Dark Matter · R&D. Quantitative infrastructure for data-driven businesses.

01 /The Problem

Your best people rekey data instead of making decisions.

Every data-heavy business carries the same gap: workflows too specific for off-the-shelf software, edges too proprietary to hand a consultancy. The work is automatable, but building it properly means standing up a full quant-engineering function. So it stays manual, and your most expensive people spend their week on data entry instead of judgment.

Days

a week your most expensive people lose to rekeying, reconciling, and assembling reports by hand

12+

disconnected tools and exports where there should be one clean, queryable data layer

Months

a single launch, deal, or decision can absorb without dedicated data infrastructure

$1M+

the fully-loaded cost of standing up an in-house data and quant-engineering team, per year

The pattern is the same across every data-heavy business: the bespoke layer is too specific to buy off the shelf and too costly to staff, so it stays manual.

02 /What We Build

The manual work disappears. The output gets better.

01

Answers in days, not months

The entire document set read, cross-checked against your data, and cited to the source page. The contradictions and red flags surfaced before a decision, not after.

02

One verified view, updated automatically

Every source, every feed, one clean number. No rekeying, no reconciliation by hand. Your reporting goes from a multi-day process to minutes.

03

Ask a question, get a cited answer

All your data in one place, with an analytical layer that answers questions instead of making you open twelve tabs.

04

Your workflow, automated exactly

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

Everything above runs in production today.

Every capability on this page is something we already operate inside the Dark Matter Terminal. We don't research the problem. We adapt the solution to your data and your workflow.

03 /Compliance & Control

The reporting and control layer underneath everything you run.

Two buyers, one system. The team that wants research and data, and the team that owns reporting, controls, and audit, buy different halves of the same platform. This is the half built to the standard your auditor and regulator will hold it to.

Lead capability

Compliance & reporting automation

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

Reports generated in your required formats (risk, exposure, financial, regulatory, limits monitoring)
Automated reconciliation across every system, feed, and data source
Audit-grade trails on every figure, source-traceable to the document
Stakeholder, investor, and board reporting produced on schedule, not by hand

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 a regulator.

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

15–25%

of skilled 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 reconciliation deployment we built

Ranges reflect published reporting-automation ROI analyses and our own deployment. The last figure is measured from a system we built and run.

◆ 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

4 of 5 management claims contradicted by the numbers. Caught automatically.

We run a systematic, market-neutral fund. The infrastructure it trades on every day is the same machinery we build for clients: forensic document analysis, automated reporting, research, and a conversational layer that answers any question with cited sources. Not a slide deck. Production systems with real consequences.

Run on a sample document set, the diligence engine flagged margins called "expanding" that had contracted, "deleveraging" while debt doubled, and "strong cash conversion" at 27%. That is the standard everything ships to.

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 · 4 of 5 claims contradicted, cited to source page

Conversational layer · a live market question, answered with cited research

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 business would otherwise do.

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

Off-the-shelf platforms
(enterprise SaaS, priced to scale with you)

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, 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 report cites the source document 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 SaaS platforms typically price on scale 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 live trading desk that builds. Not an agency that read about the problem.

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 businesses, 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 CTO, compliance lead, or head of ops 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 your auditors and your board.

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 builds
Greenfield consumer SaaS MVPs unrelated to your core operation
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, audit-ready Delivery milestone-gated

Prefer to write? · ryan@darkmatter.financial

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