For leaders who know software should move faster now, but need useful work shipped inside their own environment. Shore AI takes one real feature, workflow, integration, or technical improvement through implementation, tests, review, documentation, and handoff so the team sees the new delivery loop on real work.
This is hands-on build work. Thomas does the coding directly. If the first build creates value, the engagement can keep moving through the next useful feature, integration, internal tool, or cleanup item.
Open the one-page briefOne real backlog item, compressed through the same gates your team already trusts.
Hours to days
Pick the backlog item, success criteria, constraints, and review owner.
Agentic coding with repo-aware exploration, patches, and existing patterns.
Tests, QA notes, edge cases, and explicit tradeoffs.
Team review, requested changes, and quality gates.
Ship the work, hand off context, and keep going if the loop earns more trust.
The pilot is built for situations where the speed gap is visible and the organization needs shipped work before changing team habits, vendor expectations, or delivery process.
You know AI should compress engineering work, but the team is still using it shallowly, inconsistently, or cautiously. The first build gets real work done while giving them a serious repo-level example of what good looks like.
You are paying serious money for software delivery, but the cadence has not changed. One focused AI-native build gives you a modern benchmark for what should ship in days with quality gates intact.
Teams that need real engineering capacity now and want the side effect of a concrete example that changes how people think about delivery.
You have priority work and limited engineering capacity. You need useful software shipped, not another tool debate.
You know delivery should move in hours and days now. You need the work done and the team to see what changed.
You are evaluating AI-native practices. You need a controlled way to add delivery capacity and validate the method on real production work.
You want higher leverage without lowering review standards, maintainability, or trust.
Working code and concrete artifacts from one real workstream, not a strategy deck or training session.
A real backlog item implemented directly and left merged, staged, or merge-ready against agreed acceptance criteria.
Coverage where it matters plus explicit manual verification notes.
Help getting the change through your normal release path.
Usage notes, implementation decisions, and follow-up context.
The AI-assisted engineering loop your team can inspect, question, and reuse because it was attached to shipped work.
AI engineering pilot
A meaningful production workstream, implemented through a days-not-weeks loop with visible checkpoints, reusable team context, and a clear decision on whether to keep going.
First hours
Choose the first build, define acceptance criteria, map the repo, and agree on review gates.
Same day
Implement the highest-value path with agentic repo search, patches, tests, and reviewer context.
Day 1-2
Run checks, harden edge cases, document tradeoffs, and review the PR in your environment.
Days 2-4
Merge, stage, or demo the work, hand off context, and decide whether the next workstream is worth continuing into.
Discipline, transparency, and your data stay front and center.
Work happens in your repos and environments. You retain all IP.
We use scoped access with audit logs and remove access when we are done.
You see progress, decisions, and changes throughout.
We follow your standards, tests, and review process.
We leave you with code, docs, and context you can run with.
Let's choose the first workstream, move it through your quality gates, and keep going only if the work is worth it.
