AI got you 80% there. We get you to production.
gift-card conversions with an AI search assistant
higher average order value from contextual suggestions
lower data-storage cost, insights in hours not days
Your AI-built prototype got you here: a working demo, early believers, maybe investor conversations. But a prototype is not a production system, and the gap between them is exactly where things start to break.
AI-generated code often lacks structure, so changes don’t stay simple. Small updates start to demand rework, validation, and debugging.
AI code has 1.7× as many major issues as human-written code.
Early gaps in security and design spread into the foundation, limiting how safely you can scale and raising the risk of failure under load.
Around 45% of AI-generated code contains security vulnerabilities.
Silent rot surfaces under real pressure — growth, releases, peak usage — turning routine fixes into system-wide changes.
43% of AI-generated changes require debugging in production.
— everything works in controlled environments, then breaks in the wild
— across the system, not just where you touched
— time shifts from building to reviewing and fixing debt
— complexity grows with every change; scalability and security stay uncertain
We keep what already works and turn it into a system that holds under real usage, real load, and real scrutiny — even if it began as a throwaway weekend build.
A rapidly assembled codebase lacks a formal structure and a long-term architectural vision.
Scalable architecture designed for maintainability, stability, and future technical evolution.
The product appears stable until real usage puts pressure on it, a typical outcome of a 48-hour MVP.
Hardened infrastructure maintains consistent performance during peak user activity.
Unpredicted problems make every deployment a gamble on a structure that no one really understands.
Modular architecture operates according to strict principles: changes should be isolated, tested, and safely deployed.
There is uncertainty regarding technical viability, security gaps, and long-term scalability.
Transparent architecture and documentation provide full confidence during due diligence.
Your product works. The constraint now is the system behind it. We bring the discipline that makes it reliable, scalable, and provable — not just recommendations.
AI does the heavy lifting; a named senior engineer reviews and approves every release. People, not tools, answer for what ships — and can explain what changed, why it’s safe, and how to roll it back.
Our senior team includes Claude-certified architects, hands-on with AI development tools. We know where they hold and where they break — so we move faster and fix what actually matters.
Under our Verified Velocity method, each change ships an evidence package: what changed, test results, security scans, reviewer sign-off, and a tested rollback plan. No trust demanded — the package shows the work.
Calibrated autonomy: architecture, security, payments and personal data stay human-led; routine work runs automated inside strict checks. Autonomy is earned by verification strength, never assumed.
You don’t start over. We keep what works and re-found what doesn’t. Most systems retain the bulk of their codebase — on a stronger foundation that supports growth without constant fixes.
One senior pod designs the system and ships the changes. No gap between planning and delivery, no roadmap pause — a 90-day path with clear milestones and outcomes.
A structured path from an AI-built prototype to a system that holds — without slowing your team or disrupting ongoing development.
We review your system as it is — architecture, performance, maintainability. Untested code first gets a characterization test net that pins current behavior, so nothing changes blind.
A clear read on risks & what breaks under load.
We resolve the bottlenecks driving instability: define the architecture, harden dependencies, and install specs and quality gates. Every change is specified before it’s built and verified before it ships.
A stable, predictable system.
AI at scale with predictable performance under load, reliable deployment, and built-in governance — so ongoing development actually speeds up instead of adding complexity.
A production-grade, investor-ready platform.
We embedded governed AI into a live client platform’s daily delivery. Same team, same year. This is what changed against a flat prior-year baseline, at constant headcount.
engineering delivery output (merged pull requests across all repos)
code-review throughput absorbed with zero added latency
human-reported bugs — a full-year low, while output nearly doubled
platform-scale initiatives shipped concurrently: monorepo, CI, IaC, test suite, specs, docs
production errors auto-triaged & resolved — a workload that didn’t exist before
autonomous-run success rate at production volume (orchestration ramped 10× in a month)
Sara Lufrano
Principal Technical Product Manager, Space Needle
Always delivered on time. Highly responsive to our needs. 10/10.
Three steps, and you see the findings before you commit to anything. Most teams go from first call to a written risk readout without pausing their roadmap.
A senior, Claude-certified solution architect reviews your AI-built system — architecture, scalability, and stability under load.
A clear picture of the top risks, what will break under scale, and the exact next steps to make it production-ready.
Audit → foundation → scale: a production-grade, investor-ready system in 90 days, with proof you can audit on every release.
A senior solution architect reviews your AI-built system to assess architecture, scalability, and stability under load. You get a clear readout of the key risks, what’s likely to break as you grow, and the exact steps to make it production-ready — including recommendations for codebase audit and refactoring.
We evaluate how the system behaves under real conditions — load, data volume, user activity — and review architecture, test coverage, deployment process, and how changes affect stability. The goal is to identify risks early and define exactly what needs improvement before scaling.
We make targeted improvements without interrupting live usage — legacy or untested code first gets a characterization test net that pins current behavior — so your product keeps running while the system becomes more stable and ready to scale.
Immediate impact with no 3-month ramp-up, and a full senior pod rather than one person. Unlike big consultancies, we ship code, not slide decks — and we own both architecture and execution. No hiring delays, no split responsibility: focused work that hardens the system while your team keeps building.
Verified Velocity is our delivery method for AI-built systems: AI moves fast, but nothing ships unverified. Every release carries an evidence package — what changed, test results, security scans, reviewer sign-off, and a tested rollback plan — so you get speed you can audit, not speed you have to trust. Autonomy is calibrated to risk: routine work runs automated inside strict checks, while architecture, security, payments, and personal data stay human-led.
Under Spec-First delivery, every change is specified before it’s built and verified before it ships. We write a clear spec that defines the intended behavior, AI implements against it, and the result is checked back against that spec and its tests. For untested or legacy code, we first add a characterization test net that pins current behavior — so nothing changes blind. Changes stay isolated, predictable, and safe to deploy.
A production-grade, investor-ready system — plus everything that keeps it that way: a defined architecture, quality gates, specs, and the Verified Velocity evidence trail, all living in your own codebase. You keep the bulk of what already worked, now on a stronger foundation, and your team can keep shipping at speed without accumulating the debt that made scalingrisky in the first place.