Production-first AI engineering
Turn AI code into stable products, not codebase debt.
I'm a former FAANG technical lead with 10+ years of software leadership and 2+ years of AI-agent delivery. I help teams and founders use AI as a force multiplier without losing architecture quality, test discipline, or launch control.
Built from hard-won engineering reality
What teams miss with default AI workflows
- Context collapses as teams jump sessions and lose prior decisions.
- Generated features ship faster, then stall on quality and testing debt.
- Hidden token burn and retry loops erode speed and budget.
- Maintenance gets harder because architecture decisions were never documented.
I solve these with predictable process, not hype.
Community signals from teams using coding agents
The recurring pain points are no longer opinion — they're patterns
In real user discussions, teams repeatedly describe the same three failures: context loss, quality drift, and runaway cost. We design around each one.
Context collapse and repeat work
Agents spend too much time orienting themselves and then forget key intent. Our workflows lock key decisions in one shared architecture map and prevent repeated detours.
Quality drop in long sessions
Long unstructured sessions often reduce signal quality. We segment execution into verified stages: design, implement, test, and hardening.
Token volatility without forecasting
Teams pay for retries, extra exploration, and accidental back-and-forth. We track decisions and expected runtime so budget and velocity stay transparent.
Trust gap for non-technical founders
Teams need understandable artifacts they can trust, not black-box outputs. Every stage generates clear checkpoints, risk notes, and owner actions.
Operating model
How we eliminate the usual AI delivery failure modes
1. Architecture first
Every initiative starts with scope, acceptance criteria, and failure modes. This reduces ambiguity before generation and keeps the model focused.
2. Context memory layer
We build a compact "working memory" for each phase. Agents reuse the right context only, instead of reloading entire codebases repeatedly.
3. Verification gates
Code, tests, and security checks are tied to acceptance gates. You decide go/no-go at every stage before release cost increases.
4. Fast launch support
We prepare launch plans, policy notes, and post-launch incident patterns so your team can go live quickly without operational surprises.
What this removes
Built for teams that want control, not technical debt
Ambiguity after each handoff
Every phase produces decision logs and assumptions, so teams can pick up where they left off without rediscovering architecture choices.
Ownership confusion
You keep technical ownership and governance rights. I support both direct owner-led and managed launch workflows with explicit boundaries.
Execution bottlenecks
Work is split into independent gates and clear deliverables, so progress does not depend on one person being available 24/7.
Execution roadmap
What happens the first two weeks
Teams get a concrete plan instead of a one-time "AI experiment" promise.
Days 1–2: Scope lock
- Map the product goal, technical risks, and non-negotiables.
- Define success metrics your team can verify in one review.
- Pick the exact tools, prompts, and delivery cadence.
Days 3–7: Controlled build loop
- Set checkpoint format for every AI-assisted session.
- Generate features in bounded slices with rollback criteria.
- Validate each slice against architecture and testing gates.
Days 8–14: Launch readiness
- Run final security, policy, and release checks.
- Prepare support playbooks and ownership handoff.
- Run a pre-launch decision review with clear go/no-go points.
Service pathways
Choose the support level that matches your timeline
Starter Blueprint
$497 start
- 2-week architecture sprint
- Feature-by-feature coding workflow map
- Quality gate checklist and risk log
- Technical playbook for founders/ops teams
Most selected
Launch Readiness
$997 start
- Implementation backlog with dependencies
- Automated review gates and regression test plan
- Pre-launch launch-readiness checklist
- Post-launch triage for first 2 sprints
Managed Delivery
$1,497 start
- Done-with-you architecture + implementation ownership
- Build support through launch and publication
- Compliance support and policy alignment
- Operational handoff and optional maintenance
Who this is for
Built for people who ship fast and cannot absorb silent risk
Creator operators
You build fast products and need technical governance to protect your core user experience when growth arrives.
High fit
FAANG-level operators and startup founders
You need enterprise-grade confidence without enterprise-sized teams. You get standards that scale without adding process drag.
Independent builders
You can move quickly on prototypes. We provide milestone discipline and release confidence for the transition to paying users.
Founder credential
From a technical lead perspective, not a hype perspective
I have 10+ years of leadership in large systems and teams at FAANG, with 2+ years of hands-on AI coding-agent orchestration. The difference is simple: I treat AI as a high-velocity engineer that needs strict interfaces, test contracts, and architectural boundaries to be useful.
That means faster delivery, less firefighting, and a cleaner path to user growth.
FAQ
Common founder questions before first contract
Do I need to give up my current stack?
No. This is a process layer on top of what you already have. We improve delivery discipline without forcing migration.
How much time does a kickoff take?
Most engagements start with a 45-minute technical intake and a one-page delivery blueprint before generation begins.
Will you review outputs or just manage prompts?
We review architecture consistency, test coverage, and release risks end-to-end, and only ship milestones with explicit acceptance.
Can you support solo technical founders?
Yes. For solo teams, services are packaged to keep communication simple and decisions fast with explicit weekly checkpoints.
What deliverables do I get after engagement?
You receive an architecture decision log, checkpoint reports, backlog map, and a launch readiness packet you can reuse.
How do you price across changing scope?
Each package is fixed at entry level and scoped on milestones; scope changes are treated as transparent change requests before approval.
Proof you can rely on
Trusted by operators who are moving from prototypes to production
“Our AI iteration time dropped, but this gave us guardrails before every handoff. No more last-minute rework.”
“We stopped losing architecture context across sessions and started finishing sprints with fewer surprises.”
“The process feels like internal engineering leadership, powered by AI where appropriate and checked by standards.”
Start here
Want a technical audit of your current AI workflow?
Get a direct assessment across architecture, tooling, context strategy, and launch risk before your next sprint.