Codebase archaeology
Rapidly understand large, undocumented codebases
Our Approach
// sound familiar?
Every engagement is different, but the pattern is the same: understand the problem deeply before touching the code, prove the approach with quick wins, then build momentum toward the real fix. Here's what that looks like.
The Problems We Solve
⚡
"Our codebase is slowing us down"
Legacy system modernization. We find what's actually causing the drag — it's rarely what the team thinks — and build a practical path to fix it without stopping feature work.
🔧
"We have tech debt we can't get ahead of"
Tech debt triage and paydown. Not all debt is equal. We identify what's costing you real velocity, then systematically retire it — measurably.
📐
"We need to scale but our architecture won't"
Architecture assessment and evolution. Whether it's a monolith that needs thoughtful decomposition or microservices that need consolidation, we find the right shape for where you're going.
🔬
"We need engineering depth but can't commit to a full-time hire"
Principal-level engineering on your terms. Embedded with your team, working on your hardest problems, for as long as you need — with the judgment and autonomy that means no ramp-up theater.
These problems share a common trait: they resist simple fixes. They need someone who can hold the whole system in context — the code, the infrastructure, the team, the business constraints — and find the right lever.
One Engineer, Every Layer of the System
// principal depth – where it's needed
You don't need three specialists and a project manager. You need engineering depth that spans the whole system — backend, frontend, infrastructure, data, team dynamics — with enough judgment to start making it better on day one.
That's what twenty years of engineering across every layer of the stack delivers, amplified by AI-enhanced practices. Faster codebase comprehension. Broader technical fluency. The ability to hold your entire system in context and find the right lever.
The result: one engagement delivering impact across domains that would traditionally require multiple specialists.
Don't take our word for it. Here are real numbers from real engagements.
Cross-stack fluency
Move between backend, frontend, services, and infra without context-switch overhead
Technical writing
Architecture decision records, migration plans, and runbooks alongside code
Judgment-driven velocity
AI handles the mechanical; experience handles what to build and why
Results
// real numbers from real engagements
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%
storage reduced De-normalized billion-row tables on a Rails e-commerce platform, reclaiming 50GB in 8 weeks.
0
-person
team turnaround Stepped into a healthcare project in crisis. Reset the engineering team in 4 weeks while repairing a critical client relationship.
0
%
faster onboarding Streamlined new customer implementations at a retail communications SaaS platform.
0
-week
data strategy Built a complete event pipeline and audit dashboard using Kinesis, Parquet, and DynamoDB.
Curious what the process behind these numbers actually looks like?
How Engagements Work
Week 1 / Listen
We embed with your team, read the code, attend the standups, and understand the constraints. No prescriptions yet — just honest observation.
Weeks 2-4 / Diagnose & Ship
We deliver a clear-eyed assessment of what's actually wrong. We start shipping fixes immediately — quick wins build trust while we work on the larger plan.
Ongoing / Flexible Commitment
2-3 days a week, full weeks during crunch, advisory as things stabilize. We scale to what the problem needs, not to maximize billing.
Handoff / Leave It Better
Document everything. Transfer knowledge. Make sure the team can maintain and extend what we built — without us. That's the goal.