Solution · Transformation
AI-Native Transformation
Get your whole SDLC from A to AI.
The full-stack, AI-native rebuild of how you deliver software, run with your people and at your scale. It is a major undertaking with major deliverables, and the operating model stays with your team at the end.
At a glance
- Practice
- Transformation
- Engagement
- Turnkey transformation program
- Commitment
- Multi-quarter
- Best suited for
- An org ready to change how it ships, not just which tools it bought.
Is this you?
We don't want another pilot — we want AI to change how the whole org ships.
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
- Multiple teams already past the experimentation phase
- Leadership aligned that the operating model is the constraint
- A mandate — often board- or PE-backed — with a real timeline
The engagement
What we do.
AI-Native Transformation is the turnkey version of changing how an organization ships software. We start by building a real understanding of your current SDLC and value streams, then we do empathy mapping with your teams to identify and co-create the new roles that will exist once the transformation lands.
From there we adapt enablement to your language, your needs, and your team's actual skills, and we build a to-be SDLC alongside a roadmap that gets you from where you are to AI-native delivery. It is full-stack AI SDLC, done at scale, with you rather than to you.
This is the heavy engagement on the menu, and we are honest about that. The payoff is that the capability stays: your teams helped build the new way of working, so they keep running it after we leave.
The shift
We rebuild how you ship software, with your people and at your scale. The operating model stays with your team.
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
Value-stream and co-creation workshops
Facilitated sessions that map your SDLC, surface the real constraints, and co-create the to-be process with the people who will run it.
- Nº 02
AI Community of Practice
We launch the forum where patterns, prompts, evals, and guardrails are shared across teams instead of reinvented in each one.
- Nº 03
AI Platform Team(s)
Standing teams that own the agent-ready environments, shared substrate, and tooling the rest of the org builds on.
- Nº 04
Governance and guardrails
Agentic permissions, traceability, and accountability designed in from day one, so security and compliance can sign off.
- Nº 05
Team transformation and role co-creation
Empathy mapping and co-creation that define the new roles people will grow into, so adoption is something teams choose.
- Nº 06
Adapted enablement and training
Training targeted to your language, your stack, and your team's current skills, not a generic curriculum.
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
Understand
Map the current-state SDLC and value streams, and find where effort, rework, and waiting actually go today.
- Nº 02
Co-create
Empathy mapping with your teams to design the to-be SDLC and the roles people will hold inside it.
- Nº 03
Build
Stand up the CoP, platform teams, governance, and enablement, and prove the new way of working on real work.
- Nº 04
Scale and hand off
Adapt the backlog, scale the model across teams, and hand the operating rhythm to your org to run on its own.
Proof
From a real engagement.
- Nº 01
Consumer Products
Global 500 consumer products company
Agentic AI for product operations — work enters the SDLC clean
- Challenge
- Operational work, requests, and technology investments were scattered across systems, so leaders had limited visibility into where effort went. Thousands of duplicate, stale, and orphaned backlog items made planning reactive, and inconsistent intake created constant administrative friction.
- Approach
- Stood up an AI Community of Practice to pinpoint where product leaders were losing time, then built targeted agents and the operating process around them — intake, routing, prioritization, and estimation — integrated securely into Azure DevOps with a permission-and-guardrail system.
- Outcome
- Intake, routing, prioritization, and estimation now run as governed agents. Backlog hygiene is continuous instead of reactive, product leaders spend less time on administration, and planning is context-aware. AI changed how work enters the SDLC.
Related solutions
Often paired with.
- Transformation
Every team is reinventing AI on its own, and none of it reaches production.
AI Platform Enablement
One shared AI-native foundation, built with your teams.
~4 monthsHow 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.
Perspective·7 minAI displacement is real. Stop hiding behind the slogan.
“AI won't replace you. Someone using AI will.” The line sounds pragmatic. It's a dodge. If your AI strategy doesn't include the people affected by it, it isn't a strategy. It's cost-cutting with better branding.
Chris Sims
Perspective·9 minWhat is your chatbot doing to you?
AI has become the most patient, affirming, available presence in a lot of people's lives. I use it constantly, and the research on what it's doing to us is arriving faster than the honest conversation about it.
Chris Sims
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
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
Scope the engagement
Put real edges around AI-Native Transformation.
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.