Technical partner for AI founders.
From idea to working product — without burning money on the wrong architecture.
20+ years of engineering. Built and sold my own B2B company. I understand both the technical and business side.
30 minutes · free expert assessment · clear next step
Sound familiar?
Three situations founders come to me with.
Have an AI product idea
No technical lead. No idea where to start. Scared of burning money on the wrong approach.
AI Strategy Session → Architecture Blueprint → MVP
Have a prototype that won't scale
The no-code MVP works, but the architecture is fragile. Rewriting feels expensive and risky.
Audit → Production Architecture → Migration
Have a team but no tech leader
Developers are there, but who makes architecture calls? Who owns the technical strategy?
Fractional CTO → Architecture + Strategy + Growth
Start here
AI Project Assessment
A focused 30-minute session before you spend money on development.
I'll review your idea, pressure-test the technical risks, and tell you what to do next: validate, prototype, build an architecture blueprint, or stop before it becomes expensive.
- Is this AI idea technically realistic?
- Where does AI create real business value?
- What architecture risks could burn money later?
- What is the smallest useful next step?
Why founders trust me
I don't just write code. I've built a company, shipped products, and closed enterprise deals.
Maxim Breger
Founder · Engineer · AI Architect
Founder path
Built Avtology — a B2B technology company in automotive data. From zero: product, team, corporate clients, market entry. I understand the founder journey because I've lived it.
Engineering depth
20+ years: FinTech, Telecom, high-load systems, distributed architectures. Led engineering teams. Made architecture decisions that ran in production for years.
Building AI products today
Shipping AI-native products right now. ATMO — nervous system training with on-device AI and camera HRV (App Store + Google Play). AI agents, automation, intelligent systems. Not slides — production code with real users.
What happens after the review
If the idea is worth pursuing, the path is simple: architecture, MVP, then ongoing technical leadership when needed.
AI Idea Review
Got an idea? Let's figure out if there's real business potential here — and where AI actually adds value.
You get: Honest assessment of the idea, risks, and a clear next step.
Complexity · timeline · budget range · next step
Book Free Assessment →AI Architecture Blueprint
Before you invest in development — get a technical plan and risk map. Think of it as insurance against an expensive rewrite.
You get: Architecture, technology choices, cost estimate, roadmap.
1–2 weeks · Document + review session
MVP Development
Building the first version that works. Fast iterations, standard tech, no vendor lock-in.
You get: A working product you can show to users and investors.
4–12 weeks · Depends on complexity
Fractional CTO
Technical partner for the growth stage. Architecture, team, product evolution, technical strategy.
You get: Systematic technical decisions without hiring a full-time CTO.
Ongoing partnership
Proof of work
Case Study: RAMP
Reputation Architecture for Non-Linear Markets
RAMP is an AI-native reputation and discovery system built for one of the hardest environments on the internet: Reddit.
It does not behave like a content tool. It behaves like a learning architecture.
The system scans live communities, identifies emerging conversations, scores opportunities, generates persona-calibrated responses, routes every action through human approval, and learns from outcome signals: edits, engagement, deletions, replies, karma movement, and trust decay.
What makes RAMP valuable is not the generation layer. Generation is cheap. The value is in the operating system around it.
- Risk gates and reputation phases
- Human-in-the-loop approval
- Feedback memory and timing logic
- Avatar health and trust decay tracking
- Subreddit intelligence
- Continuous recalibration
RAMP is a working example of the architecture I help founders build: AI systems that do not just produce outputs. They observe reality, update themselves, and become more useful with every cycle.
gorampit.com →Cases
A few examples of turning technical uncertainty into products that work.
Main AI Case
ATMO / NeuroYoga
Co-Founder / CTO
Problem: build an AI-powered nervous system training product with privacy by design. Approach: on-device AI, C++17 engine, camera-based HRV. Result: App Store + Google Play, $500K pre-seed, real users, all data stays on device.
neuroyoga.app →Avtology
Founder / CTO
Problem: turn automotive data into a B2B product. Result: built from zero: product, team, enterprise clients, market entry.
AI Product Engineering
RAMP
Technical Founder / Architect
A production AI system for reputation discovery, human-approved execution, and continuous learning in Reddit communities. Not a content tool — a closed-loop operating system for community intelligence.
gorampit.com →Enterprise Engineering
FinTech · Telecom · High-load systems · Distributed architectures · Engineering leadership
How I think about work
Your code belongs to you
Standard tech. Any developer can pick it up. No vendor lock-in, ever.
Fast launch, no tech debt
Ship quickly — but with the right architecture underneath. You won't be rewriting in 6 months.
Brutally honest assessment
If the idea won't work technically, or AI doesn't add value in your case — I'll tell you straight.
Business outcomes, not billable hours
I measure success by products shipped and risks eliminated — not by lines of code written.
What people say after working with me
"Saved us from a $40K mistake. We were about to build on an architecture that wouldn't survive six months."
"He translates between business and tech. For the first time I actually understand what we're building and why."
"Knows what AI changes and what it doesn't. That's rare — most technical people either overhype it or dismiss it."
"Doesn't just advise — he builds. Everything he recommends, he's already tested on real projects."