MELISSA A. TULI Work samples Profile Contact
Case Study Legal Ops Fintech SaaS

Standing up legal ops for a fintech whose customers just want a discount answer to bank examiners.

A growth-stage fintech was signing banks and credit unions faster than it could review their paper. There was no playbook, and every customer's counsel was drafting for a regulator. I built the legal function from the ground up and became the expert leadership, Sales and Finance trust and depend on to make the call.

ClientName withheld · Fintech SaaS
SectorFraud dispute resolution software for banks and credit unions
EngagementPart-time legal operations consulting. Legal function built from the ground up: contracts, legal ops, vendor management, insurance
RoleTrusted advisor to leadership on contract risk, data rights and deal strategy
TermJune 2024 to present
ConsultantMelissa Tuli, CPCM, CPSM · 10+ years in contracts
01Situation

Regulated customers, rising volume, no playbook

The company's customers are financial institutions. Their counsel draft with examiners reading over their shoulder, so every redline has a regulatory reason behind it and none of them are unreasonable on their face. That makes it harder to say no, not easier.

Deal volume was scaling

PE backing meant growth targets. Every new bank or credit union came with its own paper or a heavy redline of ours.

Positions lived in people's heads

No written standard for what the company would give on liability, indemnity, breach notice or audit rights. Each deal reopened the same arguments.

Concessions compounded

A yes on one deal became the ask on the next. Without a record, nobody could see the drift.

Data terms carried the most risk

The platform handles consumer financial data, and AI in the stack raised new questions about use, training and third-party vendors.

02Mandate

Protect the risk posture without slowing sales down

The brief was simple to say and hard to do: let sales close regulated customers quickly, and stop the company from quietly taking on liability it can't insure. I tested every decision against three questions.

InsuranceDoes it push liability past what the coverage program absorbs?
RevenueDoes it break the pricing, term or ARR the deal was built on?
PrecedentCan every other customer have this too?
03Delivered

What I built

Six workstreams. They run together, each one feeding the others.

Workstream · Positions

MSA baseline and Deviation Playbook

Set one MSA and Software Production Schedule as the baseline every deal is scored against. Nothing gets measured against another customer's deal. The playbook covers 18 provisions, each with a standard position, an approved fallback, a plain-language risk note, and who can approve it.

Tier 1Contracts, pre-approved
Tier 2Contracts + Finance
Tier 3C-suite
Hard StopNot offered
Workstream · Governance

Deal Desk and triage

Every customer deal goes through the desk, whatever its size. Clean deals take a same-day Fast Lane. Regulated customers, Tier 2+ asks and custom data terms get full review. Borderline deals never take the fast path. Now on version 2.0.

FAST LANE · same day

Company paper, standard pricing, non-regulated, one Tier 1 fallback max.

STANDARD · 24–48 hrs

Any bank or credit union, Tier 2+ ask, custom paper. Tier 3 in 3–5 days.

Workstream · Intake

Intake fields and deal announcements

A standard intake record for every deal. Sales can't skip the fields, and incomplete submissions go back. On close, leadership gets a new-client announcement in a set format: deal highlights, ARR treatment, and operational next steps, so Finance and Implementation hear the same facts.

Workstream · Automation

AI-assisted legal ops with Claude

Built workflows and agents on Claude that take in a new contract, run it against the playbook, and flag deviations by tier before I open it. Standard deals move faster, and my review time goes to the deals that actually need it. Paired with a CLM evaluation that scored three platforms on weighted criteria, then ran the winner's own MSA through buy-side review.

Workstream · Data & regulatory

Data rights, AI terms and Reg E / Reg Z

Negotiate data use, AI/ML usage, DPAs and BAAs with financial institutions. Flag any third-party AI vendor that touches consumer financial data, so what we promise customers matches what our vendors promise us. Track Regulation E and Regulation Z as they apply to dispute resolution, and push changes into templates.

Workstream · Operations

Vendors, insurance and disputes

Run the corporate insurance program across three carriers and brokers: renewals, limits, certificates. Process vendor terminations cleanly. Resolved a credit union customer's dispute over a data extract, and papered no-cost SOWs so free work still has scope and limits.

04Work samples

Two negotiations, issue by issue

Representative samples from my portfolio. Counterparties are anonymized, and figures and facts are changed. Each issue shows the customer's redline, the agreed language, and why. Tap an issue to open it.

05Result

What's in place now

The goal was never to be the bottleneck. The goal was a function that makes the same call the same way no matter who's asking.

Before

Positions negotiated from memory, deal by deal. No record of what had been given away.

After

One baseline, 18 written positions with fallbacks, and approval tiers Sales can act on without waiting for me.

Before

Every deal got the same slow, manual first pass.

After

Clean deals clear the Fast Lane the same day. AI does first-pass playbook review before a human opens the file.

Before

Concessions drifted with no one watching.

After

Every deal logged with the same intake fields. Novel language opens a playbook revision, so one-off terms don't become the floor.

Before

Contract risk questions scattered across Sales, Finance and leadership with no one owning the answer.

After

Leadership, Sales and Finance bring deal risk, data questions and vendor calls to one place, and trust the answer they get.

06Next

Building something like this?

If you sell to regulated customers and your contract process can't keep up with your sales team, I'd like to hear about it.

Email
mtuli1105@gmail.com
Location
San Antonio, Texas · Remote
Full profile

Client and counterparty names withheld. Work samples are representative, with names, amounts and facts changed. Melissa Tuli is not an attorney, and nothing on this page is legal advice.