Solution · Transformation
AI Platform Enablement
One shared AI-native foundation, built with your teams.
A roughly four-month build of the shared base every team builds on: agent-ready environments, reusable SDLC agents, and the governance that keeps them safe. Built with your people, and yours to keep.
At a glance
- Practice
- Transformation
- Engagement
- Cross-team engagement
- Commitment
- ~4 months
- Best suited for
- Teams reinventing the same prompts, agents, and review patterns in isolation.
Is this you?
Every team is reinventing AI on its own, and none of it reaches production.
That sentence is the problem this engagement exists to solve — one problem, solved end to end, not a program that promises everything.
How it usually shows up
- Each team keeps private prompts, agents, and review patterns
- Pilots that impress in demos and die before production
- Security can't sign off on what it can't see or trace
The engagement
What we do.
AI Platform Enablement gives an organization one shared foundation for AI-native delivery instead of a dozen private ones. A senior-led team works with representatives from each of your dev teams to build the agent-ready environments, the reusable workflows, and the guardrails the whole org runs on.
It is a focused engagement, roughly four months, and it is built in the open with your people so they can extend and run it once we hand it over. The point is that your teams stop reinventing the same prompts and review patterns team by team.
The shift
Twelve private foundations compound nothing. One shared foundation, built with your teams, is what makes AI stick past the pilot.
What’s included
What you keep.
Named deliverables, not a statement of intent. Every one is built with your team so it stays useful after we leave.
- Nº 01
Agent-ready environments
Dev environment images built for code-oriented agentic workflows, so every team starts from the same capable base.
- Nº 02
SDLC managed agents
Reusable workflows delivered as managed agents, such as Prototype, Release Notes, Doc Writer, and Release Readiness.
- Nº 03
Governance and guardrails
Agentic permissions, traceability, and accountability baked in from the start, so security can sign off.
- Nº 04
AI Community of Practice
We launch and facilitate the standing forum where patterns, prompts, evals, and guardrails get shared across teams — then hand it to an internal owner.
- Nº 05
Shared base, left behind
Built with representatives from each dev team, so the foundation is theirs to extend and run after we go.
The team model
Your engineers join our team.
This is the mechanism behind “the capability stays” — not a handoff meeting at the end, but how the team is shaped from day one. Every engagement runs on it.
- Nº 01
Day one
A senior-led Sigao team arrives with the process and the platform. Your engineers don't get displaced — they get invited in.
- Nº 02
In flight
Your engineers join our team — not the other way around. One Engagement Lead owns scope and quality, and everything is built in the open on your stack.
- Nº 03
After handoff
Because your people co-built the work, they keep running it. The specs, patterns, and operating rhythm stay. The lift outlasts the engagement.
The approach
How we run it.
- Nº 01
Assess
Understand how each team works today and where a shared base removes the most duplicated effort.
- Nº 02
Build together
Stand up the environments, agents, and guardrails with representatives from each dev team.
- Nº 03
Prove
Run real work on the foundation so it earns its place before the whole org adopts it.
- Nº 04
Hand off
Transfer ownership — including the Community of Practice — so your teams extend and run the foundation themselves.
What changes
The shape of the outcome.
- Foundation
- One, shared across teams
- Built with
- Your dev teams
- Governance
- Baked in from day one
Related solutions
Often paired with.
- TransformationFlagship
We don't want another pilot — we want AI to change how the whole org ships.
AI-Native Transformation
Get your whole SDLC from A to AI.
Multi-quarterHow it works - Transformation
The board wants an AI ROI number, and I can't defend the spend.
AI Value Proof
Prove what AI is worth — in numbers a board accepts.
3–8 weeksHow it works - ModernizationFlagship
The codebase fights us on every change, and competitors who started later are shipping faster.
AI-Ready Modernization
Rewrite what's holding you back — without freezing the roadmap.
Scoped to the systemHow it works
Insights
Our thinking on this work.
Operating model·5 minWhy your legacy stack is the real blocker to AI leverage
You don't have an AI tooling problem. You have a systems problem. The fastest agent in the world still moves at the speed of the codebase it's editing.
McCaul Baggett
Cadence·3 minWhy spec-driven AI development beats vibe coding
AI agents are unforgiving readers of ambiguous requirements. The teams winning with AI write specs first, ship second.
Carter Musick
Practice·4 minFive lessons from running agents in production engineering
What we learned shipping agentic workflows for mid-size SaaS teams, including the mistakes we'd undo if we could.
Brandon Bosco
Operating model·3 minThe hidden cost of AI tool sprawl
Most engineering orgs have more AI tools than they think. Here's how to find them and what to do next.
Luke Colburn
Values·5 minAccountability stays human, even when the agents help
Agents accelerate the work. They don't accept consequences. Forty years of automation research explains why everything AI-influenced we ship has a named human owner.
Brandon Bosco
Values·4 minEthics, built in, not bolted on
AI harm rarely arrives through villains. It arrives through defaults: inherited data, unexamined objectives. That's why the reviews happen before rollout, not after.
Brandon Bosco
Questions, answered
Questions about AI Platform Enablement.
The questions buyers ask before scoping this engagement — answered the way we'd answer them on a call.
- Every team is doing AI its own way and nothing reaches production. How do we fix that?
- Give them one shared foundation instead of a dozen private ones. AI Platform Enablement stands up the agent-ready environments, reusable SDLC agents, and governance every team builds on — built with representatives from each of your dev teams over roughly four months, then handed off. The reinvention stops because there's finally a common base worth adopting.
- What do we actually get out of the four months?
- Four durable things: agent-ready dev environments every team starts from, SDLC managed agents like Prototype, Release Notes, Doc Writer, and Release Readiness, a governance layer with permissions and traceability security can sign off on, and a running AI Community of Practice with an internal owner. All of it is built with your people, so it's yours to extend.
- How does security sign off on coding agents touching our codebase?
- Because the guardrails are designed in from day one, not retrofitted after an incident. Every agent runs inside defined permissions, every action is traceable, and accountability for what ships stays with a named human. Security reviews a system they can see and audit — which is exactly what the team-by-team shadow-AI approach denies them.
- Why not just let our platform team build this themselves?
- Some do — eventually. What's hard to shortcut is the pattern-finding we've already done across real engagements: what makes agents reliable inside an SDLC, which guardrails actually hold, and how to get many teams to adopt a shared base instead of resenting it. We compress that into one focused engagement and leave your platform team owning the result.
Scope the engagement
Put real edges around AI Platform Enablement.
A conversation to understand the work, the constraints, and the shape this engagement should take for your team. If it’s not the right fit, we’ll say so.