Engineering

Full-stack product engineering.

Product engineering is building production software from day one, not prototypes rebranded as MVPs. The engineers who design the system also ship it, full-stack across React, React Native, Flutter, Node, Python, and cloud, with AI in the architecture from the first commit.

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The problem we solve

Most enterprise releases die in the handoff. Design throws a spec over the wall, engineering rebuilds half of it, and the prototype that demoed well never survives contact with real load, real data, and real users.

We close that gap by keeping design and engineering on the same team. We build for uptime and scale, wire telemetry in from day one, and validate every release with AURA, our release confidence platform.

Most of that work happens embedded. Our engineers join your team, in your repo, your standups, your Slack. Not a separate squad working a ticket queue somewhere else.

What this includes

Front-end development

Fast, accessible, responsive interfaces in React, React Native, and Flutter. AI-assisted review catches regressions before QA does.

Full-stack engineering

APIs, backend systems, databases, and cloud infrastructure built for uptime and scale. Telemetry and anomaly detection from day one.

Prototype to production

Take a Cursor, Lovable, Replit, v0, or Bolt prototype and harden it to production: test coverage, error handling, security, deploy pipelines, telemetry.

Mobile applications

Native and cross-platform apps that feel like consumer software and hold up under enterprise constraints.

Release confidence

Every release validated with AURA. Intent-driven testing across API, UI, and data layers. Not brittle scripts, not manual checklists.

Legacy modernization

When the codebase came first.

Your codebase works, but it was built before AI-native tooling existed. Coding assistants cannot reason about it, tests are thin or missing, and every new feature takes longer than the last. Technical debt has quietly become AI debt.

We bring it under control without a rewrite: test coverage first, then a smaller dependency surface, then modular boundaries and AI tooling wired in. Your team keeps working in the repo they already have.

Audit, 7 days

Fixed scope and fixed price. We find the real blockers across coverage, dependencies, architecture, and AI-tooling readiness, and write a prioritized plan. No obligation to continue.

Sprint, 2 to 4 weeks

We write the test suite, reduce the dependency surface, and set up AI-native workflows. You get merged pull requests and a written modernization report, not a slide deck.

Embedded, one quarter

Everything in the sprint, plus we pair with your engineers daily on real code and measure the velocity change against a baseline. This is where the capability transfer happens.

Proof

We took Gritwell from zero to a production MVP in four months across web, mobile, and PWA. The platform went on to raise 3 million dollars after launch.

IIDM came to us with a production codebase that worked but could not keep up with AI-native development. We generated meaningful test coverage in a day, reduced dependencies by 40%, and restructured the code so their team could use AI coding tools effectively. The engagement paid for itself within one quarter.

Questions

Common questions about engineering.

Product engineering is building and shipping production software, not just writing code to a spec. The same team designs the architecture, builds full-stack across web and mobile, wires up testing and deploy pipelines, and owns the release. AI is part of it from the first commit: code generation, automated testing, and intelligent backend orchestration.
Yes, this is one of our most requested engagements. We take a prototype from Cursor, Lovable, Replit, v0, or Bolt and harden it: test coverage, error handling, security, performance under real load, deploy pipelines, and telemetry. Sometimes as a fixed-scope sprint, sometimes as an embedded team alongside the founding team.
Full-stack across React, React Native, Flutter, Node, Python, and cloud-native infrastructure. We choose the stack for the product and the team that will maintain it, not for novelty. Telemetry and anomaly detection are wired in from day one.
Project builds typically run 8 to 20 weeks end to end, with the first week spent scoping so the timeline is set before the build begins. Embedded teams are longer partnerships. Every release is validated with AURA, our release confidence platform.
In most cases, we generate meaningful unit test coverage within the first day using AI-assisted test generation and our own tooling. The coverage is not cosmetic. Tests are scoped to actual business logic and validated against real runtime behavior. From there, we layer in integration and edge-case coverage across the sprint.
No. Modernization is not a rewrite. It works with your existing codebase: we improve structure, reduce dependencies, add coverage, and make the code ready for AI-assisted development without throwing away what already works. We work inside your repo, your CI pipeline, and your deployment process rather than asking you to move to ours.
Modernization handles the codebase. AURA, our release confidence platform, adds the validation and automation layer on top. The pairing means a legacy product can move from no test automation to a working automation suite in days rather than months. AURA reads the product, generates intent-driven validation across API, UI, backend state, and data integrity, and runs in your existing CI. You get coverage without the script-by-script test build that traditional automation requires.
Product engineering

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Part of our services. See also product design, AI product development, and advisory.