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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.

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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.

  1. 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.

  2. 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.

  3. 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.

  4. Nº 04

    Governance and guardrails

    Agentic permissions, traceability, and accountability designed in from day one, so security and compliance can sign off.

  5. 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.

  6. 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.

  1. Nº 01

    Day one

    Sigao teamYour engineers

    A senior-led Sigao team arrives with the process and the platform. Your engineers don't get displaced — they get invited in.

  2. Nº 02

    In flight

    One team · one process

    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.

  3. Nº 03

    After handoff

    Your team runs it · we leave

    Because your people co-built the work, they keep running it. The specs, patterns, and operating rhythm stay. The lift outlasts the engagement.

Sigao engineers & coachesEngagement Lead — one owner for scope and qualityYour engineersYour engineers, carrying the new system

The approach

How we run it.

  1. Nº 01

    Understand

    Map the current-state SDLC and value streams, and find where effort, rework, and waiting actually go today.

  2. Nº 02

    Co-create

    Empathy mapping with your teams to design the to-be SDLC and the roles people will hold inside it.

  3. Nº 03

    Build

    Stand up the CoP, platform teams, governance, and enablement, and prove the new way of working on real work.

  4. 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.

Insights

Our thinking on this work.

All insights

Questions, answered

Questions about AI-Native Transformation.

The questions buyers ask before scoping this engagement — answered the way we'd answer them on a call.

How do we get AI to change how the whole engineering org ships, not just one pilot?
By changing the delivery system, not just the tooling. AI-Native Transformation maps your current SDLC and value streams, co-creates the to-be operating model and roles with your teams, and stands up the platform, governance, and operating rhythm that make AI compound across the org — proven on real work before it scales.
How long does an AI-native transformation take?
It's a multi-quarter program, and we're honest that it's the heaviest engagement on our menu. It runs in stages — understand, co-create, build, scale — and nothing rolls out org-wide until it has proven itself on real work inside your constraints. If you're not ready for that weight, a smaller engagement like AI Platform Enablement is the better start.
Will our teams actually adopt the new way of working, or will it die like the last rollout?
Top-down rollouts die because they're done to teams, not with them. Here your people co-create the new roles and the to-be process through empathy mapping and working sessions, so adoption is something they choose — and by the end they're running a system they helped design, not complying with one they were handed.
What do we keep when the engagement ends?
The operating model itself: the platform teams, the AI Community of Practice, the governance and guardrails, the enablement adapted to your stack, and people trained by running it. The engagement is designed to end — your org runs the system without us, and can prove it with delivery numbers.

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.

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