From Code Repositories to Investment Committee Decisions
We replace subjective management interviews and superficial tick-box surveys with deterministic, static code forensics across 22 acquisition pillars. Here is our 10-day structured workflow.
Phase-Gated Forensics Roadmap
Each phase produces concrete verification artifacts designed to eliminate deal blind spots before binding signatures.
Client Parameter Ingestion & Repository Clean-Room Setup
Establishing secure client onboarding, ingesting transaction parameters, and isolating target repositories into a secure offline environment to construct a localized Code Property Graph (CPG).
- ›Bilateral execution of non-disclosure agreements and forensic access covenants.
- ›Client onboarding and parameter gathering: provisioning read-only repository URLs, declaring the deal thesis archetype (e.g., scale, integration, IP moat), and providing financial modeling assumptions (proposed valuation price, regional engineering rates).
- ›Secure, local repository mirroring and parsing across 11+ supported languages (TypeScript, Python, Go, Java, Rust, C/C++, C#, PHP, Ruby, etc.).
- ›Construction of a localized mathematical mapping of symbols, cross-file references, and dependency relationships.
- ›SLOC inventory, framework cataloging, and generation of a deterministic cryptographic audit lineage fingerprint.
Static Forensics, CFG-Grounded Taint Flow & Security Auditing
Executing static analysis engines across 22 M&A pillars—including secrets detection, CVEs, IaC misconfigurations, and CFG/DFG-grounded taint tracking—without code execution.
- ›Shallow and deep control-flow graph (CFG) construction with data-flow graph (DFG) cross-file stitching for critical attack surface files.
- ›Source-to-sink taint tracking mapped to Common Weakness Enumerations (CWEs) across public endpoints, payment rails, and database queries.
- ›Supply chain and direct/transitive dependency auditing against OSV.dev and PyPI/npm/endoflife.date registries.
- ›Automated license identification (GPL, AGPL, copyleft) and IP ownership tracking across commit histories.
Architectural Entanglement & Human Capital Forensics
Mining git commit histories and codebase metrics to map bus factor, key-person risk, churn rate, and technical debt without overburdening target developers.
- ›Quantifying structural complexity, test deficit, coupling entanglement, and technical debt across modules.
- ›Git commit log mining over repository history: Bus Factor calculation, sole-maintainer concentration, and key-person risk tracking.
- ›Execution of targeted, auto-generated interview questionnaires derived directly from static code findings to validate unverified risks.
- ›Assessing AI/ML code exposure, documentation staleness, and CI/CD maturity.
Deal-Thesis Financial Translation & Artifact Delivery
Translating technical findings into hours-per-unit (HPU) labor models, multi-scenario deal economics aligned with client financial inputs, EBITDA/valuation adjustments, and actionable 180-day integration plans.
- ›Mathematical modeling of remediation costs and Valuation Scenarios ( Pre-Close, Post-Close, Partial, Downside VaR).
- ›Structuring specific deal safeguards, escrow holdbacks, and transaction economic impact bridges for deal counsel based on client deal-thesis targets.
- ›Generating client-ready executive reports, board-level decision briefs, and a ~50+ sheet due-diligence Excel workbook.
- ›Providing post-acquisition packs including a 180-day integration plan, interactive Gantt chart, knowledge transfer plan, and remediation schedule.
Traditional IT Checklists vs. MTD Deterministic Forensics
Why conventional consulting firms miss critical post-close balance sheet traps.
| Diligence Dimension | Traditional IT Advisory | MTD Services Forensics |
|---|---|---|
| Primary Data Source | ✕Self-reported CTO questionnaires & subjective slide decks | ✓Offline parsing, mathematical object making, and git log mining |
| Security & Vulnerability | ✕High-level policy review ('Do you use 2FA?') | ✓CFG-grounded CWE taint flow tracing unvalidated inputs directly to sensitive data/payment sinks |
| Key-Person & Team Risk | ✕Headcount chart in data room & organizational hierarchy | ✓Git commit log mining, bus factor calculation, and sole-maintainer concentration metrics |
| IP & Licensing Integrity | ✕Legal representations & warranties boilerplate | ✓Automated license detection and dependency scanning for copyleft (GPL/AGPL) contamination |
| Financial Connection | ✕Vague risk ratings ('Yellow', 'Red', 'High') | ✓Quantified HPU remediation budgets, 4-scenario economics, EBITDA/EV adjustments, and sensitivity grids |
| Target Team Friction | ✕Days of repetitive interviews distracting developers | ✓Zero target engineer burden during scanning; targeted auto-generated questionnaire reserved for Phase 3 |
Secure Offline Diligence Protocol
Target company source code is trade-secret intellectual property. Our offline, zero-code-execution infrastructure guarantees complete confidentiality, zero data contamination, and strict auditability.
Deterministic Offline Processing
All static analysis tools execute locally without invoking or executing target binary/source code, operating completely offline or in isolated network environments.
Zero Generative AI / Public LLM Risk
Target code is never uploaded to external Generative AI models or third-party APIs. Analysis is entirely deterministic, rule-based, and local.
Auditable Cryptographic Lineage
Every metric, finding, and dollar figure in the financial model is linked via a deterministic lineage fingerprint back to specific code files, commit hashes, and rule executions.
Deploy Our Evidence-Led Methodology On Your Target
From 48-hour pre-LOI red flag screenings to full 10-day confirmatory diligence, we mobilize within 24 hours of NDA execution.