Fractional Chief AI Officer for Capital Markets

01

Finance is native, not a vertical I learned for the pitch.

I built real-time pricing, P&L, and risk systems on an $85B mortgage desk, then ran data and AI products at Disney Streaming scale. I have priced a bond, carried a P&L, and shipped to a regulator.

That order matters. I build agentic systems that work inside the constraints a trading desk, a risk team, and an allocator operate under, because I have sat in those seats.

Most agents fail because the data underneath them was never ready — and because whoever built them never understood the business they were built for.

02

Built for a specific buyer.

This works for a narrow set of firms. Here's the line.

A strong fit if you are

A trading desk, risk function, or asset manager moving AI from scattered pilots to systems people rely on.
A capital-markets fintech that needs credible AI leadership your buyers and regulators will trust.
A firm that wants to own its AI capability outright.
A leadership team that needs board-ready AI governance before it can justify a full-time executive hire.

Probably not a fit if

You need someone onsite five days a week. This is part-time by design.
You need registered advice or anything requiring a Series license. I don't provide it.
You need a build team more than a leader. Applied Agents may fit; this engagement won't.
You need production inside six weeks. I'll tell you no on the call.
Your domain is outside finance, where my desk experience stops being the differentiator.

03

Why AI stalls inside financial firms.

Pilots that never leave the lab

A demo impresses the offsite, then dies on contact with real positions, real latency, and real accountability. There was no operating model to carry it into production.

A data layer that was never ready

Agents are only as good as the foundations beneath them. Lineage, quality, and governance get skipped, and the system fails quietly where it costs the most.

Vendors who don't speak desk

The people selling AI rarely know what an axe, an RFQ, or a model-risk regime is. Their tools fight your constraints instead of working within them.

04

What I own as your fractional CAIO.

Embedded executive leadership. I sit with your team, carry the thread across quarters, and leave you with capability you keep.

01

AI strategy and roadmap

A roadmap sequenced against your regulatory calendar and your P&L: which use cases, in what order, against which desk.

02

Governance and model risk

Model risk under SR 11-7. Acceptable-use policy, validation evidence, audit trails, and a review cadence your MRM function will sign.

03

Build oversight, systems you own

Architect and oversee the agentic systems themselves, on your infrastructure, inside your perimeter, so the capability stays with your team.

04

Vendor and build evaluation

Buy-versus-build from someone who can read a vendor's latency claims and knows which ones matter at your holding period. No referral fees.

05

Board and leadership reporting

Updates an investment committee will trust: progress, governance health, and where the next dollar goes.

06

Team enablement

Build internal AI literacy and capability so the firm grows past needing me. That's the point.

05

The track record this is built on.

Every line below is a seat I sat in.

Capital Markets

7 years on Citi's MBS trading desk
Real-time pricing, P&L, and risk systems
$85B book across 13 trading desks
OCC regulatory delivery
Hedge-fund risk work at StepStone
Master of Finance

Data & AI at Scale

Data and AI products at Disney Streaming
PB-scale viewership and subscriber data
60M+ subscribers
Forecasting and content analytics in production
Production multi-agent orchestration on AWS Bedrock
The full data foundation agents stand on

Regulated by Design

Financial model-risk frameworks
Runs inside your perimeter — your cloud, your keys
Controls, guardrails, observability, human-in-loop
Survives a validation review

06

I build the systems.

Strategy backed by hands on the system. The agentic and data layers I design, oversee, and can build directly. The build practice lives at applied-agents.ai.

Agentic Systems

Multi-agent orchestration, tool use, MCP
RAG, knowledge bases, retrieval strategy
Evals, guardrails, observability
Human-in-the-loop control

Data Foundations

Strategy, governance, quality, lineage
Lakes, warehouses, lakehouse
Pipelines and orchestration
Dimensional modeling, analytics, BI

Stack

AWS Bedrock · AgentCore
LangSmith · Step Functions · Lambda
Claude Code

07

How we work together.

Three ways in, scoped to where you are. Every engagement starts with a call to confirm fit before anything is signed.

Entry point

Strategy Sprint

A time-boxed engagement to produce a sequenced AI roadmap, a governance baseline, and a clear buy-versus-build call. The fastest way to turn pressure into a plan.

FormatProject

Duration4–6 weeks

OutputRoadmap + charter

Flagship

Embedded Fractional CAIO

Ongoing executive ownership of your AI agenda: roadmap, governance, build oversight, vendor decisions, and board reporting. Embedded in your leadership team across quarters.

FormatRetainer

CadencePart-time exec

Min term3 months

Targeted

Advisory & Build

Defined-scope work: a specific agentic system to architect and ship, a model-risk review, a vendor evaluation, or standing advisory presence for an internal team.

FormatProject / advisory

ScopeDefined

Hands-onYes

08

Questions worth answering up front.

How is a fractional CAIO different from a consultant?

A consultant delivers a project and leaves. A fractional CAIO is embedded in your leadership team over time, owns the AI roadmap, attends the meetings, makes ongoing vendor and build decisions, and is accountable for outcomes across quarters. The goal is to build capability you keep.

Does our data ever leave our environment?

No. The architecture is designed to stay inside your data perimeter. Systems run on your infrastructure and your cloud, which is non-negotiable for trading, risk, and allocator data and is built in from the first design decision rather than bolted on.

Why finance specifically?

Because the differentiator is real domain experience. Seven years on a mortgage desk and hedge-fund risk work mean I know what a desk, a risk team, and an allocator do, and I build agents that work within those constraints. Outside finance, that edge stops applying, which is why this page commits to it.

How much time does an engagement involve?

It depends on scope and maturity. A flagship embedded engagement is part-time executive presence on a retainer with a three-month minimum. Sprints and defined-scope advisory are sized to the work. We confirm the right shape on the first call before anything is committed.

What does pricing look like?

Pricing is scoped to the engagement and discussed on the call, once the fit and shape are clear. Every engagement is bespoke to your firm's regulatory surface and maturity, so a fixed menu would misrepresent the work.

Who you'd be working with

Camille Lambert

For seven years I built the pricing, P&L, and risk systems traders used on Citi's mortgage desk, then led data and AI products at Disney Streaming scale. This practice is where those two halves meet: executive AI leadership for capital markets and asset management firms, plus the agentic systems — built through my firm, Applied Agents — to back the strategy up.

I work with two or three firms at a time, embedded closely enough to be accountable for what ships.

Master of Finance · LA-based, available nationally

The rare AI leader who already knows what your desk does.