AI & CLM Consultant · Certified Paralegal

Where Legal Expertise Meets Contract AI

I help legal teams stop reviewing contracts manually at scale and start trusting AI to do it accurately. 40+ clause detection models. 50,000 contracts processed. 95%+ accuracy, every time.

See the Work Get in Touch

By the numbers

40+

Clause detection models built across indemnification, limitation of liability, termination, assignment, and payment obligations

50K

Contracts processed in a single engagement

95%+

Minimum accuracy threshold before any model advances to production

Engagements across financial services, energy, and law firm portfolios

01 — About

Legal fluency meets AI precision

Chantel Hill

AI & CLM Consultant · Certified Paralegal

"Most people on this work are either lawyers who don't trust the model or engineers who've never read a contract. I've done both — and that changes everything about how I build."

Based in

Remote · US

Background

6 yrs legal · 1+ yr contract AI

Credential

Certified Paralegal (CP)

Education

B.S. Psychological Sciences · NAU

Most AI implementations in legal fail for one of two reasons: engineers who don't understand contracts, or lawyers who don't trust AI outputs. I've spent years on both sides of that problem.

With 6 years as a certified paralegal and 1+ year building enterprise contract intelligence systems, I translate clause-level legal risk into model requirements — then build, evaluate, and deploy those models to a 95%+ production standard.

I've worked across financial services, energy, and law firm portfolios. I know how clause language varies by industry, how to design extraction pipelines that hold up under attorney scrutiny, and how to present model performance to stakeholders who have never seen a precision-recall curve.

That combination is rare. It's why teams bring me in when the stakes are high.

Contract Intelligence

Clause detection, extraction, and structured datasets

Clause detection modeling, extraction pipeline design, and structured dataset creation from unstructured legal language — built to hold up in production.

AI Implementation & Governance

From use case scoping through defensible deployment

End-to-end delivery with governance structures attorneys can defend: confidence thresholds, escalation paths, human-in-the-loop workflows, and audit trail requirements.

Stakeholder Translation

Complex model outputs in plain language

I present AI performance findings to attorneys, legal ops directors, and executives — bridging the communication gap most technical teams can't close.

02 — Case Studies

Real engagements, real results

Financial Services · Scale

50,000-Contract Portfolio: Building AI Review Infrastructure from Scratch

The Problem

A financial services client needed to extract and analyze key clause provisions across a massive contract portfolio. Manual review at that volume was cost-prohibitive and slow.

What I Built

Designed the full clause taxonomy and extraction architecture. Built and evaluated detection models for indemnification, limitation of liability, termination, assignment, and payment obligations using prompt engineering on Relativity's AI platform.

How I Validated It

Ran precision, recall, and F1 evaluation at every iteration. Built reviewer playbooks and labeling guidelines so attorneys could audit and trust model outputs before any result entered production.

Result: ~50% increase in review capacity on active matters · Models met the 95%+ accuracy bar before attorney handoff · Select clause types reached 100% accuracy

Cross-Industry · 11 Engagements

Building Reusable AI Infrastructure Across Legal Teams

Rather than starting from zero on each engagement, I developed reusable clause libraries, prompt templates, and reviewer playbooks that could be adapted across financial services, energy, and law firm clients — compressing onboarding time and raising the quality floor on every new matter.

11 engagements delivered with consistent production standards · Reusable assets reduced setup time on repeat clause types

Municipal Government · High Volume

200+ Contracts/Year: The Foundation That Informs AI Design

Before building AI models, I was the person doing the work they would eventually replace — reviewing 200+ municipal contracts annually across procurement, public works, and intergovernmental agreements. That hands-on clause analysis became the foundation of how I design detection models today.

Deep clause-level expertise across 6 years of legal practice · Risk pattern recognition that most AI consultants don't have

03 — How I Think

Decisions I make that most consultants skip

I hold every model to a 95%+ accuracy threshold before it touches production

Not because a client asked for it — because anything less isn't defensible in a legal context. If a model can't meet that bar, I iterate on prompt design and labeling guidelines until it does. Attorney trust is too expensive to lose on a bad output.

I build human-in-the-loop workflows, not black boxes

AI in legal doesn't replace attorney judgment — it has to earn it. Every model I deploy includes confidence thresholds, escalation paths, and audit trail design. Legal ops teams need to be able to explain what the AI did and why.

I evaluate on precision, recall, and F1 — not just "does it seem right"

Gut-checking outputs isn't evaluation. I run rigorous statistical validation, track error patterns across clause types, and document what breaks and why. That's what separates a model that works in a demo from one that holds up on 50,000 contracts.

I translate between two worlds that rarely speak the same language

When attorneys ask "why did the model flag this?" I can answer in legal terms. When engineers ask "what should the model extract?" I can give them a structured requirement. That's where most AI legal implementations break down.

I design for clause variation that exists in the real world

Indemnification language in a financial services agreement reads nothing like indemnification in a municipal contract. My 6 years of hands-on legal review means I build models that handle real-world variation — not just the clean examples that make demos look easy.

I scope before I build — and push back when the use case isn't ready

Not every AI use case in legal is worth building yet. Part of my consulting work is helping clients figure out where AI actually saves time versus where the error rate makes it a liability. Sometimes saying "not yet" is the highest-value thing I can offer.

04 — Tools & Stack

The stack behind the work

AI & Modeling

Legal & CLM

Certifications

AI & Modeling Skills

Prompt Engineering Clause Detection Modeling LLM Application Development Structured Output Design Model Evaluation (P/R/F1) Error Analysis Agentic Workflow Design Human-in-the-Loop Review AI Governance

Legal & CLM Skills

Contract Intelligence CLM Implementation Legal Operations Contract Risk Analysis Clause Taxonomy Design Certified Paralegal (CP) Relativity Contracts Pro DocuSign CLM & Insight Microsoft Copilot

Active Certifications

Relativity Generative AI Pro · 2025 Relativity Contracts Pro · 2026 DocuSign CLM Administration Pro · 2026 IBM Prompt Engineering · 2026 Vanderbilt Agentic AI · 2026 Certified Paralegal (CP)

05 — LinkedIn

Current profile, as it appears live

Current Headline

AI & CLM Consultant | Contract Intelligence + Clause Detection (95%+ F1) | Helping Legal Teams Deploy AI at Scale | Relativity · DocuSign

Current About Section

Most AI models built for contract review are wrong more often than people realize. The teams using them just don't always know it yet.

I got into this work from the legal side. Six years as a certified paralegal, including time at a city attorney's office reviewing contracts, flagging risk, and catching the kinds of clause-level issues that become expensive problems when they get missed. That background shapes how I build AI systems now. I know what the model is supposed to find, and I understand the real cost when it doesn't.

At Cimplifi I work on contract intelligence problems that sit at the harder end of what legal AI can do right now. Across 11 engagements in financial services, energy, and law firms, including one matter with roughly 50,000 contracts, I've built 40+ models that extract and classify specific clause types: indemnification, limitation of liability, termination, assignment, payment obligations. Every model goes through a rigorous accuracy testing process before it touches a real review. I don't advance anything until the numbers prove it's ready, and I've hit 100% accuracy on select clause types depending on the contract dataset.

Day to day that looks like:
— Translating legal risk into AI model requirements clients can actually act on
— Building clause detection models and testing them against measurable accuracy benchmarks
— Designing extraction workflows with attorney review built into the process from the start
— Helping legal teams understand what the model is doing and where to trust its output
— Setting up governance structures so clients know what gets flagged, what gets escalated, and how to audit it

Tools: Relativity Contracts Pro and GenAI, DocuSign CLM and Insight, Microsoft Copilot.
Certifications: IBM Prompt Engineering, Relativity Generative AI Pro, Relativity Contracts Pro, DocuSign CLM Administration Pro, Vanderbilt Agentic AI.

Legal AI is still early. Most teams are working through the same questions around governance, defensibility, and how to operationalize these systems responsibly at scale. That is the work I find most interesting. Always glad to exchange thoughts with others in the space.

chantelhill.cp@gmail.com

Profile URL

chantelhill.com