Forward deployed engineeringFinancial Services & WealthTech

We embed engineers inside your firm until it runs on AI.

Mid-market wealth managers, asset managers, and PE firms lose 18-22 days of capital deployment time per institutional client due to manual KYC/AML workflows, unstructured custodian data, and fragmented middle-office operations. Neume compresses that to hours.

Book a call with an engineer How it works

$800K+

Annualized compliance cost reduction per firm

14 days to 4 hrs

Client onboarding cycle compression

Zero

Data-related trade breaks in 12 months

3-5x

Middle-office throughput without net-new hires

One engagement. From the first day on the floor to production.

Compliance / KYC-AML

What the engineer found

Mid-market private equity firm, $4.2B AUM, 85 employees, 12-person compliance team processing 150+ institutional onboardings annually across fund vehicles and co-investment structures

Institutional KYC onboarding averaged 18 days due to manual reading of 200+ page trust documents and operating agreements to map Ultimate Beneficial Owners through multi-layered offshore entity structures. The compliance bottleneck was actively delaying capital deployment and frustrating institutional LPs accustomed to faster competitors.

What the engineer built

Compliance / KYC-AML

Deployed Neume's compliance-trained LLM architecture to ingest unstructured legal entity documents, automatically construct corporate ownership hierarchies, flag PEP/sanctions exposure, and present pre-structured onboarding packages to senior compliance analysts for final sign-off. SOC 2 Type II compliant processing with full audit trail.

Built in the client’s repository. Runs on their infrastructure.

What the company got

Onboarding compressed from 18 days to 3 hours for standard-complexity entities. Time-to-revenue for new capital deposits accelerated by 18 days. The firm avoided a planned $1.2M expansion of its compliance team while increasing onboarding throughput by 4x.

18 days compressed to 3 hours -- $1.2M compliance hiring avoided

12 weeks from engagement to production deployment

Every number is from a confirmed engagement. Names withheld under NDA.

01The model

Not a consultant. Not a dev shop. An engineer on your floor.

A forward deployed engineer is a senior engineer who works inside your company rather than for it. They sit with the claims processor, the dispatcher, the underwriter. They find the workflow where AI pays off first, build the system on top of the software you already run, ship it to production, and train your people to run and extend it. Then they move to the next workflow.

In 2019 the hard part of enterprise AI was the model. Today the models are commodity infrastructure. The hard part is the twenty-year-old ERP, the undocumented process, the compliance rule nobody wrote down, and the operations manager who has watched three digital transformations fail. That is an engineering problem, and it is solved on site.

Consultancies

Deliver a strategy deck and leave. Cannot write production code.

Writes the code. Stays until it runs.

Dev shops

Build what you spec. Never see your operations.

Sits in your operations. Finds what to build.

Hiring

Six months to hire one AI engineer who starts from zero.

Arrives in a week with patterns from 47 deployments.

SaaS tools

Horizontal products configured for nobody in particular.

Systems built for your data, your edge cases, your compliance.

We don’t consult. We engineer intelligence.

02How it works

Contact. Audit. First deliveries. Monthly.

From the first call to a system in production in six weeks. Then a retainer that scales up or down every month.

01 / Contact30 minutes

A call with an engineer, not a salesperson.

We find out whether there is a fit and which workflow to start with. No deck, no discovery workshop, no proposal cycle.

Call with an engineer30 min

Who: Your COO, your head of operations, and a Neume engineer.
We ask: Where the work piles up. Which systems it lives in. Who reviews it today.
You get: A straight answer on fit, and the workflow we would start with.

Nothing to prepare. Bring the person who owns the process.

02 / AuditWeek 1

The engineer embeds with the teams doing the work.

Every workflow mapped and ranked by AI leverage. The output is a 90-day build plan with the first systems specified and ROI attached to each.

Workflows ranked by AI leverageWeek 1 · 8 departments

01
Institutional Client KYC Onboarding
14-21 days per institutional onboarding; 8-16 analyst-hours per entity; $2,800-$4,500 fully-loaded cost per onboarding
Build first
02
OFAC/Sanctions Screening & Alert Management
3,500 alerts/month; 15-30 min per alert; 2.5 FTE dedicated to alert review; 72-hour average disposition time
Queued
03
Periodic CDD/EDD Refresh Cycle
400 refreshes/quarter; 2-4 hours per refresh; 6-8 week backlog during peak cycles; 15% overdue rate
Queued

90-day build plan signed. First system: Compliance / KYC-AML.

03 / First deliveriesWeeks 2 to 6

The first system ships to production with human review switched on.

Real data, real compliance constraints, real users. This is the proof, before any retainer.

Institutional Client KYC Onboarding Production

Human review

WK 02All
WK 04All
WK 06Flagged

Starts at 100% review. Falls as the system earns trust.

18 days compressed to 3 hours -- $1.2M compliance hiring avoided

12 weeks from engagement to production deployment

04 / MonthlyMonth 2 onward

The engineer stays. Department by department, workflow by workflow.

Review thresholds fall as the systems earn trust. Scale the engagement up or down each month. Our revenue depends on it working, so we stay until it does.

Retainer roadmapOne engineer · embedded

M2Corporate Action Event Processingshipped
M3New Household Account Openingshipped
M4Quarterly Fee Billing Cyclein review
M5Daily Investment Guideline Monitoringscoped

3.2x

productivity, day 90

at twelve months

data incidents

Embedded. Not engaged.

03Field reports

What production looks like when the engineer is in the building.

Two financial services engagements, written the way an engineer would report them. Names withheld under NDA. Every figure confirmed.

Report 01Compliance / KYC-AML12 weeks from engagement to production deployment

Before

Mid-market private equity firm, $4.2B AUM, 85 employees, 12-person compliance team processing 150+ institutional onboardings annually across fund vehicles and co-investment structures

Built

18 days compressed to 3 hours -- $1.2M compliance hiring avoided

Result

Report 02Middle Office & Trade Operations14 weeks from engagement to production deployment

Before

Mid-market asset management firm, $6.8B AUM across 40+ strategies, 12-person middle-office team processing corporate actions across 5 custodian relationships and 2,000+ securities positions

Every day, custodians sent hundreds of unstructured emails and PDFs detailing corporate actions -- stock splits, special dividends, mergers, tender offers. The middle-office team was manually keying these events into the portfolio management system. One missed decimal meant a catastrophic trading error the next morning. The team had experienced 6 data-related trade breaks in the prior year, including one that cost $340K to resolve.

Built

Zero trade breaks in 12 months -- previously 6/year with $340K single-event loss

Deployed Neume's autonomous parsing engine to intercept all custodian communications, extract critical CUSIP numbers, ratios, and effective dates, cross-validate across custodian sources, and structure data for direct API injection into the portfolio system. Human analysts intervene only when the AI flags conflicting data between two custodians.

Result

Complete eradication of manual data entry in the middle office for corporate actions. Zero data-related trade breaks in 12 months post-deployment. Processing capacity scaled to handle 3x volume during peak periods without additional headcount.

47+

Production systems deployed

6 wks

Average kickoff to production

3.2x

Productivity gain at 90 days

At twelve months

Data incidents

The full report

Every financial services department mapped before the engineer walks in.

The analysis our engineers work from: pain points, opportunities with ROI, workflows before and after, the regulatory constraints, and a phased roadmap.

Read the financial services report

Departments mapped

AI opportunities

Workflows, before and after

04AI native, defined

Five levels. The engineer moves you up one workflow at a time.

AI native means AI is inside the workflow, not beside it. Every recurring process has been examined for what an agent does and what a human must do. Every knowledge worker has a copilot wired to the company’s own data. And the people who work there can extend the systems after the engineer leaves. The audit tells you where you are today.

Audit · Week 1

Where does the company sit today?

Five levels · one ladder

0level
Curious
ChatGPT on personal accounts. No policy, no systems, no data connected.
1level
Experimenting
A pilot or two in a sandbox with clean data. Nothing in production.
2level
Operational
One or two systems live in one department, with humans reviewing every output.
3level
Embedded
AI inside the core workflows of most departments. Humans handle the exceptions.
4level
Native
New work is designed AI-first by default. Your own team extends the systems.

Most companies arrive at level 0 or 1. The first system takes them to level 2 in six weeks.

05What we build

Fine-tuned models map regulatory language to internal control frameworks, automate compliance extraction, and generate risk assessments using institution-specific taxonomies and historical examination data.

Read the brief

Computer Vision

Turn cameras, scanners, and satellite feeds into structured, actionable data -- replacing manual visual inspection with auditable, sub-second analysis at any scale.

Read the brief

Not sure which one?

If the work runs on documents, decisions, and handoffs, one of these applies. The audit tells you which to build first.

Book a call with an engineer

06Engagement shapes

Monthly. Scale up or down each month.

No minimum term. The shape is agreed on the call and can change as the roadmap does.

Two days a week

Fractional

One engineer, part time, one workflow at a time. The right shape for a 50 to 200 person company taking its first system into production.

Talk about this shape

One full-time engineer

Embedded

One engineer inside one department, shipping a system every four to six weeks and training the team that runs it.

Talk about this shape

Two to three engineers and a lead

Pod

Several departments at once, with a lead who owns the roadmap across them. For companies that want to move up the ladder fast.

Talk about this shape

07Built for companies that cannot get this wrong

Your data. Your infrastructure. Your repository.

Four guarantees, in every contract, before any system goes live.

Human in the loop

Every system ships with a review layer your team controls. It starts at 100% human review and falls as the system earns trust. By month six most clients run 85 to 90% autonomous, with humans on the edge cases.

100% audited outputs

Data sovereignty

Your data stays on your infrastructure and never trains public models. SOC 2 Type II, with every access, inference, and review logged and attributable.

0 data incidents

You own everything

Everything the engineer builds lives in your repository and runs on your infrastructure. Your people are trained to run it and extend it. Nothing walks out the door when the engagement ends.

Yours code and repository

Contractual KPIs

Processing time, error rate, cost per transaction, hours freed. Agreed before we build, reported monthly. If the return is not there, we tell you first.

Agreed before deployment

The window

Every week you wait, the gap compounds.

Productivity is 3.2x at month three and closer to 8x at month twelve, because the systems learn from every transaction and the people learn alongside them. A competitor starting from zero a year from now faces the same six-week build. They are twelve months of institutional learning behind, and that gap does not close.

Book a call with an engineer

30 minutes. An engineer, not a salesperson.
Or forward this page to your CEO.

rohan@neumelabs.ai