Velocity Framework
The public, working playbook for AI-augmented engineering. Built in the open to help teams ship faster with rigor, clarity, and measurable quality.
Pods are the unit of velocity
AI-augmented engineers plus an AI Delivery Architect form the core operating unit.
Quality gates by default
Automated and human review, testing, metrics, and security hygiene are built-in expectations.
Built in public
You see the exact methodology we run. Nothing is filtered for sales and nothing is hidden.
Human + AI is the default
Each change pairs AI assistance with human judgment so speed never replaces accountability.
Teams, not soloists
Velocity Pods operate as a unit with clear roles and shared responsibility for quality and delivery.
Quality and security first
Strict gates, testing discipline, and clear AI safety hygiene.
AI-native, not AI-decorated
Workflows start with AI capabilities and keep humans accountable for outcomes.
Human + AI > Human
AI accelerates the work, but engineers remain accountable for correctness, quality, and outcomes.
Quality is non-negotiable
Quality gates, reviews, security hygiene, and metrics are baked into every change.
Transparent and open
The framework is public by design. It is the methodology we actually use, not a sales pitch.
Portal areas
Everything lives in the open
Explore the methodology, see how onboarding applies it, and read the ongoing narrative of what we learn and ship.
Framework
Philosophy, pods, workflows, governance, quality gates, security, and the AI Delivery stack.
- AI-augmented engineer playbook
- Velocity Pods & roles
- Quality, metrics, security
- Golden Stack and standards
Developer Onboarding
A guided path that applies the framework to bring new engineers up to speed.
- Phased onboarding journey
- Environment & tooling setup
- Rules and quality gates in practice
Blog
Narratives, deep dives, and lessons learned as we evolve the Velocity Framework.
- Real-world experiments
- Process improvements
- AI patterns and prompts
How the Velocity loop runs
A six-step loop built for AI and humans together
The framework favors small, reviewable batches. Each iteration is measurable and reinforces the next one with clearer context, stronger rules, and better prompts.
Define & clarify
Tighten intent with stakeholders and AI-assisted clarity prompts before typing code.
Write the spec
Capture architecture, data flows, constraints, and risks in a lightweight technical spec.
Generate the plan
Break work into safe, reviewable increments tied to files, functions, and tests.
Implement & review
Ship with AI assistance but review every diff for structure, correctness, and alignment.
Test & iterate
Run and expand tests with AI help; feed failures back into the spec and plan.
Finalize & document
Produce PR narratives, changelogs, and docs so teams can reuse what worked.
Get started
Use the framework and adapt it for your teams
Start with the framework overview, walk the onboarding path, or jump into the blog to see how the methodology evolves. There is no marketing fluff. It is the operating system we run with AI. If you reuse it, we’d love to hear your results.