Michael Wibracht Founder & Principal | AllDark.HorseSan Antonio, Texas
Executive Overview
Michael Wibracht is a 25-year real estate development veteran and AI solutions architect based in San Antonio, Texas. As former Founder, Managing Partner, and President of 210 Development Group and Mission DG, he led the strategy, capitalization, and execution of transformative projects spanning adaptive reuse, urban infill, and complex public-private partnerships. He holds a BBA in Management Information Systems from Texas A&M University, College Station — combining formal technology training with deep industry expertise.
Through AllDark.Horse, his AI solutions studio, Michael builds purpose-built AI financial tools for the real estate development and affordable housing finance industries — bringing a rare combination of hands-on deal experience and technical capability that no generalist AI firm can match.
Development Track Record
- $1B+ in construction and development overseen
- 6,000+ multifamily and affordable housing units delivered or in development
- $405M total development investment
- $140M in historic adaptive redevelopment
- $114M LIHTC eligible basis
- $31M in Federal Historic Tax Credit equity
- Deep expertise in HUD 221(d)(4), HUD 223(f), LIHTC, Fannie Mae DUS, and large-scale construction finance
AI Tools & Technology
AllDark.Horse designs and operates a suite of AI-powered intelligence platforms purpose-built for affordable housing and adaptive reuse finance. Four of these platforms have been submitted to the U.S. Patent and Trademark Office as provisional patent applications (April 2026), covering 101 claims across multi-source data pipelines, automated underwriting, architectural layout generation, and cost estimation. ALLDARK.HORSE and DIGITAL TWIN ENGINE trademarks are pending.
The Digital Twin Engine is the unifying platform layer — live pipeline intelligence with FRED API rate integration, automated daily re-underwriting, viability scoring (0–100), and tiered email alerts. A FastAPI REST backend (12 endpoints) and React dashboard expose sensitivity analysis, NMTC modeling, and a layered soft-source gap filler covering deferred developer fee, PBV, HOME, CDBG, TDHCA, local gap/TIF, FHLB AHP, and NMTC sources. Containerized with automated rate cycles, weekly Excel exports, and SSL-secured multi-tenant deployment.
Nirmata Core automates the architectural and cost-estimation side of LIHTC and adaptive reuse development. Native DWG/DXF ingestion converts construction documents into structured building geometry without manual takeoff. From that geometry, the platform generates code-compliant residential unit layouts (including loft mezzanines), detailed interior unit plans for every unit type on every floor, and dual-format construction cost estimates that simultaneously output LIHTC development cost schedules and CSI MasterFormat division breakdowns from a single computation.
The Kasati is an authenticated multi-source opportunity pipeline that surfaces LIHTC and adaptive-reuse development targets from government parcel records, federal overlays (QCT/DDA/Opportunity Zone), tax foreclosure notices, and market listings. Records are fused across sources via normalized-address fingerprinting with per-attribute source provenance preserved, then scored against a multi-dimensional viability rubric. The platform operates exclusively on authenticated APIs and user-initiated data transfers — no web scraping, no terms-of-service violations.
Foundational financial models underpin the platform layer:
- 10-Year Treasury Predictor with HUD Pricing — automated daily FRED data pull with three-model forecast engine (momentum, mean reversion, volatility-adjusted), calibrated HUD 223(f) and 221(d)(4) spreads, weekly email delivery
- HUD 223(f) Loan Sizer — DSCR-constrained mortgage sizing with current MIP (25 bps flat, per FR-6522-N-02), LTC caps, and debt yield analysis
- HUD 221(d)(4) Loan Sizer — 40-year amortization, 1.11x DSCR, 90% LTC constraint, construction-period interest modeling
- LIHTC Financial Model — full 4% and 9% credit underwriting with eligible basis calculation, applicable fraction, 10-year credit stream, investor pricing, Texas State LIHTC (HB 1058) layer, and TDHCA-format Development Cost Schedule
- Fannie Mae Small Loan Sizer — side-by-side Freddie Mac SBL and Fannie Mae comparison with LTV/DSCR constraints and rate lock analysis
Technical stack: Python (FastAPI, pandas, NumPy), React/JavaScript, REST API design, Docker containerization, OAuth 2.0 authentication, FRED API integration, automated email delivery, nginx reverse proxy with SSL, cron-based scheduling, PostgreSQL, libredwg and ezdxf for CAD pipeline ingestion, and full-stack cloud deployment on DigitalOcean/Ubuntu infrastructure.
Domain specialization: AI-powered underwriting for LIHTC (4% and 9%), HUD 221(d)(4) and 223(f), Federal and State Historic Tax Credits, New Markets Tax Credits (NMTC), Project-Based Vouchers (PBV), and layered affordable housing capital stacks including HOME, CDBG, TDHCA soft debt, FHLB AHP, TIF/TIRZ, and seller financing. Purpose-built for the complexity that generalist AI platforms cannot address.
Professional Standing
Mr. Wibracht maintains full eligibility to participate in federally related and regulated transactions and is not suspended, debarred, or restricted from engaging in projects involving institutional or government-related capital.
