Product
Define the work.
Write structured contracts — event models, acceptance criteria, constraints — that the system actually executes. The gap between what you asked for and what shipped finally closes.
No translation layer.
AI-augmented SDLC · on-prem
Your team bought AI tools. Your velocity didn't move.
Stochastic Macro is the system Product, Design, and Engineering run together — with AI handling implementation inside human-defined constraints. One binary. Your machine. Every correction trains the next cycle.
// Single binary · any AI provider or local model · zero telemetry
No servers touch your code
SSO / SAML
Audit-logged
The adoption–outcome gap
AI made individuals faster. It hasn't made teams better.
Three data points your board already knows. The gap isn't talent or technology — it's the absence of a system the whole team runs through.
of AI projects fail
80 percent or more Twice the failure rate of traditional IT projects. Not pilots — production initiatives with budget and sponsorship.
RAND Corporation · 2024
show no measurable P&L impact
95 percent of Gen-AI pilots that ship but fail to move the numbers leadership actually tracks.
MIT Media Lab · 2025
report meaningful impact
Only 6 percent of Organizations whose AI investment delivered real business results. You want to be the 6.
McKinsey · 2025
$2–4M/ year · 50 eng team
In a controlled study, developers using AI tools believed they were 20% faster. They were actually 19% slower. For a 50-engineer team, that's $2–4M/year in rework you can't see on a dashboard.
METR, 2025 · estimate based on 19% productivity gap at $200K fully loaded cost. See methodology → How it works
Four gates. One pipeline. Nothing ships without human sign-off.
Stochastic Macro turns the SDLC into an explicit, auditable sequence — Product defines, AI implements, quality gates verify, Engineering approves.
01 / Define
Product writes the work
Structured contracts — event models, acceptance criteria, constraints. No more prompt-and-pray.
02 / Implement
Agents do the build
AI agents execute inside the contract. Any provider, any stack. Your keys, your machine.
03 / Verify
Gates before you look
Tests, lints, design-system checks, quality gates — all run before a human sees it.
04 / Ship
Engineering applies judgment
Review becomes verification, not discovery. Every correction trains the system.
One system · three roles
Built for teams that ship, not just teams that code.
Most AI dev tools are engineer-only. Product can't define what to build. Design can't enforce standards. So your SDLC has a bottleneck at the top and a free-for-all at the bottom. Stochastic Macro connects all three.
Design
Set the standards.
Embed design-system constraints directly into the delivery pipeline. Agents respect tokens, components, patterns — and you can verify compliance before anything ships.
Constraints, not corrections.
Engineering
Apply judgment.
Review code that already passed tests, lints, and quality gates. Review becomes verification — not discovery. AI handles implementation, you handle the calls only humans should make.
Nothing ships without sign-off.
Learning loop
Every correction makes it smarter.
Most AI tools make the same mistakes on loop. Stochastic Macro treats every review comment, every rejected PR, and every design correction as training signal.
Every correction is captured
Rejected PRs, design nits, re-scoped specs — all recorded with their context. The system knows why, not just what.
Process tuned, not just output
The refinement targets how the agent works — retrieval, context assembly, gate thresholds — not just the code it writes.
Your 100th cycle is categorically better
Teams without learning loops correct the same AI mistakes indefinitely. This is the difference between the 6% and the 94%.
The three p's
Predictable. Portable. Private.
The constraints every engineering leader I've ever worked with insists on — built in from day one, not bolted on later.
01 · Predictable
Same input. Same quality. Every run.
Structured workflows mean AI output is consistent and auditable — not a surprise with every cycle.
02 · Portable
No vendor lock-in. Ever.
Claude, GPT, Gemini, or any local model that speaks the OpenAI or Claude API. Mix and match per agent — a cheap model for the trivial calls, a frontier one for the hard ones. Swap anytime.
03 · Private
Your binary. Your keys. Your machine.
No Stochastic Macro servers, no telemetry, no cloud dependency. Your code goes only to the AI providers you choose.
Evaluation criteria
How to evaluate any AI SDLC platform.
Whether you choose Stochastic Macro or not, these are the criteria that separate tools that actually work from tools that just demo well. We welcome the comparison.
Cross-functional by design — not just an engineering tool.
Structured workflows — not prompt-and-pray.
Provider-agnostic — no single-vendor lock-in.
Quality gates before human review — not after.
Learning from feedback — not repeating mistakes.
Auditable by default — every decision traceable.
Runs on your infrastructure — your keys, your machine.
Measurable ROI framework — before/after data, not vibes.
Gradual adoption path — not an all-or-nothing bet.
Stack-agnostic — works with what you already use.
Pricing
One product. One price.
Every seat includes the complete SDLC platform. No tiers, no feature gates, no surprise add-ons. On-prem deployment, bring your own AI keys or point at a local model, 30-day evaluation included.
Early access
$59/ seat / month
The complete AI-augmented SDLC platform. Product, Design, and Engineering workflows. On-prem. Bring your own AI keys — or point at any OpenAI/Claude-compatible local model. Lock in introductory pricing before general availability.
Enterprise (100+ seats)?
Volume pricing →
- Full SDLC platform — Product, Design, Engineering
- Structured AI agent cycles with human review gates
- Any AI provider — or local models via OpenAI/Claude-compatible APIs
- Single binary · on-prem deployment
- Metrics dashboard — throughput, quality, cycle time
- Cross-functional team workflows
- SSO / SAML integration
- Audit logging + compliance reporting
- Priority support
Why I built this
“Teams bought AI coding assistants expecting velocity. What they got was more rework, slower reviews, and a failure pattern I'd seen my entire career — technology without process.”
John Wilger
Founder · 25 years shipping production software
I've spent twenty-five years building production software — distributed systems, enterprise SaaS, and the SDLC tooling that holds them together. I've led teams of five and teams of fifty. And what I kept seeing with AI dev tools was the same pattern: powerful technology, nonexistent integration.
Product couldn't define work in a way the AI understood. Design couldn't enforce standards. Engineering spent more time reviewing AI output than writing code themselves.
So I built Stochastic Macro — a structured SDLC platform where Product, Design, and Engineering work through one system, AI handles implementation within human-defined constraints, and every correction trains the next cycle.
I'm building it the same way I'd want any team to use it — structured specs, AI-assisted implementation, human review at every gate. The platform is its own proof of concept.
I didn't build this for everyone. I built it for teams that refuse to choose between quality and speed — and know the right system means they don't have to.
Request early access
Tell us about your team. We'll reach out.
No sales call. No demo required. We review every request individually. If your team is a good fit for early access, you'll hear from the founder within a few business days.
01 · Early access
Request access
Best for product teams of 5–25 engineers. Full product, onboarding support, direct line to the founder.
02 · Research guide
Not ready? Read the research first.
20 studies. A 16-question readiness assessment you can run with your leadership team in 30 minutes. Evaluation criteria for any AI SDLC platform — including ours.
- The 80/95/6 problem, sourced
- Why it's a process problem, not a tools problem
- 16-point gap analysis
- Pilot structure you can propose this week