top of page

CASE STUDY -> SYSTEM DESIGN

Designing Human-Guided AI for Contract Onboarding

Legal teams don’t lack data they struggle to trust it.
When contracts are onboarded at scale, small errors compound into broken relationships, unclear ownership, and unreliable systems.
 
This project explores how AI can assist onboarding 
not by replacing humans, but by working alongside their judgment.

We don’t start from clean systems. We inherit chaos.

Data ingestion from legacy contracts isn't a clean extraction. It's a messy reconstruction of history. Before the redesign, every new batch of contracts arrived as a fragmented puzzle of mismatched entities and broken hierarchies.

48 %

Missmatch

34%

Duplicates

56%

Hierarchy Issues

Business Impact
Manual validation slows legal teams
Legal Engineers were spending 60% of their billable hours manually cross-referencing CSV exports against PDF scans to verify basic details.
Relationship mapping is error-prone
Understanding which Master Services Agreement (MSA) linked to which Statement of Work (SOW) was a cognitive nightmare.
Work spills into spreadsheets
Because the internal tools were brittle, teams reverted to external spreadsheets, creating a dangerous "shadow" data layer.

How the Onboarding Experience Evolved

How Humans Disambiguate

01

02

03

Identify

"Who is this?" — Resolving legal entity names from fuzzy strings into a single source of truth.

Validate

"What’s connected to it?" — Mapping parent-child hierarchies between agreements automatically.

Verify

"Can I trust this?" Presenting AI evidence to humans for final high-confidence approval.

The cost of poor onboarding isn’t immediate. It compounds.

Onboarding delayed by weeks
Increased operational cost
High risk of incorrect data
Skilled teams doing repetitive work
bottom of page