For regulated, asset-heavy operations

Every business obeys physics.Most run on precedent.

The feed rate. The pricing rule. The risk cutoff. Every number your business runs on was set by somebody once, then never questioned — and you've been paying for it ever since.

We go back to first principles: measure what actually drives the outcome, prove it, and build the system that acts on it. We call it Business Physics.

Clients see 12–20% margin improvement in 90 days.

See what variance is costing you →

Two weeks. We'll quantify where margin is leaking and what fixing it is worth. Sometimes it isn't.

30+ projects across pricing, risk, fraud and operations4 patentsRisk-model discipline from JPMorgan Chase

Business Physics · 70 seconds

Stop guessing. Start seeing the physics.

The hidden forces under your pricing, risk, and operations — made visible, tested, and brought under control.

Results From The Field

+20%

Credit Approvals

Auto Lender, Thin-File Segment

+12%

Gross Profit

National Construction Co.

60%

Cost Reduction

vs. Public API Costs

90 Days

To Measurable ROI

Typical engagement timeline

From Noise to Structure

Most teams operate on noisy, incomplete data.

These aren’t data problems. They’re physics problems.

Phenx isolates the signal, uncovers the hidden structures and real drivers of your business, and stabilizes decisions with AI that behaves like physics - predictable, explainable, consistently profitable.

Noise

Signal

Trusted in High-Stakes, Regulated Environments

30+ enterprise projects

4 patents (named inventor)

Regulated, audit-heavy expertise

Private, explainable, on-prem-ready systems

Who This Is For

You’re a good fit if:

A 5% pricing error costs you $500K+ annually
Your models work in backtests but fail in production
You’re in a regulated industry where “black box” isn’t acceptable
Your team is too small to hire a full data science org
You’ve been burned by consultants who delivered decks, not systems

Typical clients: CTOs, VPs of Analytics, and Heads of Pricing at $50M-$500M companies in construction, equipment rental, specialty finance, and asset-heavy operations.

Typical domains

pricing & estimating

credit & risk

fraud & abuse detection

asset-heavy operations & maintenance

regulated or audit-driven teams

Why Phenx

The only firm built on Structural Clarity.

This is not model tuning. This is structural engineering.

Most firms optimize models. We optimize reality.

Phenx treats your business like a physical system:

identify the true variables
measure the forces acting on them
separate signal from noise
engineer stable, predictable behavior

Instead of black-box “AI magic,” you get:

clear structural explanations
explicit assumptions
traceable mechanisms
systems that behave consistently under stress

We don’t sell models. We install structure.

What You Gain

What You Gain With Business Physics

Predictability

Systems that behave consistently under stress, not models that break silently.

Stability

Variance reduction across pricing, risk, fraud, and operations.

Leverage

Clear levers you can pull with confidence because you understand the mechanics underneath.

Visibility

A structural map of your system: forces, constraints, noise vectors, and dominant drivers.

Book your free diagnostic →

Our Method

The Structural Scientist Framework

A 3-layer architecture we apply to every engagement.

Clarity → Confidence → Control

1. Clarity Layer · Reveal the Structure

Map how your business actually behaves — not how you think it behaves.

How we do it

We begin by making the invisible visible. We map the architecture of your system and show how your business is actually behaving, not how you think it’s behaving.

price dynamics and margin behavior
fraud and abuse patterns
operational flows and bottlenecks
noise vectors and data quality breaks

2. Confidence Layer · Validate the Physics

Test the structure. Intuition becomes evidence.

How we do it

Once we see the structure, we test it. This is where intuition is converted into evidence. You stop arguing from anecdotes and start deciding from physics.

Bayesian systems to capture uncertainty and prior knowledge
hybrid ML (statistical + machine learning) to model true mechanics
Monte Carlo simulations to probe edge cases and tail risk
variance decomposition to find where instability really comes from

3. Control Layer · Engineer Predictability

Engineer decisions that hold up under real-world stress.

How we do it

With structure understood and validated, we design control. Control is about making the system behave predictably under real-world constraints.

recommend actions to estimators, underwriters, and operators
adjust pricing and limits in real time within safe bounds
predict risk, demand, failure, and attrition before they surface
automate decisions where confidence is high

Proof in Structure

Patterns We’ve Revealed in the Real World

Each insight shows how Phenx extracts the signal, names the structural drivers, and designs the repeatable playbook.

Case 01· 90 days from kickoff to production system

+12%

gross profit on repriced segments

National Construction Company

Estimators discounted “to be safe.” Margin swung ±18% on similar jobs.

Full case detail

Estimators were discounting “to be safe” - but no one knew which discounts were necessary and which were leaving money on the table. Margin variance was ±18% on similar jobs.

What we found

Hidden customer segmentation by urgency and project complexity
Non-linear demand curve - higher prices actually won more in certain segments
Estimator bias was costing 8-12% GP on 40% of bids

The outcome

+12% gross profit on repriced segments
Higher win rates (not lower) after removing unnecessary discounts
Pricing logic the team actually understood and trusted

Timeline: 90 days from kickoff to production system

Wireframe steel structure with a rising orange profit line

Case 02· 8 weeks to validated prototype, 12 weeks to production

+20%

approvals in the thin-file segment, no rise in defaults

Top-10 Auto Lender

40% of applicants had thin credit files: reject them all, or approve blindly.

Full case detail

40% of applicants had thin or no credit files. The existing model either rejected them all (leaving money on the table) or approved blindly (spiking defaults).

What we found

Behavioral signals buried in non-traditional data that traditional scores ignored
Default risk patterns tied to behavioral sequences, not static attributes
Explainability tuned for regulatory review

The outcome

+20% approval rate in thin-file segment
No increase in default rate
Regulator-ready model transparency

Timeline: 8 weeks to validated prototype, 12 weeks to production

Curve sweeping through a field of applicant dots, lighting a segment orange

Case 03· 6 weeks to detection system, ongoing monitoring

−30%

false positives — alerts the ops team could act on

Payments Platform

Fraud losses climbed 15% QoQ while the dashboards said everything was fine.

Full case detail

Fraud losses were climbing 15% QoQ, but the model’s validation metrics looked fine. The team couldn’t explain why reality diverged from their dashboards.

What we found

New fraud patterns emerged outside the training distribution
Model optimized for yesterday’s fraud, not today’s - attackers had evolved
Layered supervised detection for known patterns plus unsupervised for formation

The outcome

Detected net-new fraud clusters invisible to the old model
Reduced false positives by 30% - ops team could act on alerts
Hybrid system catching known patterns and flagging unknown formation

Timeline: 6 weeks to detection system, ongoing monitoring

Network graph with an anomalous cluster isolated in an orange bracket

What Clients Say

Proof from leaders who operate under real constraints

“They had significant domain and subject matter expertise. This shows in their overall approach and delivery of solution.”

Ranga Kothamasu

CEO of Cortex

“They were able to adapt to our company’s specific business needs and understand our industry.”

Director of Strategy

Simon Roofing

“Phenx is an integral component of our mission-driven strategy to deliver financial inclusion to underserved, credit-invisible consumers. They demonstrated tremendous value in applying deep learning to enhance the decisioning power of our risk segmentation model.”

CEO

Specialty Lender

Start with Structure

Get Your Free Business Physics Diagnostic

20 minutes. We’ll show you where variance is costing you money.

In 20 minutes, we’ll map the hidden forces driving your outcomes - and show you exactly where variance is costing you money.

No pitch. No fluff. If there's no structural insight, we'll tell you.

You'll leave with

3 dominant forcesTop noise sourcesPredictability Score90-day plan

✓The 3 dominant forces shaping your results
✓Your top noise sources (and which ones are fixable)
✓A Predictability Score for your current system
✓A 90-day action plan - whether you work with us or not

Only 4 diagnostic slots available per month.

Ready to map the physics?

Private, explainable, and deployment-ready systems.

If there is no structural insight, we’ll tell you that too.

Book Your Free Diagnostic →

A 20-minute structural assessment. Zero fluff.

Not ready to talk?

Get the Self-Assessment →

Download our 1-page Business Physics Self-Assessment and diagnose your own system.

StructureSignalsConstraintsControlsFeedback Loops

Built for Regulated, High-Stakes Environments

✓Private, on-prem deployable (no data leaves your environment)
✓Explainable models (audit-ready documentation)
✓Designed for regulated, audit-heavy environments
✓Enterprise SLAs with 24×7 support
✓Architecture patterns aligned with enterprise security standards

Founder: PhD in AI | 20+ years enterprise experience | JPMorgan Chase alumni | 4 patents (named inventor)