Services
AI-Assisted Development
Hands-on software engineering where people and AI tools work the same backlog—architecture, implementation, review, and delivery—with senior judgment on what ships.
Applied Development
Use AI on Real Work—Not as a Demo.
Engagements are ordinary engineering with better tools: build features, run parallel tracks when it helps, modernize legacy systems, and keep a senior engineer accountable for design and sign-off.
Pair-Programming with AI
Architecture, code, and review in the same loop.
Work side-by-side with AI the way a senior engineer pairs with a colleague. Agents draft and iterate; the operator steers design, catches edge cases, and owns what ships. The outcome is working software—APIs, services, UIs, and data layers—not demos that die in a slide deck.
Best for: product and platform teams that want higher throughput without giving up ownership of the codebase.
Typical deliverables: feature slices, shared libraries, refactors with tests, and clear handoff docs for the team that maintains the work.
Parallel AI-Assisted Workstreams
One lead. Specialized tracks that stay aligned.
Split a complex effort across roles—implementation, tests, docs, migrations—while a human lead defines handoffs, acceptance criteria, and guardrails. Tools move in parallel; the lead keeps the work aligned with the architecture and the deadline.
Best for: migrations, multi-module releases, and backlogs where sequential solo work is the bottleneck.
Typical deliverables: coordinated pull requests, status across workstreams, and a single accountable technical lead for merge readiness.
Remediation with AI Collaboration
Pay down debt faster without rewriting everything at once.
Use AI-assisted analysis and surgical changes to stabilize legacy systems: dependency upgrades, security fixes, performance hotspots, and platform moves (.NET, Java, Linux, cloud). The human sets priority and risk; agents accelerate the repetitive excavation and drafting so the team can ship durable fixes.
Best for: brownfield apps that still run the business and cannot wait for a greenfield rewrite.
Typical deliverables: prioritized remediation plans, patched modules with tests, and phased modernization paths.
Quality Gates for AI-Assisted Work
Speed is useless if the merge is wrong.
Treat AI output like any junior draft: review for correctness, security, maintainability, and fit with domain rules. Establish practical gates—diff review, tests, dependency hygiene—so assisted changes stay trustworthy under load and over time.
Best for: teams already using AI tools who need a senior eye on what is landing in main.
Typical deliverables: review standards, risk notes on critical paths, and hardened merge practices for AI-assisted PRs.
Ship More Without Cutting Corners
More finished work. Same engineering standard.
Parallel workstreams and tight feedback cycles turn agent capacity into shipped software: features, fixes, and migrations that pass review and survive production. The measure is completed outcomes—not tokens generated or tools installed.
Best for: organizations that need velocity and still answer to audit, uptime, and customer trust.
Typical deliverables: release-ready increments, reduced backlog age, and a repeatable collaboration rhythm for the in-house team.
How Work Is Scoped
| Focus | Typical deliverable | Example scenario |
|---|---|---|
| Collaborate | Paired feature delivery | Ship a new service or module with AI as the pair |
| Orchestrate | Multi-track release | Run implementation, tests, and docs in parallel under one lead |
| Modernize | Remediation plan + fixes | Upgrade a legacy platform without a big-bang rewrite |
| Review | Quality standards + audits | Raise the bar on AI-assisted pull requests before merge |
| Deliver | Outcome-based sprint support | Clear a critical backlog while keeping production stable |