Sigao

Service Nº 02 · Align & Enable

Change how your team ships.

We work alongside your leadership and engineering teams to rebuild the operating model, modernize the platform, and raise AI capability, then hand the whole thing over. Your teams run it without us.

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Built for

Mid-size SaaS engineering orgs that handed out AI tools, then watched the operating model, platform, and ways of working fall behind them.

What we’re hired to change

Four areas we transform.

Most engagements pull on more than one of these four areas. The diagnostic tells you which to start with and which can wait. Each maps to what the market is now buying: AI-native development platforms, spec-driven development, context engineering, eval-driven development, and a workflow no longer centered on the IDE.

  • Use case 01

    Operating model rollout

    We redesign how teams work day to day: lanes, ceremonies, decision rights, and workflows built around intent, spec, and validation. Governance lives in the platform and pipeline, not the IDE. Your teams end up with a system they run day to day.

  • Use case 02

    Platform team & AI-native platforms

    We stand up the platform team that runs your AI-native development platforms: CI/CD, deployment safety, internal tooling, AI infrastructure, and the security guardrails that keep them safe to use. Everything your engineers build on sits on top of this.

  • Use case 03

    AI capability uplift

    We stand up the AI Guild and the practices that make AI compound: spec-driven development, context engineering, and eval-driven quality gates. Your engineers learn to orchestrate coding agents, and what one team figures out spreads to the rest instead of staying locked in one corner.

  • Use case 04

    Leadership alignment & coaching

    We get your leaders agreeing on direction, priorities, and how decisions get made. When that's settled, the teams below know what to act on, and the change holds.

How a transformation runs

We diagnose, design, prove, then scale.

No big-bang reorg. We prove the change inside one real value stream first, then scale once the evidence is in.

  1. Nº 01

    Diagnose

    A short assessment of how your delivery system actually works across product and engineering: flow, quality, decision-making, and how ready you are for AI.

  2. Nº 02

    Design

    We design the target operating model with your people: lanes, ceremonies, decision rights, platform priorities, and the AI patterns your teams will actually use.

  3. Nº 03

    Prove

    We run the new model inside one real value stream, coaches and engineers embedded. Before anything scales, the change has to show up in the numbers.

  4. Nº 04

    Scale

    Once it's proven, we spread the pattern across the org with Practice Leads, capability programs, and measurement that doesn't stop. Your teams pick it up as it goes.

How we coach

Embedded coaches, not a deck on a shelf.

Most transformations die when the consultants present a deck and go home. Ours stay in the work until your teams own the new system without us.

  • Embedded coaches, not one-off training

    Our practitioners coach where the work happens: standups, backlog refinement, architecture reviews, exec syncs. We stay until your teams own the new system.

  • Outcomes tied to delivery metrics

    We judge the change on what moves downstream: cycle time, defect-escape rate, deployment frequency, eval pass rates, customer signal. Not on what gets said in a retro.

  • Spec-driven development, audit-friendly trail

    Every operating-model decision gets written down as a living, machine-readable spec, the same way your product specs are. You can review it, trace it, and defend it months later.

  • Knowledge transfer is the deliverable

    We don't sell ongoing dependency. We're done when your team is running the new system on their own and can prove it with numbers.

Selected work

Case studies from recent transformations.

Some named, some anonymized at the client’s request. On a discovery call we’ll walk through any of them in detail, including what we got wrong the first time.

  • 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.

What clients say

We sit alongside your leaders, not above them.

“These are not common staff augmentation engineers. These are people who will consider the system architecture, come up with solutions, and present options for a path forward.”

Todd Maranda

Software Architect / Security Officer

  • This was all made possible by the steadfast commitment of the Sigao team, and for that I would like to extend my utmost appreciation.

    Mark Lonsway

    Senior VP, Professional Services

  • The MVP version of the solution is nearly complete and reflects Sigao's high-quality work. They're communicative and always bring up potential delays before they become issues.

    Melissa Jakubowitz

    President & Founder

Engagements

How we help you transform.

Three engagements that build on each other: prove the value, stand up the shared foundation, then change how the whole org ships. Start wherever your situation starts.

Not ready for a call? Start here · 4 minutes

Find out where your delivery system actually stands.

Twenty questions across five pillars of AI-native delivery. You get a radar chart, a maturity tier, and the gaps worth closing first — the same read we’d build in a first working session, without the meeting.

Take the assessment

Questions, answered

Fair questions, straight answers.

If you've watched an AI rollout produce a lot of motion and no results, you should be asking us hard questions. Here are the most common ones.

We already bought AI tools. Why isn't the lift showing up?
Because tools don't change how work moves through an organization. If intake, specs, review, governance, and the operating rhythm stay the same, an assistant produces motion, not lift — every team reinvents its own prompts and patterns in private. We rebuild that system with your teams, so the tools you already own start compounding.
Is this a strategy-deck engagement?
No. We don't hand a roadmap over the wall. Embedded coaches and engineers run the new operating model inside one real value stream first, and the change has to show up in delivery numbers — cycle time, defect-escape rate, deployment frequency — before anything scales.
What does "the capability stays" mean in practice?
Your people co-build the platform, the agents, and the practices with us, and every operating-model decision is written down as a living spec your team owns. We're done when your org runs the system without us and can prove it with numbers — not when a contract renews.
How do you keep agentic delivery safe and governable?
Governance lives in the platform and pipeline, not in an IDE plug-in: permissions and traceability for agents, quality gates with second-person review, and an audit-friendly trail for every change. Nothing ships because an agent said so.
My board wants an AI story. What do I take back to them?
A realistic, sequenced plan and evidence in the metrics boards accept — delivery numbers from a real value stream, not adoption stats. Just as important, an honest read on what AI will and won't do on your timeline, so you're never defending a promise the technology can't keep.

Insights

Current thinking on transformation.

All insights

Start a transformation

Bring us your engineering org. We’ll bring the team to change how it ships.

A conversation about where your org is now, where it needs to get to, and which engagement model fits the change in front of you.

Reporting to a board or PE sponsor on AI? You’ll walk away with a sequenced, evidence-backed plan and DORA-style metrics that hold up under questioning, plus a straight read on what AI will and won’t do on your timeline.

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