Active: 2026 UAD 3.6 Production Window

Enterprise Data & Compliance Pipelines

We engineer lightweight, containerized microservices and resilient data architectures that insulate core valuation networks from structural regulatory updates.

Featured Blueprint

UAD 3.6 / MISMO 3.6 Compliance Bridge

Don't lose weeks of engineering sprints manually mapping Fannie Mae's granular, repeatable room containers. Our drop-in Python microservice leverages strict validation frameworks to capture schema anomalies upstream, eliminating critical submission failures.

  • Production-Ready FastAPI Engine & Pydantic V2 Schemas
  • Comprehensive Native Test Suite for Asset Mapping
  • Dockerized Container Setup for Zero-Config Local Mounting
app/schemas/uad.py
from pydantic import BaseModel, Field
from typing import List

class RoomContainer(BaseModel):
    room_type: str = Field(..., example="Kitchen")
    count: int = Field(..., ge=1)
    condition_rating: str = Field(..., regex="^C[1-6]$")

class UADPayload(BaseModel):
    property_id: str
    rooms: List[RoomContainer]
    
# Containerized Local Mounting:
# $ docker build -t uad-bridge .
# $ docker run -p 8000:8000 uad-bridge

End-to-End Walkthrough Demonstration

See exactly how the validation microservice ingests a highly nested JSON payload, enforces strict Pydantic V2 schemas at the application edge, and compiles structured, portal-compliant MISMO 3.6 XML schemas.

01 / Rigorous Guardrails

Upstream Validation

We build strict Pydantic parsing systems that catch compliance configuration errors before they traverse downstream to federal schemas.

02 / Zero Footprint

Container Isolation

Our compliance blueprints deploy as localized sidecar services, removing environment friction and maintaining architectural isolation.

03 / Native Performance

Clean Compilation

No bloat or hidden wrapper overhead. We generate high-velocity, namespaced MISMO XML arrays directly using memory-optimized structures.