Fix the Data.
Grow the Value.
We help PE-backed portfolio companies find and fix the data problems that erode valuation, stall AI, and slow growth.
Bad data costs you whether you sell or hold.
Most portfolio companies can't reproduce last quarter's revenue from source systems in under an hour. Finance, sales, and ops report different numbers for the same metric. Critical workflows run through spreadsheets maintained by one person. This isn't just a diligence problem. It slows every operating decision, undermines AI initiatives, and erodes the value you're trying to build.
Finance, sales, and ops all report different numbers. Close enough for a board meeting. Not close enough for real operating decisions or diligence.
Critical processes run through Excel files maintained by one person. Every growth initiative stalls while someone rebuilds the same data manually.
The operating plan says "deploy AI." The data says "not yet." Without clean, governed data, AI projects burn budget and deliver nothing.
We make your data an asset, not a liability.
Not with dashboards. With reconciled numbers, documented lineage, and a data foundation that supports real operating improvement and withstands buyer scrutiny.
Graeme Crawford
Founder and CEO
Graeme Crawford spent 20 years leading data programs at Fortune 100 scale. At Capital One, he led a cloud migration that enabled the closure of legacy data centers, saving hundreds of millions. He built a real-time web analytics platform with sub-millisecond latency that powered personalization and fraud decisioning across billions of transactions.
Before that, at IBM, he was the designated fixer for hostile recoveries and severely damaged implementations. Now he applies that same discipline to mid-market PE-backed companies building value and preparing for exit.
View LinkedIn →Built for PE timelines, not the consulting calendar.
- 12-month roadmaps that outlast the deal timeline
- Platform implementations that solve the wrong problem
- Dashboards that can't survive a follow-up question
- Junior teams running enterprise playbooks at mid-market scale
- AI projects launched on top of broken data
- 90-day sprints aligned to your value creation plan
- Fix the 10 numbers that matter most to the business first
- Every deliverable built to drive decisions and withstand scrutiny
- Senior operators with Fortune 100 and deal-side experience
- Weekly demos and measurable outcomes, not status updates
The data problems that slow deals, erode valuation, and block growth.
We work with PE firms, portfolio companies, and transaction advisors on the issues that surface in diligence, stall integrations, and cost multiples at exit.
Revenue that doesn't reconcile
Finance reports one number. The CRM says another. The warehouse says a third. When a buyer asks "what's your MRR?" and the answer changes depending on who they ask, it creates transaction risk.
The business runs on spreadsheets
Core operations across dozens of Excel files linked by macros and tribal knowledge. One person leaves, the reporting breaks. Modern infrastructure costs a fraction of what mid-market companies expect.
Diligence surfaces problems too late
Eight weeks in, the target's customer data is unreliable or financial reporting can't be reproduced from source systems. The deal slows, the price adjusts, or the deal dies.
Data migrations fail at the last minute
Go/no-go meeting. The data team says "we can't move this across." The migration stalls, the timeline blows out, and the integration gets expensive.
Post-acquisition integration without a common language
Two companies merge. "Active customer" means one thing in Company A and something else in Company B. Consolidating reporting is impossible until someone reconciles the data model.
Leadership can't agree on what to measure
The CEO says growth. The COO says efficiency. The CFO says margin. None of them can point to a dashboard that tells the story. The technology follows the strategy, not the other way around.
AI initiatives stall because the data isn't ready
Every portfolio company wants to "do AI." But the underlying data is fragmented and ungoverned. AI amplifies whatever it's fed. If the data is wrong, the AI is confidently wrong.
No one owns the data
Data flows through every team but no one is accountable for quality, accuracy, or availability. When something breaks, everyone points at someone else.
Enterprise platforms seem out of reach
Mid-market companies assume modern data platforms are too expensive. We stood up a cloud warehouse with hundreds of millions of rows for under $10,000 a year in platform costs.
The exit is 18 months out and the data isn't ready
The PE sponsor wants to exit, but the data can't support the story. Customer counts are unreliable. Revenue attribution is manual. Buyers will find this, and it will cost multiples.
Data Readiness Assessment
4-6 weeksOn-site discovery. Score data maturity across six dimensions. Identify constraints. Deliver a prioritized remediation roadmap.
Implementation
60-90 daysData warehouse buildout, pipeline automation, governance frameworks, BI and reporting. Fortune 100 experience at mid-market speed.
Ongoing Support
MonthlyPost-implementation monitoring, optimization, and knowledge transfer. The capability stays after we leave.
What clients say
"They transformed our data in clear, automated insights that drive real business decisions. Their combination of big platform experience and practical, actionable results is uniquely invaluable."
"Working with them is both memorable and impactful, with dividends well beyond the initial scope of any engagement. I wholly recommend their partnership."
"They will come to the table as a true thought partner at the onset of your process, bringing reliable, well-managed systems that ensure your company truly shows the receipts."
"They helped us pioneer brand new technology to turn complex data into clear business value. If you want to unlock the real potential in your business data, they are the partner you need."
Inside the Data Room
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Get InsideQuestions
How long does this take?
Our standard engagement is a 4-week Data Readiness Assessment followed by a 90-day sprint. The assessment identifies the 2-3 constraints doing the most commercial damage. The sprint fixes the primary one. Most clients see measurable improvement in data defensibility within 90 days of starting.
We're not planning to sell for 3-5 years. Is it too early?
It is never too early. The companies that fix their data during the hold period make better operating decisions, get more from AI investments, and trade at premium multiples when they do go to market. The work that makes data defensible for exit is the same work that makes it useful for growth.
When should we start relative to our exit timeline?
12 months before exit is ideal. 6 months is tight but workable. 3 months is emergency triage. The earlier you start, the more you can fix and the less it looks like you are cleaning up for a sale. Buyers can tell the difference between genuine operational improvement and last-minute window dressing.
We already have a data team. Why do we need outside help?
Your internal team knows the business. We know what PE firms and buyers look for. The gap is usually not technical skill. It is knowing which data points matter most for value creation and what "defensible" looks like when the pressure is on. We work alongside your team, not instead of them.
How much will this cost?
The Data Readiness Assessment is a fixed fee in the low-to-mid five figures. Follow-on sprints are scoped and priced based on the assessment findings. Every engagement has a defined scope, timeline, and deliverable. No open-ended retainers.
How do you work with our existing advisors and bankers?
We complement them. Your banker tells the equity story. Your accountant runs QoE. We make sure the data underneath both of those survives scrutiny. We have worked alongside investment banks, QoE providers, and legal teams. Our deliverables are built to support theirs, not compete with them.
Can you help with AI readiness?
Every AI initiative lives or dies on the data underneath it. We assess your data quality, governance, and pipeline readiness before you invest in models or platforms. Most companies that come to us after a failed AI project discover the root cause was data, not the technology.
Find out where your data is holding you back.
Ten questions. Two minutes. You'll know whether your data is ready to support growth, AI, and buyer scrutiny.
Free Data Valuation Score