Our Engineering Philosophy

The Principles We Engineer By

Software should get easier to change as it grows. Here is the engineering stance behind every system we design and line of code we write.

01

Complexity is debt with interest.

Every line of code, dependency, or server instance is a liability. We design to delete moving parts. Simple code costs less to write, takes minutes to debug, and scales under load.

As codebases grow, developers add abstractions for hypothetical future features. We do the opposite. We prune unused layers, trim dependency trees, and choose direct code over clever metaprogramming.

// Code Comparison / Example
// Speculative factory abstraction
class DatabaseProxyFactory {
  constructor(provider) { this.provider = provider; }
  async execute(query) { return this.provider.query(query); }
}

// Direct approach: query the database directly
const result = await db.query("SELECT * FROM users WHERE id = $1", [id]);
02

Determinism before AI.

LLMs are probabilistic. Before adding an AI model to your stack, check if a database query, state machine, or simple regex can solve it. Keep your system deterministic where possible.

AI is a powerful tool, but it is slow, costly, and non-deterministic. We follow a strict ladder: if a task can be resolved with standard algorithms, SQL queries, or regex, we implement those first. We spend tokens only when human-like reasoning is required.

// Code Comparison / Example
// Probabilistic classification (slow, expensive, variable output)
const category = await llm.classify(ticketText); 

// Deterministic alternative (0 token cost, 100% consistent)
const category = ticketText.match(/billing|invoice|payment/i) ? 'billing' : 'support';
03

Prefer boring technology.

We build on stable, mature tools—Rails, Next.js, PostgreSQL, Redis. Push native capabilities to their limits before reaching for new frameworks.

Mature stacks are predictable and well-documented. Instead of deploying a dedicated vector database, we use Postgres pgvector. Instead of managing complex microservices, we optimize a modular monolith.

// Code Comparison / Example
# Adding pgvector to a PostgreSQL migration (zero extra servers)
class AddVectorExtensionToDatabase < ActiveRecord::Migration[7.1]
  def change
    enable_extension "vector"
  end
end
04

Simplicity scales.

Scaling isn't about rewriting your stack in Rust. It's about database indexing, query locks, queue performance, and clearing N+1 bottlenecks.

Teams often blame their framework when an app slows down. In 95% of performance audits, the real issue is missing database indexes, table bloat, or N+1 queries. Monoliths scale remarkably well when query paths are clean.

// Code Comparison / Example
# N+1 Query: fires 11 separate queries for 10 users
users = User.limit(10)
users.each { |u| puts u.profile.bio }

# Eager loading: runs 2 single queries total
users = User.includes(:profile).limit(10)
users.each { |u| puts u.profile.bio }
05

Architecture is leverage.

Good system design compounds. When your data model matches your domain, shipping features stays fast. When it doesn't, engineering grinds to a halt.

Clean architecture pays off every time a developer opens an editor. Clear boundaries and unidirectional dependencies make refactoring safe and prevent regressions.

// Code Comparison / Example
// Coupled layout: direct DB call inside UI component
function UserProfile({ userId }) {
  const user = db.query("SELECT * FROM users..."); // DB logic in view
}

// Clean architecture: decoupled layers
async function getUserProfile(userId) {
  return db.query("SELECT * FROM users..."); // Data access layer
}
function UserProfile({ user }) {
  return <div>{user.name}</div>; // View component
}

Want to discuss your codebase architecture?

We perform comprehensive codebase audits to identify performance bottlenecks, index bloat, and LLM orchestration leaks.

Book a Codebase AuditSee Our Capabilities