The Organizational Transition

My team helps companies like yours transition to the new AI reality. Agentic loops, tactical agentic teams, ops, development, support, and more.

We design and deploy orchestrated agentic systems for engineering teams. These systems handle product design, test writing, code generation, code review, documentation, and deployment. They can also handle business concerns, such as marketing, sales, internal operations, and more.

AI is evolving rapidly, as is the tooling that goes with it. We are far from chatbot territory, as these AI tools improve themselves over time. This is a hocky stick, not a simple curve. What was only dreamt of a few months ago is rapidly becoming reality, with the first single-person, $1B company on the horizon.

We've been testing and speed-running these systems on our own projects for over a year. The process is mature, battle-tested, and repeatable. It's also evolving and changing, but patterns are now emerging that are withstanding the churn. In short: it's time to make the move.

This is where we can help you.

We come in, build the system around your actual codebase and team structure, train your engineers to run it, document everything, and leave. What stays behind is yours.

The companies we work with aren't struggling with AI adoption because the technology is hard. They're struggling because nobody has shown them how to integrate it into their actual workflow, with their actual codebase, on their actual timeline.

The ROI

The value here isn't just speed. It's what your team gets to focus on when the tedious stuff is handled. For now, let's focus on software development.

Development velocity increases dramatically. Tasks that took days start taking hours. Your team ships more, with better test coverage and fewer regressions. The productivity gains are measurable within the first sprint.

Your senior people do senior work. Instead of writing boilerplate and chasing down bugs, your best engineers focus on architecture, product decisions, and the problems that actually require a human brain.

Token costs are trivial. Running these loops costs a fraction of what you'd spend on a single additional hire. The math is not close.

You become a place senior engineers want to work. The best people in the industry are gravitating toward teams that operate this way. Being early matters more than most executives realize right now.

Our engagement pays for itself within weeks. Most organizations see the investment returned within the first quarter, purely from velocity gains. That's before accounting for the compounding effect as your team builds fluency over time.

How We Work

01: Audit
We start by understanding your project, team structure, and workflows. We talk to your engineers, read your repos, and find where the friction lives. We're looking for the spots where tactical agentic programming will have the highest immediate impact, and the spots where humans should stay in the loop.

02: Architecture
We design your agentic fleet for your specific situation: which parts of your workflow benefit from agents, how they orchestrate with each other, what the human checkpoints are, and how everything ties into your existing CI/CD and tooling. You see the blueprint and sign off before anything gets built.

03: Build
We set up the loops, configure the agents, write the orchestration, and run them against your actual codebase. Real project, real code, real problems. Not a toy environment.

04: Transfer
Your team learns to run, debug, and evolve the system. We document everything. The goal is that you don't need us when we're done. We show your team how things are done in this new day, their productivity explodes, everyone is happy.

Engagement Programs

These are the programs that have worked for our clients in the past, but we're happy to create a custom engagement as needed.

The One-Week Intensive

Designed for teams that want to move fast and already have a clear target in mind, with some AI experience.

Codebase and workflow audit (days 1-2)
Agent architecture design and sign-off
Build and deploy a focused agent fleet (2-4 agents) against a specific, high-value part of your workflow
Team walkthrough and handoff documentation
Two weeks of async support post-engagement

Best for teams with an immediate bottleneck to eliminate: code review loops, documentation generation, test coverage, sales engineering.

The Two-Week Build

A fuller engagement that covers more of your workflow and leaves your team with a more complete system.

Full team and codebase audit
Agent fleet architecture (4-6 agents) with orchestration design
Build and deploy across multiple workflow stages
Training sessions for the engineering team (virtual or on-site)
CI/CD integration where applicable
30-day async support

Best for engineering teams of 5-20 who want a solid foundation they can extend over time. You leave with something your team actually owns and understands.

The One-Month Transformation

For larger organizations with multiple teams, compliance requirements, or a longer internal adoption process to manage.

Multi-team audit and architecture planning
Custom agent fleet per team, built and deployed iteratively
Security and compliance review integrated into the architecture
On-site workshops
Executive briefings on outcomes, ROI, and next steps
Leadership strategy sessions on long-term AI infrastructure
60-day embedded support after the engagement wraps
Quarterly check-ins through the first year

Best for organizations making a deliberate, company-wide move to AI-assisted development. This isn't a pilot. It's a transition.

Costs and Fees

We do per-project pricing based on the number of people we need to interface with, and the type of work we'll be doing. Software development is our specialty, but we can also help with AI transition with the more established business centers, such as sales and marketing.

Background

Our team brings over 45 years of combined experience shipping software, managing engineering teams, and building systems at scale. We've worked with organizations from early-stage startups to large enterprises, and we understand the difference between what works in a demo and what works in a production environment with real constraints, real codebases, and real teams.

Reach out if you have any questions.