Dark Matter · R&D. Bespoke infrastructure for serious financial firms.

01 /The Problem

Bespoke workflows. Custom data. The systems vendor platforms can't reach.

Every serious firm carries the same gap: workflows too specific for off-the-shelf platforms, edges too proprietary for a consultancy. Data scattered across custodians and fund admins. Diligence that needs forensic depth. The work is automatable, but building it properly means standing up a full quant-engineering function, so it stays manual and your best people rekey data instead of making decisions.

$1.4T

in asset-manager IT spend industry-wide, and the bespoke layer keeps growing, not shrinking

~70%

of asset managers cite tech debt and system integration as their top operational blocker

57%

of family offices cite a lack of internal technology expertise as their #1 barrier

Months

a single mandate, direct deal, or fund launch can absorb without dedicated infrastructure

Sources: Citi Private Bank Global Family Office Report 2025 · UBS Global Family Office Report 2024 · Deloitte / EY industry surveys, 2024.

02 /What We Build

Bespoke systems, scoped to the workflows that matter most.

01

Diligence systems

Read an entire data room, cross-check management's claims against the source documents, cite every page. Months of work compressed into days, and it stands up to your IC.

02

Data consolidation & reporting

Holdings across every custodian, bank, and asset class in one verified view. A multi-day reporting process turned into minutes, on a clean data layer your models can actually use.

03

Research & monitoring infrastructure

Live engines that aggregate market, portfolio, and on-chain data into a single source of truth, with an analytical layer that answers questions instead of making you dig through tabs.

04

Custom builds

If it's financial, document-heavy, and done by hand today, it can be built. Scoped to your exact workflow. Not a generic platform you bend to fit.

One system, four modules. Not a menu of hourly work.

Diligence & forensics

Full data-room ingestion (PDFs, decks, 10-Ks) → structured
Claims-vs-data cross-check, cited to source page
Beneish M, Altman Z, Piotroski F forensic suite
Management-memo verification & red-flag triage
Committee-ready memo output, one click

Reporting & data layer

Multi-custodian holdings consolidation (cash to derivatives)
Statement / K-1 / capital-call PDF → structured data
Position & performance reporting, scheduled or on demand
Reconciliation against custodian and bank feeds
Clean, AI-ready data layer the whole firm can query

Research & monitoring

Live market-data pipelines (equities, crypto, macro, energy)
13F intelligence and ownership-change alerts
18-method intrinsic-value ensemble
Machine-learning signal & forecasting models
Conversational analytical layer over your data

Execution & ops

Backtesting and signal infrastructure (tick-level if needed)
Order management, execution analytics, slippage attribution
Risk dashboards: exposure, concentration, scenario
Internal workflow automation across front / middle / back
Investor-relations reporting and audit-grade trails
Provenance and governed access: every output cited, every run logged

Every item above is something we already operate inside the Dark Matter Terminal. We don't research the problem. We adapt the solution.

03 /Compliance & Control

The layer the CIO doesn't buy and the COO can't run without.

Two buyers, one system. The CIO buys research and data; the COO, compliance officer, and controller buy the reporting and control layer underneath. This is that layer, built to the standard your auditor and examiner will hold it to.

Lead capability

Compliance & reporting automation

The layer that removes the operational overhead constraining your mandate. Risk and regulatory reports in your required format, reconciled against every feed, audit trail on every number. What takes a team a week, produced on schedule, unattended.

Regulatory report generation in required formats (risk, exposure, concentration, limits monitoring)
Automated reconciliation against custodian, prime-broker, and fund-admin feeds
Audit-grade trails on every figure, source-traceable to the document
Investor / LP reporting, capital-account statements, capital-call and K-1 handling

Paired capability

Governance & provenance

Every output traces to the document it came from; every run is logged. Identity, permissions, and approved sources enforced in one place, built to stand up to compliance, audit, and an examiner.

Every figure cited to its underlying document or data point
Full run logging and audit trail on every job
Identity, permissions, and approved sources enforced centrally
Built for review by compliance, audit, and the regulator

What the overhead costs, and what automating it gives back.

60–70%

of regulatory-reporting cost is staff time, not systems. The manual work is the expense.

15–25%

of analyst hours automation removes from low-value reporting and reconciliation work

20–35%

cut in reporting and reconciliation labor cost once the layer runs unattended

80%

fewer manual steps in a live asset-manager reconciliation deployment

Sources: Deloitte and industry regulatory-reporting analyses · LexisNexis True Cost of Compliance · finance-automation ROI and asset-manager reconciliation deployments, 2023–2025.

◆ Confidentiality & data

Mutual NDA before any engagement. It runs in your environment, under your controls. We never hold or move your data. Source, infrastructure, and data are yours outright.

NDA first

Mutual NDA signed before the scoping sprint begins.

Your environment

AWS, Azure, GCP, or on-prem. Your account, your VPC, your controls.

Your data stays put

We never custody, host, or move your data into a vendor cloud.

Owned outright

Source code, infrastructure, and data are yours. No lock-in.

04 /Proof

We built this to run our own fund.

We run a systematic, market-neutral fund. To operate it, we built the Dark Matter Terminal: a live market-intelligence system spanning forensic accounting, an 18-method valuation ensemble, a live 13F reader, dark-pool tagging, and shipping-flow tracking, fronted by a conversational layer that answers any market question with cited research.

Built end to end, in-house. Not a slide deck: production infrastructure the fund trades on every day. Most firms put systems like this in a five-year plan. We run ours daily, and it's the proof of what we'll build for you.

Conversational analytical layer · a live market question, answered with cited confluence

Diligence Engine: an ELEVATED RISK forensic memo, 4 of 5 management claims contradicted by the numbers, with Beneish M, Altman Z and Piotroski F scorecards — Diligence Engine · forensic memo, run on a sample data room

05 /How It Works

Scoped, staged, and risk-reversed.

01

Fixed-fee

Scoping sprint

A fixed-fee sprint to spec your workflow, data sources, and the build, with a working prototype on your real data. Fully credited if you proceed; if the spec isn't right, you walk with the work and owe nothing. You know exactly what you're getting before you commit.

02

Milestone-gated

Milestone build

Delivered in stages tied to working deliverables on your data. You don't pay past any milestone unless it's delivering exactly what was promised. Code review with your CTO, external auditor, or trusted advisor is welcomed. Encouraged, actually.

03

You own it

Live & maintained

It runs in your environment, your controls, full audit trail. Source, infrastructure, and data are yours: no black box, no lock-in, no year-two ransom. Optional maintenance keeps it sharp as your needs evolve.

Loaded cost of building it in-house$900k–$1.6M / yrA realistic team: senior quant developer + senior data engineer + ops engineer, fully loaded. A single senior quant developer alone runs $450k–$800k.

A scoped build, end to endFraction of one FTE / onceDelivered in weeks, owned by you, runs without supervision. The same outcome as a multi-year in-house buildout, without the recruiting cycle or the ramp.

What you walk away with

Working system in your environment Full source code in your repo Integration tested against your live data Architecture & runbook documentation Training session for your team Mutual NDA + audit trail

◆ Validation

Before anything ships, it is tested to break.

The same adversarial process that has killed more of my own strategies than it has kept, turned on your models, your backtests, your assumptions. What survives is what you deploy. Nothing reaches your book on trust alone.

06 /Engagements

Three ways to work together.

01

Project

Build

Custom systems delivered end to end, on your data, the source owned outright. Scoped by a fixed-fee sprint and delivered milestone by milestone.

02

Retained

Embedded principal

Fractional quant engineering for a CIO or PM: architecture, vendor assessment, technical review, and hands-on build. The capability of a senior hire, without the headcount.

03

Cohort

Training

Bring your analysts up on the systems and methods, taught from the same practitioner stack a live fund runs on. Hands-on or cohort-based.

07 /vs The Alternatives

Stack us against what your firm would otherwise do.

A sceptical buyer's mental matrix, drawn explicitly. Pick the column that fits.

Off-the-shelf platforms
(enterprise wealth-tech, AUM-priced)

Big consulting
(global firms & systems integrators)

In-house hire
(quant dev + data eng + ops eng)

Dark Matter R&D
Custom build, you own it

Cost

~$75k–$300k+/yr, AUM-based, recurring forever

$500k–$5M+ per engagement, partner-rate

$450k–$800k/yr per senior quant eng; team of 3 ≈ $900k–$1.6M/yr

Scoped to the build. A fraction of any of these, then it's yours

Fit to your workflow

Generic. You bend to it

Custom, but slide-heavy

Custom, if they stay

Custom. Scoped to you, on your data

Time to value

Months of onboarding

6–18 months

6–12 months to hire, then ramp

Weeks, milestone-gated

Who builds it

Vendor PM + global support

Team of juniors, partner sells

Whoever you can hire

A fund operator. End to end.

Ownership

Vendor lock-in, your data on their cloud

Deliverables + ongoing dependency

Yours, and the key-person risk

Source & data fully yours, in your environment

What happens year 2

Renewal invoice

Statement of work #2

Salary + benefits + bonus

It keeps running. Optional retainer if you want changes.

With

Every claim in the memo cites the filing it came from.

Without

An analyst's spreadsheet nobody can audit six months later.

With

The system runs in your environment, every action logged.

Without

Firm data sitting in a vendor's cloud you can't inspect.

Platform figures are third-party estimates: enterprise wealth-tech platforms typically price on assets under management and do not publish rates. Consulting and in-house figures are typical loaded costs, not quotes. Comparison is illustrative, for reference only.

08 /Why Us

A fund desk that builds. Not an agency that read about finance.

Ryan Germain

Founder & Principal Engineer

I run Dark Matter, a systematic, market-neutral digital-asset fund, and built the market-intelligence system it trades on: data pipelines, research and forensic engines, execution, and a conversational layer that answers any market question with cited research.

Through Dark Matter R&D I build systems of that caliber for other firms, on their data, in their environment. You work directly with the person writing the code, not a junior or an account manager. The distinction that matters: the fund trades on these systems every session, so when a pipeline breaks or a backtest turns out to have been lying, it costs me money the same day. That is a materially different incentive from a consultancy whose deliverable ends at handoff, and it is why the work is built to survive a live book rather than a demo. If your team is doing by hand what well-built infrastructure should handle, I'd welcome the conversation.

What that means for you

We run what we build

A live trading desk. We know what survives an IC, an auditor, and a Monday open, because we ship to ourselves daily.

One principal, end to end

The person scoping your build writes the code and stands behind it. No juniors, no telephone, no offshoring.

Committee-ready by design

Source-traceable output, cited to the document. Built to stand up to an IC and an auditor, not to impress in a demo.

Your data, your environment

Runs under your controls, full audit trail. Source and data yours outright, no positions ever touched.

09 /Frequently Asked

Anticipating the obvious questions.

If your IC, CTO, or compliance officer would ask it, it's probably below.

Where does our data live?

Your environment: AWS, Azure, GCP, or on-prem, in your account and VPC. We never hold or move your data into a vendor cloud. Mutual NDA on every engagement, signed before the scoping sprint.

What if you get hit by a bus?

You have the full source and documentation in your repo from day one. A qualified engineer can pick up where I left off. No black-box hosting, no proprietary runtime, no lock-in.

Do we need a CTO to maintain it?

No, it runs unattended. For changes, retain me monthly, hand it to your team, or hire a freelance engineer. Most pick the retainer because it's cheaper than one developer day.

Why not just buy an off-the-shelf platform?

Off-the-shelf platforms force your workflow to fit their schema, charge in perpetuity, and don't extend to the messy edges: your bespoke statements, your custom diligence, your specific monitoring. If a platform fits, use it. If it doesn't, the manual work isn't going away, and that's where I come in.

How is this not just another "AI consultant"?

I'm not packaging a generic LLM behind a chatbot. What I'd build for you is purpose-built infrastructure where AI is one layer over a deterministic, source-traceable engine. Every claim cites the underlying document or data point, and the output stands up to an investment committee.

What's the typical engagement size?

Scoping sprints are fixed-fee and fully credited to the build. Builds scope from single-workflow systems through enterprise-scale infrastructure. The sprint produces a written spec with a fixed quote, so you decide on a known number, not an open meter.

What language and stack?

Python for data, machine-learning, and quantitative work; C++ / Rust in performance-critical paths (execution, tick-level signals); TypeScript / React for any UI. The right tool for the job, every choice documented so your team or a successor can read the code.

Will you sign our paper?

Yes. Mutual NDAs, MSAs, your DPA. I welcome code review by your CTO, an external auditor, or a trusted advisor. I'd rather you verify it than take my word for it.

◆ Engagements we'd decline

Anything that requires us to hold, custody, or trade your assets
"Build us an AI hedge fund from a YouTube tutorial"
Generic CRM, HR, or marketing automation outside finance
Greenfield SaaS MVPs unrelated to the buyer's existing book
Pure advisory or PowerPoint engagements without a shipped artifact
Anything we wouldn't ship to ourselves

Start a Conversation

Where vendor platforms end,
we begin.

Bring one process. The one your best people lose a day a week to and everyone agrees should be automated: the reconciliation, the diligence read, the reporting pack, the data pull nobody trusts. Tell me what it costs you in hours and where it breaks.

You get a working diagnostic: whether it should be built, what it would take, and where the hard parts are. If a platform already does this well, I'll tell you that instead. Direct, no slide deck.

Engagements taken selectively. Principal-led, in sequence, on the work that matters

Runs in your environment Ownership source & data are yours Audit full trail, committee-ready Delivery milestone-gated

Prefer to write? · ryan@darkmatter.financial

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