POVA gives investors, M&A teams, and strategic partners an independent read of a target's own exported data, before the deal, the investment, or the partnership closes. One platform, three products: Operational & Financial, Cyber, and Data Access. Each works through the full population rather than a sample, and shows the reasoning and the source records behind every finding rather than a single black-box score.
The target's data stays under their control. POVA runs inside the data owner's own IT environment, or on a supplied offline POVA BOX appliance, and returns a signed pack the recipient can trace: ranked findings, the read of the business, data-confidence notes, and questions for management. Most reviews complete within one to two weeks; focused scopes move faster.
A short intro to what POVA looks at and what lands in the board pack.
POVA shows where reported results diverge from the underlying records: in transactions, customers, vendors, controls, and cash conversion.
Most anomaly tools flag thousands of rows and leave you to work out why. Every POVA finding carries its reasoning and the source records behind it, so a reviewer can act on it or set it aside on the merits.
Full export → connected evidence surface
POVA runs three reviews from one platform. Commission one or all three. Each works through the full population rather than a sample, and ties every finding back to the records behind it.
Reads the financial and operational export in full: general ledger and sub-ledgers, revenue and receivables, payables, payroll, inventory, and the operating records behind them.
Typical findings
Earnings quality and cash conversion, revenue and margin durability, customer and vendor concentration, working-capital movements, and segment-level performance, each supported by the underlying transactions.
Reviews security and system activity across event logs, internal records, and external third-party references, covering authentication, privileged access, and configuration change.
Typical findings
Dormant and over-privileged accounts, out-of-policy and after-hours access, change-management bypasses, segregation-of-duties conflicts, and audit-trail gaps.
Maps how data moves and who reaches it, using access and transfer logs together with internal and external third-party references.
Typical findings
Over-broad permissions, unusual access and transfer patterns, exposure through partners and vendors, and gaps between stated policy and actual access.
Each product returns the same deliverable: ranked findings, every one tied to the source records behind it, in a signed pack the recipient can trace. Dedicated pages for each are in progress.
A company reports as one business, but the data usually shows several inside it: a durable core, a cash-generative unit, a margin drag, a fast-growing segment that leans on a handful of customers. The Operational & Financial review tells them apart.
Margin durability, period-end behavior, cash conversion, and the signals behind reported numbers.
Concentration, retention, payment behavior, renewal exposure, and weak-cash customers.
A fixed fingerprint per segment: growth, margin, cash conversion, concentration, and data confidence.
Strengths, fragile areas, management questions, watchlists, and 100-day actions.
POVA does not assert fraud or confirmed loss. Each finding is routed to one of three places: ready for review, check the context first, or monitor over time.
Use when the pattern is strong enough for immediate row-level review by finance, internal review, or operations.
Use when the signal may be legitimate but needs policy, contract, or operating context.
Use for weaker, incomplete, or trend-only signals that should stay in the control conversation.
Gather structured files from your existing systems. POVA runs inside your own IT environment, or on a supplied offline POVA BOX appliance — no live access, no connectors, no production credentials — and retains nothing once the report is delivered.
Deterministic detectors, statistical methods, and cross-source logic inspect the full supplied population and build ranked candidates.
Receive a board-readable pack with financial quality, segment DNA, ranked findings, evidence references, and review actions for the owning teams.
Findings become board and review cases, each tied to source
The report separates the business summary, the evidence, and the technical trace, so the board reads the summary while reviewers can still reproduce the work.
Earnings quality indicators, revenue durability, customer quality, working-capital behavior, and control overlay.
Total-company and per-segment fingerprint showing growth, margin quality, cash conversion, concentration, and data confidence.
Clear headline cases with amounts, transaction counts, owners, review actions, and evidence references.
Follow-up questions for management, next-quarter watchlist items, and optional 100-day or restructuring implications.
A short cross-section of what scans have surfaced in practice. Industry context retained; identifiers removed.
Identified dormant admin accounts with elevated privileges retained after role changes. Found after-hours system access bypassing change management. Detected audit-trail gaps.
Discovered significant variance in first-visit resolution rates. Found repeat-visit patterns indicating training gaps. Identified parts-inventory mismatches.
Outlined a regime change where significant earnings and inventory adjustments appeared inconsistent. Identified where data manipulation likely occurred, with dates, amounts, and actors.
Identified subcontractors with divergent billing. Found recurring maintenance issues. Detected work-order timing patterns suggesting padding.
Mapped reliability scores across data sources. Found systematic lag patterns. Detected transformation errors during integration.
Allocated network, capex, and customer-service costs to actual segment usage rather than blended assignment. Surfaced cases where reported segment margin reversed once infrastructure was fully allocated, regional capex amortizing against revenue it did not generate, and growth segments structurally subsidized by mature ones.
Industry, findings, and actions are described at a level of generality that protects engagement confidentiality. Specifics available under NDA.
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Use POVA when management's reported numbers need to be tested against the records underneath them.
| Question | POVA | Typical dashboards | Internal analytics projects | Monitoring platforms |
|---|---|---|---|---|
| What is reviewed? | ✓The full supplied export across financial, customer, vendor, operational, and control data where available | Known views and selected metrics | Depends on project scope and analyst capacity | Configured event streams, rules, or models |
| What is the unit of analysis? | ✓Total company plus segment-level DNA | Usually aggregate metrics and dashboards | Can be built, but usually as a separate project | Usually alerts, entities, or events |
| Why does a finding matter? | ✓Each finding includes a reason, source rows, and review action | Metrics require interpretation | Depends on documentation and analyst notes | Often alert-first, context added later |
| How is it delivered? | ✓Signed board pack, evidence pack, and traceable outputs | Dashboard or management report | Slides, spreadsheets, or notebooks | Dashboard, alert queue, or workflow |
| Best use | ✓M&A and investment diligence, strategic-partner vetting, board and owner review, and recurring deep scans | Recurring management visibility | Custom analysis and internal exploration | Operational monitoring and exception handling |
POVA does not replace your auditor or your investment-committee judgment, and does not produce a regulated audit opinion. The deliverable is a data-driven business-quality review intended to inform formal audit, valuation, or accounting work performed by licensed firms — not replace it.
Test the story behind the deal, find fragile segments and control gaps, and turn findings into management questions before close.
Vet a target's financial durability, cyber posture, and data exposure before integrating systems, customers, or brands.
Run the same diligence-grade scan on your own business ahead of a raise, sale, or board cycle.
Move from samples and dashboards to full-population cases, segment-level action, and findings you can trace to source.
