target architecture
A high-level design for the solution your data and goals point to.
Prioritized AI/ML scenarios, rated by complexity
Use cases ranked by feasibility and implementation effort, so you know where to start.
What Ready Data Makes Possible
The audit is the start. Here's where it leads when the data can actually carry AI — real systems, real numbers.
Book your audit and get a free 1-hour call with our data expert
A full hour on the phone — your goals, your data, and exactly what to do next.
The People Who Own the "is our data ready?" Question
Chief Data Officers & Heads of Data
"Is our data enough to support AI?"
Get a defensible answer with numbers, not a hunch — and a roadmap to close whatever's missing.
Heads of AI & AI Strategy leaders
"Where do we start, and what pays back?"
A ranked use-case shortlist tied to ROI, so the first AI project is the one most likely to land.
CTOs & VPs of Engineering & Data Architects
"What has to change in our stack?"
A clear view of the pipeline, integration, and governance work that has to happen before models go near production.
Why HQSoftware
Data-first since 2001 — before AI was the headline.
We've been building data and software systems for over two decades. The audit is how we make sure we — and you — start in the right place.
- 24+ years and 450+ delivered projects across manufacturing, retail, logistics, and more.
- 125+ engineers across offices in Tallinn, New York, and Warsaw.
- We handle the data and software layer - including on top of the IoT devices you already run.
- Agile and ISTQB certified teams. JIRA and Slack transparency, and an NDA from day one.
Find out if your data can carry AI — before you bet a build budget on it.
Tell us a little about your setup. We'll come back with scope, timing, and a fixed price.
Sergei Vardomatski
Founder