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Real Estate Contract Automation

AI Automation / Document AI / CRM

An email-triggered pipeline that extracts structured data from PDF contracts, gates the result on confidence, checks for duplicates, and creates transactions in the CRM — built for real estate operations.

Year
2026
Role
Generative AI Engineer, Tekvix
Source
Not public
Real Estate Contract Automation — concept diagram

Technologies

  • n8n
  • Claude API
  • PDF Extraction
  • A-Frame CRM
  • Webhooks

The problem

Signed contracts arrive as email attachments and end up retyped into a CRM by hand. It is slow, it is error-prone, and the same contract routinely gets entered twice.

The solution

Let the email itself be the trigger. The contract PDF is parsed into structured fields, the extraction is gated on a confidence threshold rather than trusted blindly, duplicates are checked before anything is written, and only then is a CRM transaction created.

Architecture

How it is put together.

01

Email trigger

An incoming email with a contract attachment starts the pipeline.

02

Structured extraction

The PDF is read and its fields extracted into a defined schema.

03

Confidence gate

Low-confidence extractions are held back rather than written through to the CRM.

04

Duplicate check

Existing records are checked so the same contract is not entered twice.

05

CRM transaction

A transaction is created in the CRM from the validated data.

Key features

What it actually does.

  • Email-attachment trigger — no new tool for the team
  • Structured field extraction from PDF contracts
  • Confidence gating before any write
  • Duplicate detection against existing records
  • Automatic CRM transaction creation

Engineering challenges

The parts that were hard.

Extraction confidence is the whole safety story

An LLM will answer even when it should not. Gating on confidence is what separates automation from silent corruption of the CRM.

Duplicates are the common failure

Forwarded and re-sent emails are normal. Deduplication had to happen before write, not as cleanup afterwards.

Outcomes

What came out of it.

Qualitative outcomes only — no invented benchmarks or metrics.

01

Contract data reaches the CRM without manual re-entry

02

Low-confidence extractions escalate instead of being written

03

Duplicate transactions prevented at source

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Next project

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Batch workflow that upserts contacts, scores deliverability and writes first-contact emails.

Let’s build

Want something like this for your business?

Tell me the problem and I will come back with an architecture, a scope and a timeline.