Real Estate / Public-Records Workflow Automation
From Manual Research to an Auditable Operations System
A document-heavy real estate research process required people to move between county records, court systems, property data, recorded documents, spreadsheets, and internal records just to determine what should happen next.
B&T designed and built a multi-stage research and decision-support system that turns that fragmented process into a structured workflow with automated screening, document retrieval, deterministic rules, evidence tracking, and human review.
- Public-record research
- Browser automation
- Document processing
- Rules engines
- Workflow orchestration
- Human review
The Challenge
The work was repetitive. The decisions were not.
A single record could require research across several independent systems before a reliable decision could be made.
Researchers had to determine whether a record was worth pursuing, check whether it had already been researched, search public databases, identify the correct owner and property, retrieve recorded documents, reconstruct ownership and debt history, perform calculations, and organize the supporting evidence.
The underlying data made that difficult to automate safely.
Names were inconsistent. The same person could appear across multiple properties. Documents varied by source and era. Public systems could be incomplete or temporarily unavailable. Some decisions had clear rules, while others depended on interpreting evidence.
A single autonomous AI agent would have been the wrong architecture.
What We Built
A workflow made of controlled checkpoints
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Monitor & Intake
New records can be detected, normalized, enrolled into the workflow, and tracked from a central operations system.
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Eligibility & Duplicate Screening
Clear business rules handle early qualification decisions and identify records that have already entered the process.
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Public-Record Research
Browser-based workers search county, court, property, and recorded-document systems, including sources without convenient APIs.
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Document Retrieval & Processing
Supporting documents are collected, retained, extracted, and connected back to the record being researched.
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Ownership & Debt Research
The system helps connect evidence to the correct property and reconstruct relevant ownership, mortgage, lien, judgment, and release history.
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Human Review
When evidence is incomplete, conflicting, or genuinely ambiguous, the workflow stops instead of guessing and routes the record for review.
The Architecture
Automation where the answer is clear. AI where interpretation is useful. Human review where judgment matters.
Workflow
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Intake
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Eligibility
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Duplicate Check
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Civil Research
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Property + Ownership Research
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Debt Research
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Evidence + Calculations
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Human Review
Supporting layer
- Source Documents
- Audit Trail
- Rule Versions
- Exceptions
- Database
- Human Approval
Each checkpoint can advance a record, stop it, or route it for review without compromising the rest of the workflow.
Beyond Simple Automation
This is not "when X happens, send Y."
Many operational processes cannot be solved with a single trigger and action.
- Detect & validate
- Research across systems
- Retrieve & interpret evidence
- Apply rules & calculations
- Preserve results & update the system
- Route uncertainty to human review
The result is closer to a purpose-built operational system than a collection of disconnected automations.
Built to Handle the Exceptions
Reliable systems need to know when not to guess.
Production workflows expose edge cases that are difficult to predict on a whiteboard.
Instead of hard-coding fixes around individual records, B&T built a controlled process for identifying recurring failure patterns, testing improvements against known examples, versioning the underlying rules and parsers, and validating changes before promoting them into the workflow.
Missing evidence is not the same as system failure.
When a source document could not be retrieved, the workflow was redesigned to distinguish unavailable evidence from an actual failed research run.
The same person does not mean the same property.
Property-specific scoping prevents unrelated records belonging to the same owner from contaminating the ownership history being researched.
Messy names should not force fuzzy guesses.
Multi-party ownership records led to a versioned deterministic parsing system designed to improve name handling without breaking previously successful cases.
Ambiguity should stop the workflow.
When multiple plausible events or records could apply, the system can route the case for human review rather than manufacturing certainty.
Engineering Proof
Tested stages, not projected outcomes.
- 335 / 335
- Successful source checks
- Completed during combined county connector testing across two public-record environments.
- 177
- Records enrolled
- Passed through one automated time-window intake stage.
- 114
- Records advanced
- Moved into county duplicate screening after intake.
- 28 / 58 → 45 / 58
- Multi-party owner parsing
- Improved through a deterministic parser upgrade while preserving previously successful parses.
These figures describe specific tested workflow stages and engineering runs, not end-to-end case outcomes.
Before / After
What changed in how the work gets done.
Before
Researchers manually moved between county lists, public portals, court systems, spreadsheets, PDFs, and internal records.
Progress depended heavily on individual consistency, manually tracking what had already been checked, and remembering what step should happen next.
After
Each record moves through a defined operational workflow.
Clear decisions can be automated. Supporting documents are retained. Results carry source provenance. Ambiguous evidence is routed to review. Workflow state is tracked centrally, and the system knows which checkpoint should happen next.
The Bigger Lesson
The workflow was specific. The underlying problem is not.
This system was built around a real estate and public-records research process, but the architecture applies much more broadly.
Many businesses still depend on employees to search multiple systems, retrieve and review documents, compare records, apply business rules, calculate outcomes, copy information between tools, track exceptions, and decide what happens next.
Those patterns exist in lending, title and escrow, legal operations, financial services, insurance, compliance, logistics, real estate, and other document-heavy back-office environments.
The goal is not to force every decision into AI.
The goal is to understand how the work actually happens, determine what can be handled reliably by software, and keep people in control where judgment still matters.
Show Us How Your Team Does It Today
If an important process still depends on spreadsheets, inboxes, public portals, documents, repetitive research, and people remembering what happens next, there may be a better way to run it.