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 3–5x the output without growing headcount.
// 20 engineers doing the work of ~26 — a 30% capacity expansion with zero headcount growth.
// 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.
The pipeline is designed to be run by junior engineers 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 junior engineers
Claude does the heavy technical lifting. The pipeline turns entry-level engineers into high-leverage operators.
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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 twelve 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
- 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 deploy
- QA
→ produces QA report, traceability matrix, human sign-off
- Deployment
→ produces Deployment record, monitoring reports, Gate 2
- Maintenance
→ produces Cycle records, compatibility matrix, CHANGELOG
// 12 steps, 6 gates, 0 arbitrary decisions.
// Every artifact reviewable. Every decision traceable.
// WHY IT WORKS
Four 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
Junior engineers, Claude-powered.
The pipeline is designed so entry-level engineers can execute it with Claude as their collaborator. 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.
Hard gates at PRD, Architecture, Data Modeling, Infra, TDD, and Deployment. AI does the work — a human approves before it propagates downstream. The faster you move, the more expensive mistakes become. The gates are cheap.
// ABOUT
devyasa
AI-First Engineering Strategist
About devyasa
devyasa is the practice of Peter Trennum, an AI-First Engineering Strategist working with SMB engineering teams.
After more than a decade building and leading engineering teams across [industries / companies], I built the AI-First SDLC Pipeline to solve a problem I kept seeing: teams with real talent shipping a fraction of what they were capable of — not because of skill gaps, but because of process gaps.
The pipeline has been refined across [n] 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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30% capacity expansion
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Zero headcount growth
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12-step pipeline