Output doesn't match the team you're paying for.
You're paying for a full engineering team but getting fraction-of-capacity results. The calendar says forty hours. The roadmap shows ten.
cat ./mission.md
Your engineering team is capable of far more than they're currently shipping.
devyasa is the home of the AI-First SDLC Pipeline — a structured, Claude-powered workflow that helps SMB engineering teams deliver 5–10x the output without growing headcount.
// One engineer shipping what used to take a team.
// THE PROBLEM
You already know something is off. Here's what you're feeling.
No structure, no consistency.
Different engineers make different decisions. Documentation gets skipped. Tests come after the fact — if they come at all. Every feature is a new negotiation.
More engineers won't fix it.
Adding headcount without fixing the underlying workflow just scales the chaos. More people shipping in more ways, and still no compounding output.
// THE SOLUTION
The AI-First SDLC Pipeline is the answer.
A structured, AI-assisted workflow that takes a product from idea to production — and governs every change after that. Every future change re-enters the pipeline at the earliest step it affects, so nothing slips past the gates it should be checked against.
The pipeline is designed to be run by any engineer on your team, with Claude as the primary technical collaborator. Every step produces deterministic, reviewable artifacts. No guessing. No arbitrary decisions left to the AI. No "we'll figure it out in implementation."
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Built for engineers at every level
Claude does the heavy technical lifting. The pipeline turns any engineer into a high-leverage operator — from their first day on the team.
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The same foundation, every time
Clear requirements. Documented architecture. Validated schema. Tested contracts. Automated CI/CD. Verified deployment.
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Made for SMB teams
Not enterprise consultants. Not solo developers. Teams of 5–50 engineers who need to ship more without hiring more.
// THE PIPELINE
From idea to production — in thirteen structured steps.
Each step produces a named artifact. Each artifact must pass a check before the next step begins. Hover a step to see what it delivers.
tree ./pipeline
- Feature Spec
→ produces Locked feature decisions, ready for requirements input
- PRD
→ produces Requirements without implementation detail
- Architecture & Tech Stack
→ produces Stack, patterns, and API design
- Data Modeling
→ produces Schema, migrations, and access patterns
- UI/UX Design
→ produces Wireframes, component library, Stitch prompts
- Infra Setup
→ produces Terraform IaC, environments, runbook (written, not yet applied)
- TDD
→ produces Contracts, directives, discipline-based milestone map
- Implementation
→ produces Working codebase via subagents and CLAUDE.md
- CI/CD Pipeline
→ produces CI workflows, deploy pipelines, DORA metrics
- Deployment Foundation
→ produces All three environments provisioned, first staging deployment
- QA
→ produces QA report, traceability matrix, human sign-off
- Deployment
→ produces Deployment record, monitoring reports, Gate 2
- Maintenance
→ produces Cycle records, compatibility matrix, CHANGELOG
// 13 steps, 9 hard gates, 0 arbitrary decisions.
// Multiple steps and disciplines running in parallel.
// Every future change re-enters at the earliest affected step.
// WHY IT WORKS
Five principles. The whole thing rests on them.
P1
What, not how.
Every requirement document answers what the product must do — never how to build it. This prevents engineers from building the wrong thing with impressive technical precision.
P2
Explicit and teachable.
The pipeline is designed so any engineer can execute it with Claude as their collaborator — from day-one hires to senior leads. This is not a constraint — it is the point. The workflow is explicit, teachable, and not locked in the heads of senior engineers who might leave.
P3
Deterministic outputs at every step.
Each step produces a specific artifact that must pass validation before the next step begins. No arbitrary decisions left to Claude. No undocumented choices. No "we'll figure it out in implementation."
P4
Human gates where it matters.
Nine hard gates across the pipeline. AI does the work — a human approves before it propagates downstream. The faster you move, the more expensive mistakes become. The gates are cheap.
P5
AI does the work. Humans direct it.
The pipeline runs on two layers. In one, you and Claude decide what to build and produce the plans. In the other, autonomous Claude Code sessions execute against those plans — writing code, running tests, verifying contracts. The clean separation is why any engineer can run the pipeline: they're orchestrating, not coding.
// ABOUT
devyasa
AI-First Engineering Advisory
About devyasa
devyasa is the practice of Peter Trennum, an AI-First Engineering Strategist working with SMB engineering teams.
I've spent more than two decades building and leading engineering teams across just about every kind of industry, stage, and tech stack. I built the AI-First SDLC Pipeline to solve a problem I kept seeing: strong teams shipping a fraction of what they could — not because of a skill gap, but because of a process gap.
The pipeline has been refined across a portfolio of products and is now packaged as a system any team can adopt.
I write about agentic engineering, AI-first workflows, and the practical realities of building software with AI — for founders who want to understand what their engineering team is actually doing, and for engineering leads who want to do more with the team they have.
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5–10x output
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Zero headcount growth
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13-step pipeline