AI & CLM Consultant · Certified Paralegal

Where Legal Expertise Meets Contract AI

I turn high-volume, messy documents into structured data teams can trust — extraction pipelines, real quality bars, and automated workflows that hold up at scale. Proven in legal, built to transfer: 100+ production models, 50,000 documents processed, 95%+ accuracy, every time.

See the Work Get in Touch

By the numbers

100+

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 data systems, I translate domain risk into model and data requirements — then build, evaluate, and deploy to a 95%+ production standard. Contracts are where I proved it; the method works on any document set that has to be right.

I've worked across financial services, energy, government, and law firm portfolios. I know how source data varies by industry and jurisdiction, how to design pipelines that survive expert 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 — and why the discipline transfers to any data that has to be trusted, not just contracts.

Contract Intelligence at Scale

100+ clause models. 50,000 contracts. 95%+ accuracy.

Detection modeling, extraction pipeline design, and structured dataset creation from unstructured documents — proven on legal language under attorney scrutiny, and built to work on any high-stakes document set.

Agentic Workflow Design

Multi-agent systems with human-in-the-loop governance

Multi-agent orchestration with the patterns enterprise AI deployments need: doer + reviewer agent pairs, structural HITL gates at every handoff, low-confidence escalation, and event traces that show exactly what each agent did. See Stride for a live, working demonstration.

AI Program Delivery & Governance

From use case scoping through production adoption

End-to-end delivery across 12 enterprise engagements: discovery workshops, governance design, pilot programs, change management, and reusable playbooks that compress program setup from days to hours.

Stakeholder Translation

Precision, recall, and F1 in plain language

I present AI performance findings to attorneys, legal ops leaders, and executives — bridging the communication gap most technical teams can't close, and accelerating sign-off where it matters.

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 · 12 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.

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

Private Sector → Government → AI Consulting

1,500+ Contracts and 200+ Bills a Year: The Foundation That Informs AI Design

Before building AI models, I was the person doing the work they would eventually replace. In private practice: estate planning, probate, M&A, corporate transactions, and real estate closings across 50+ concurrent matters. Then a city attorney's office: 1,500+ contract requests a year at zero backlog on a 24-hour turnaround, plus 200+ bills and resolutions drafted and shepherded to public record. That range — and working across differing court systems and jurisdictions — is the foundation of how I design data systems today.

Breadth most AI consultants don't have: estate planning to M&A to legislation · Fluency in how jurisdictions and court systems differ · Private sector to government office to AI consulting

03 — Built Work · Updated July 2026

Live applications, not just slides

Beyond client engagements, I ship working systems end-to-end — designed, evaluated, and deployed at production standards. Each one is real and working, hosted on chantelhill.com or running live in Google Workspace, and demonstrates a different facet of how I build: multi-agent orchestration, human-in-the-loop governance, workflow automation, and the same evaluation rigor I apply to client clause-detection work.

Multi-Agent System · Live at stride.chantelhill.com

Stride — Multi-Agent Orchestration with Human Oversight

A working AI tool for project managers. Seven specialized agents — status synthesizer, risk detective, meeting prep, action tracker, comms tailor, tracker curator, and a reviewer/analyst that critiques every doer's output — execute in parallel and surface drafts at human-in-the-loop gates before any output advances. Built on the Anthropic SDK with client-side orchestration, parallel doer + reviewer phases, deterministic curator logic, and a persistent status tracker that survives across runs.

What it demonstrates: agentic workflow design · doer + reviewer pattern that flags low-confidence output before the user sees it · four kinds of structural HITL gates (between-agents handoffs, external-action approval, final-publish review, and low-confidence escalation) · the same disciplined evaluation and governance design that enterprise AI deployments require

See it live →

Causal Eval Harness · Live at attribution.chantelhill.com

Attribution Truth-Checker — Validating AI Claims with Causal Evidence

A working prototype that validates marketing attribution against measured causal incrementality. Built on synthetic data with known ground truth (50 cities, 26 weeks, 100K users), a hand-rolled geo-lift engine using two-way fixed-effects panel regression with cluster-robust standard errors, and a self-evaluation harness scored on precision, recall, F1, and threshold sweeps.

What it demonstrates: the same evaluation rigor I apply to clause-detection work — precision, recall, F1, error analysis, and threshold sweeps — transferred to a different domain to prove the discipline is portable, not industry-specific

See it live →

Workspace Automation · Google Sheets + Apps Script

Project Command Center — Self-Running Project Operations

A six-tab project system that does the manager's prep itself. A live dashboard rolls up KPIs and team load with zero manual entry; a weekly status report writes its own headline and sections from tracker data; a follow-up builder mail-merges ready-to-send stakeholder drafts from any row; and a 7 AM Apps Script digest emails everything overdue or due for follow-up, unprompted. Priority-aware alert logic, 1,400+ live formulas, and every row pre-armed so new work inherits the system automatically.

What it demonstrates: data-ops discipline applied outside the legal niche entirely · automation that hands management time back instead of adding tooling overhead · deliberately built AppSheet-ready, with fields that map cleanly to Jira, Asana, and Monday · status-meeting prep reduced to zero minutes

Request a walkthrough →

04 — 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 categories, 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 real documents.

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 the variation that exists in real-world data

The same concept reads completely differently across industries and jurisdictions — indemnification in a financial services agreement looks nothing like it does in a municipal contract. Six years of hands-on review means I build systems that handle that messiness, 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.

05 — Skills & Stack

The stack behind the work

AI & Delivery

Legal & Ops

Tools

Certifications

AI & Delivery

AI Program Management Cross-Functional Program Coordination Use Case Scoping Client Engagement Leadership Prompt Engineering Clause Detection Modeling Model Evaluation (Precision/Recall/F1) LLM Application Development Agentic Workflow Design Human-in-the-Loop Governance AI Risk & Governance Pilot Program Design Change Management Playbook & SOP Development Workflow & Alert Automation

Legal & Ops

Contract Intelligence CLM Implementation Legal Operations Contract Risk Analysis Certified Paralegal Cross-Functional Stakeholder Coordination Process Optimization

Tools

Claude Code Cowork Relativity Contracts Pro & GenAI DocuSign CLM & Insight Microsoft Copilot Microsoft 365 Google Workspace Google Apps Script AppSheet Monday.com

Active Certifications

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

06 — 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 12 engagements in financial services, energy, and law firms, including one matter with roughly 50,000 contracts, I've built 100+ 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