A clear verdict on your AI readiness.
Two weeks. $15,000.
Before your team spends another quarter on AI -- know whether your stack can support it. A scored assessment across 5 dimensions. If you're ready, we scope the first build. If not, you get exactly what to fix and in what order.
AI on bad data doesn't fail loudly.
It fails quietly.
of organizations admit their data isn't truly ready for AI -- but most are deploying anyway. Gartner, 248 data management leaders
of enterprises say their data is completely ready for AI. The other 93% are guessing. Cloudera / HBR, 2026
ROI on the AI strategy deck sitting in your Google Drive. 42% of companies abandoned most AI initiatives in 2025. A scored verdict is not a presentation. S&P Global, 2025
95% of AI pilots never made it to production in 2025 -- not because the technology failed, but because they were deployed on unstable ground. The question isn't whether AI works. It's whether your data can hold the weight.
The AI Stack Audit: 5 dimensions. 1 score. 90-day roadmap.
The audit scores your readiness across the 5 dimensions that determine whether AI agents will succeed or stall in your organization. Every score is backed by evidence from your actual systems -- not a questionnaire.
Data Foundation
Is your data warehouse structured, tested, and documented? Do you have a transformation layer (dbt or equivalent)? Can your data models support AI workloads?
Warehouse + dbt + data modelsInfrastructure Maturity
Is your cloud infrastructure code-managed (Terraform)? Do you have CI/CD pipelines? Can you provision and tear down environments reliably?
Terraform + CI/CD + cloud governanceData Quality
How reliable is your data? Do you have automated testing? What's your data freshness SLA? Are metric definitions consistent across teams?
Testing + freshness + consistencyOrg Readiness
Does your team have the skills to maintain AI agents post-deployment? Is there executive sponsorship? Are stakeholders aligned on AI use cases?
Skills + sponsorship + alignmentUse-Case Viability
Which of your AI ambitions are realistic given your current stack. Use cases are prioritized by feasibility, impact, and data readiness. You'll know which to build now, which to defer, and which to drop entirely.
Feasibility + impact + prioritizationWhat you walk away with
AI Readiness Scorecard
A scored assessment (1-5) across all 5 dimensions. Clear, objective, backed by evidence from your actual systems. No hand-waving.
Gap Analysis
Specific gaps identified in each dimension with severity ratings. You'll know exactly what's blocking your AI ambitions and how critical each gap is.
90-Day Roadmap
A prioritized, actionable plan: what to fix first, what it costs, and what AI capabilities each fix unlocks. Scoped, priced, and sequenced.
Use-Case Prioritization
Your AI wish list ranked by feasibility and impact. You'll know which agents are buildable now, which need foundation work, and which aren't worth it.
Honest Verdict
If your stack is ready: the next step is scoping the first build. If it's not: you get a sequenced list of exactly what to fix, in what order, with time and cost estimates. A clear path either way -- no upsell pressure.
Executive Summary
A board-ready summary you can present to leadership. Plain language, business impact framing, clear next steps. Built for decision-makers, not engineers.
2 weeks. Start to finish.
Not a 3-day workshop that generates more workshops. A scored assessment of your actual systems, delivered in two weeks.
Assess
- Kickoff call with stakeholders
- Access to data warehouse, pipelines, infrastructure
- Audit data models, test coverage, pipeline reliability
- Review infrastructure (IaC, CI/CD, cloud governance)
- Stakeholder interviews (data team, leadership)
Score & Deliver
- Score all 5 dimensions (1-5 scale)
- Identify and severity-rank gaps
- Prioritize AI use cases by feasibility
- Build 90-day roadmap with cost estimates
- Present findings to leadership -- with a clear verdict and the path forward
You've probably considered these options.
A large consulting firm
They scope it with a senior partner, build it with a junior analyst, and hand off a deck when the engagement ends. "The consultants had already moved on to their next client" -- that's not a hypothetical, it's what data teams report, consistently. The audit is delivered by the same practitioner who assessed your stack. No hand-offs.
An internal review
Your team knows the stack best. They also have three other projects, no external authority with the board, and no structured scoring methodology. The audit gets started, paused, and tabled. You need a verdict, not a long weekend with a spreadsheet.
Waiting for a better time
The board question doesn't go away. 42% of companies abandoned AI initiatives in 2025 -- not because they lacked ambition, but because they committed before checking whether the foundation could hold it. Two weeks now is cheaper than six months of stalled engineering later.
Is this you?
This is for you if:
- Your board is asking about AI and you don't have a clear answer
- You want AI agents but suspect your data isn't ready
- You've tried AI initiatives that stalled or underdelivered
- You're a company with $5M-$200M revenue
- You have a cloud data warehouse (or plan to adopt one)
- You need a clear, honest assessment -- not a sales pitch
This is NOT for you if:
- You're pre-seed with no data infrastructure at all
- You already have a mature 10+ person data + AI team
- You're looking for someone to build a software product
- You need hourly consulting, not a fixed-scope engagement
- You don't believe AI is relevant to your business
What it looks like when it works.
"The diagnostic confirmed the data foundation was there. We scoped the automation in week 3. The Monday reporting workflow that had taken 3 hours every week was running automatically in 5 days."
"Two agent deployments had been blocked for a quarter -- the team knew something was wrong with the data layer but not what. The diagnostic named the specific gaps. Six weeks of foundation work later, both agents were in production."
$15,000. Published.
A verdict in 2 weeks -- not a 3-day workshop that creates more workshops.
- A clear verdict: AI-ready to build, or here's exactly what to fix first
- Scored gaps with a prioritized fix sequence -- no guessing which problem to solve first
- A 90-day roadmap with cost estimates your board can approve the same week
- An AI use-case ranking: build now, defer, or drop entirely
- Board-ready summary you present with confidence
- Live leadership presentation -- not a slide deck in an email
- Direct access to the practitioner -- no account managers, no junior hand-offs
That's less than one month of a mid-level data hire. And you'll know exactly where you stand in 2 weeks.
A failed AI initiative costs 6+ months of engineering time. The audit tells you whether you're set up to succeed before you commit that time.
Every other firm makes you fill out a form to find out if you can afford them. The price is $15,000. It's on this page. More on how this compares to alternatives.
Based in India? Reach out directly for INR pricing. info@mldeep.io
A roadmap only matters if someone owns the first 90 days.
The audit can end cleanly with your team owning the roadmap. If your team is already overloaded, MLDeep can stay on as the implementation owner: a monthly retainer scoped on the fit call, 3-month minimum, one active build lane at a time.
Foundation repair or first build
- Turn the top audit finding into a scoped build lane
- Close the highest-risk data, dbt, reporting, or workflow gap
- Document decisions and handoff as work ships
Production execution
- Build the next workflow, agent, or data foundation improvement
- Keep one accountable senior technical owner on the backlog
- Decide whether to renew, pause, or hand off in week 10
Book your AI Stack Audit
Start with a 15-minute fit call. If the audit is the right move, you'll have a verdict on your data readiness in two weeks.
What Happens Next
Free 30-min discovery call to confirm fit. If it's a go, we kick off within one week.
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