Our Process
Make it real early.
Make it correct before it ships.
Then improve — tighter, richer, and faster, every cycle.
We build software through a structured, iterative collaboration between people and AI — refined over a year of daily practice on a production platform.
At the center is a commitment to contract-driven development and accountability. AI accelerates the work — but it does not own it. People remain responsible for clarity, alignment, and truth across the system.
Nine phases
From intent to continuous evolution
01
Start with Intent
Every project begins with conversation — what's needed, what's possible, and what matters. We don't rush to specs. We work to understand the shape of the problem first. This is where accountability begins: clear intent, shared understanding, and explicit ownership.
02
Prototype Early
We move quickly into working prototypes. Designers and engineers collaborate to produce something visible and testable within days — not weeks. This creates a shared reference point and prevents misalignment from taking root before it has a name.
03
Let Structure Emerge
Only after the idea has taken shape do we formalize: data models, system architecture, technology choices. This ensures the system supports the idea — not the other way around. Decisions made here are explicit and owned. Nothing is implicit.
04
Align System and Interface
Front-end and back-end evolve together. Interfaces are designed to reflect real data structures as closely as possible, reducing friction and rework later. Fake data feeds emerging prototypes. This phase requires strong communication across roles and rewards it.
05
Establish Strong Patterns
We lock in reliable patterns early: clean API structures, async processing where needed, clear separation of responsibilities. We also formalize contracts — what each part of the system guarantees to others. These are explicit, versioned, and enforced. The system becomes coherent and extensible.
06
Maintain Fidelity
Documentation decays. Code moves. Assumptions drift. We use Puddlejump — our own structured human-AI governance workflow — to ensure what's written and what runs never diverge. Canon docs are versioned and frozen. Nothing changes without explicit human approval. Discrepancies are not ignored — they are resolved.
07
Test with Real Users
We introduce real users early and often. We test usability, clarity, performance, and edge cases — using Playwright for E2E coverage, Django unittest and pytest for the backend, and structured pre-test scenarios for every significant change. Feedback is built into the product, not collected after the fact.
08
Release Thoughtfully
We release in controlled stages: internal previews, limited-access versions, incremental feature rollouts. Each release is intentional and supported by stable contracts and verified behavior. Stakeholders are shown real logins, solid features, and compelling prototypes — in that order.
09
Continue, Don't Restart
We don't treat launch as an endpoint. The system is designed from the beginning to grow: new features integrate cleanly, data models hold, documentation stays aligned. The loop from intent to prototype to release runs again — faster each time, because the foundation is sound.
AI-Collab
Not AI-driven. Not AI-adjacent. AI-Collab.
AI is part of our process — but not a replacement for judgment. We use it where it excels: rapid prototyping, exploring design variations, validating data structures, accelerating iteration, maintaining documentation coherence.
But increased capability requires increased accountability. As AI takes on more execution, people take on more responsibility for cross-team communication, stakeholder alignment, system integrity, and decision clarity.
The work shifts — it does not disappear. What changes is where the highest-value human attention goes. We design our process around that shift, not around the fiction that AI makes oversight unnecessary.
AI executes
Rapid prototyping
Design variation exploration
Data structure validation
Doc coherence checks
Delta classification
Iteration acceleration
Humans govern
Scope agreement at every phase gate
Sign-off on all canon changes
Contradiction resolution
Stakeholder alignment
System integrity calls
Decision clarity
Emergent Tooling
Puddlejump
Human-AI documentation governance
Puddlejump is our structured workflow for maintaining fidelity between documentation and code as a system evolves. It runs as a 10-phase human-AI collaboration: inventory, classify, repair, diff against code, security review, quality review, fix, pre-test, help docs, certify.
Every phase gate requires human agreement before anything changes. Canon documents are versioned. Changes cascade — when a data model spec changes, every doc built from it is reviewed. The audit trail is append-only. Sign-off is explicit, never assumed from silence.
Puddlejump is currently emergent tooling — a process refined through daily use on a live production codebase, in active co-discovery with AI. It is being productized as a server tool built on our Inkwell LLM service. The middle state is intentional: the process is the discovery. Every pattern we name, every step we formalize, is a step toward the product.
This is AI-Collab in practice — not a tool we bought, but a capability we are building by using it.
The result
A system that is grounded, sound, and ready to evolve.
Grounded in real use
Structurally sound
Contract-driven
Clearly documented
Accountable at every layer
Ready to evolve
Want to bring this discipline to your team?
We work with organizations to introduce AI-Collab workflows — not as a technology install, but as a genuine shift in how a team builds.