AI-assisted workflows reconcile and structure fragmented financial data into QoE and transaction-ready analysis.
Built for lower middle-market transactions where speed, clarity, and cost discipline matter.
Workflow infrastructure currently being expanded with pilot clients.
Most lower middle-market diligence delays happen before analysis even begins. Deal teams lose weeks:
By the time the data is usable, deal timelines are already compressed, budget is partly consumed — and the risk of missing something material has increased.
Proprietary normalization workflows and AI-assisted reconciliation compress the data preparation layer: senior judgment is applied to QoE and financial analysis, not cleanup.
We accept financial data in any format — no pre-cleaning required on your end.
Proprietary workflows reconcile and normalize data across sources — preserving traceability.
Senior-reviewed analysis built on clean inputs — delivered as IC-ready workbooks with clear findings.
A full QoE delivered in days, at a fixed fee. Below what you'd pay a Big 4 firm. More rigorous than what most regional alternatives produce.
Normalized EBITDA, revenue quality, non-recurring items, and accounting irregularities — with a management team interview to understand what's really driving the numbers.
Fragmented source files reconciled into a structured, decision-ready dataset with full source traceability.
No 100-page decks. An Excel workbook and summary with QoE findings, key issues, and deal considerations — ready to present to your lender or investment committee.
Concentration risk, revenue stability vs. attrition, hidden customer dependencies. Structure raw billing files into usable revenue intelligence.
NWC peg and normalized level. Identifies working capital trends and normalizations that directly impact the purchase price mechanism.
Compensation, technology, and rent normalized under your ownership structure. Synergies and gaps that feed directly into your purchase price.
Concentration risk, revenue stability vs. attrition, hidden customer dependencies. Structure raw billing files into usable revenue intelligence.
NWC peg and normalized level. Identifies working capital trends and normalizations that directly impact the purchase price mechanism.
Compensation, technology, and rent normalized under your ownership structure. Synergies and gaps that feed directly into your purchase price.
Deep-dive AUM and client analysis for Wealth Management and RIA add-on acquisitions. Improve book evaluation, deal structuring, and earnout design.
Organic vs. Inorganic Net flows, and client acquisition/attrition metrics by period. Separates organic growth from market appreciation — critical for defending AUM-based revenue projections at closing.
Advisor-level AUM concentration, productivity flags, and departure scenario modeling. Quantifies the AUM at risk if a key advisor leaves — directly informs earnout design and retention structures.
Client-level AUM concentration, tenure cohort analysis, and retention patterns. Identifies top-client dependency and distinguishes sticky from transactional AUM — the quality layer beneath the headline number.
Current AUM × fee tier waterfall to a defensible run-rate revenue figure. Accounts for fee schedule breakpoints, blended rates, and AUM mix shifts — the foundation of any RIA valuation model.
Organic vs. Inorganic Net flows, and client acquisition/attrition metrics by period. Separates organic growth from market appreciation — critical for defending AUM-based revenue projections at closing.
Advisor-level AUM concentration, productivity flags, and departure scenario modeling. Quantifies the AUM at risk if a key advisor leaves — directly informs earnout design and retention structures.
Client-level AUM concentration, tenure cohort analysis, and retention patterns. Identifies top-client dependency and distinguishes sticky from transactional AUM — the quality layer beneath the headline number.
Current AUM × fee tier waterfall to a defensible run-rate revenue figure. Accounts for fee schedule breakpoints, blended rates, and AUM mix shifts — the foundation of any RIA valuation model.
Senior judgment should be applied to analysis, not reconciliation and data cleanup.
We reconcile and normalize data before analysis begins. No conclusions built on faulty assumptions. Every output is traceable to the source file.
Internally developed workflows identify non-recurring items, normalize P&Ls, and accelerate QoE building — flagging irregularities and structuring outputs in a fraction of manual time.
Not just automation. Senior professionals validate every output, go deep on complex items, and take full ownership of the work product.
No hourly billing surprises. A flat retainer sized for lower middle-market deal economics — aligned incentives, no scope creep.
Compress weeks of diligence into days. Move from LOI to Close faster and with more conviction than your competition.
One platform with all Deal Intelligence in one place. Findings translate directly into modeling decisions, legal implications and deal term considerations — dynamically.
Designed for confidential financial diligence workflows.
All client files are encrypted at rest and in transit. No raw financial data is stored beyond the engagement lifecycle.
Built on AWS with controlled access, structured auditability, and isolated environments per engagement.
Client financial data is never used to train or fine-tune any AI model. Your deal information stays yours.
Engagement-level access controls and document audit trails under development.
10 years at PwC leading financial due diligence on M&A transactions.
Deal timelines weren't compressed by analysis — they were compressed by data preparation. Weeks lost reconciling exports, tracing balances, and normalizing messy trial balances before any real analysis could begin.
diledge.ai uses internally developed workflows to eliminate that layer, accelerate deal execution, and apply senior judgment where it actually matters: understanding the business, calling the adjustments, and telling you the truth about what you're buying.
Currently onboarding a limited number of pilot engagements. If you have a deal in diligence or heading toward LOI, I'd like to hear about it.
Or email directly: thomas@diledge.ai