How we work: Eight Labs. Three Engines. Decision Visibility throughout.
Pūtake Labs is a 50/50 Māori-Pākehā partnership applying structured decision analysis, AI-supported simulation and human judgement to help organisations test decisions before commitment. Every engagement is principal-led, evidence-driven and designed around the specific decision pressure. Decision Visibility runs through every engagement.
A disciplined sequence, regardless of which Labs are involved.
Every Pūtake Labs engagement follows the same disciplined sequence. The Labs used vary based on where the decision pressure sits. The Context Engine, Risk Trajectory Engine and Direction Engine run across the work. Decision Visibility surfaces power, people and cultural conditions throughout.
Understand the decision, pressures, constraints, stakeholders and what would actually help. Both principals. The first conversation has no cost and no commitment.
Select Labs based on where the decision pressure actually sits. Define deliverables, timeline, evidence needs, risk focus and investment. Tasks divided by best fit between both principals.
Establish what is happening in the organisation and environment. Replace assumption with verified operational, civic and stakeholder truth. The Context Engine governs evidence integrity. Decision Visibility surfaces power dynamics, cultural conditions and whose voice is absent.
Run the selected Labs. The method surfaces evidence quality, power dynamics, stakeholder conditions, cultural context, risk trajectory and implementation reality alongside AI-supported analysis and simulation.
Return with structured options, explicit trade-offs, risks, evidence gaps and decision conditions. The Direction Engine governs recommendation quality. Integrated findings from both practice systems in a single evidence-led output.
Where required, implementation support covers roadmaps, process design, governance, change management and capability building. Changeable can support practical delivery.
Three engines govern what goes in, what changes and what comes out.
The Context Engine, Risk Trajectory Engine and Direction Engine are shared methodology infrastructure. They ensure the integrity of evidence entering the Lab system, visibility of risk as conditions change, and the quality of recommendations leaving it.
Governs evidence gathering, data integrity, source verification and context establishment before any Lab analysis begins. Ensures the Labs are working from verified fact, not inherited assumption. Separates what is known from what is inferred, missing or contested.
Tracks how risks evolve from pre-decision conditions through implementation and into long-term operation. It maps how risk transfers, compounds, drifts and resolves across the full lifecycle of a decision.
Governs the quality, traceability and defensibility of recommendations leaving the Lab system. Ensures every recommendation is connected to evidence, assumptions are named, trade-offs are explicit and decision conditions are clear.
Four ways to engage.
Every engagement begins with both principals. Once scope is agreed, tasks are divided based on best fit and value, drawing on each principal’s distinct expertise to deliver the strongest outcome.
First conversation
Understand the decision, the pressure, the timeline and what is already known. Both principals. That conversation tells everyone whether the fit is right.
Commitment No cost. No obligation. Confidential.Scoped engagement
One decision examined in depth, or several when structurally connected. Labs selected based on where the decision pressure actually sits. Decision Visibility integrated throughout.
Delivery Clear deliverables, defined timeline, agreed investment. Tasks divided by best fit between both principals.Complete decision assurance
Full engagement across multiple Labs for decisions with significant consequence. Evidence, simulation, challenge, cultural analysis, risk trajectory and implementation assurance delivered as one integrated programme.
Delivery Integrated findings. Defensible recommendations. Implementation support available.Implementation support
Once recommendations are accepted, practical support to implement them. Roadmaps, process design, governance, change management and capability building are scoped to what was agreed.
Delivery Advisory work stays grounded in evidence. No ongoing dependency created.Principles that govern every engagement.
These are not aspirations. They shape how the work is designed, delivered and explained.
Humans decide. AI helps us see further, faster.
Pūtake Labs uses AI-supported analysis and simulation behind the work. Clients engage a decision intelligence practice, not a technology product. AI is a capability we apply, not a product we sell.
AI helps compare evidence, surface patterns, test assumptions and model consequence at a pace not possible through manual review alone.
Human judgement defines the decision question, understands consequences, interprets results and decides what to do with the insight.
The work is governed by the Pūtake Labs Responsible AI Policy and Privacy Policy. For individual AI capability building, see Zero to AI.
How We Work FAQs
Common questions about the Pūtake Labs method.
How do you decide which Labs to use?
The first conversation establishes the decision, pressure, constraints and uncertainty. Labs are selected based on where the decision pressure actually sits, not from a fixed menu. Both principals are involved in scoping.
What is Decision Visibility?
Decision Visibility is the kaupapa Māori, Indigenous and sociological lens that runs through every Pūtake Labs engagement. It surfaces where power sits, who is affected, what cultural conditions shape the outcome, and where AI or organisational systems may shift accountability away from people.
What are the Context Engine, Risk Trajectory Engine and Direction Engine?
They are methodology engines, not standalone Labs. The Context Engine governs evidence integrity coming into the Lab system. The Risk Trajectory Engine tracks how risk evolves, transfers and compounds. The Direction Engine governs the quality and traceability of recommendations going out.
Who does the work?
Pūtake Labs is a 50/50 Māori-Pākehā partnership. Both principals are involved in every initial engagement. Once scope is agreed, tasks are divided based on where each principal’s expertise delivers the most value. There are no juniors, no subcontractors and no account managers.
How does kaupapa methodology integrate?
Where Māori interests, Māori data, mātauranga Māori or Te Tiriti obligations are in scope, the Kaupapa Methodology Module is activated. This includes attention to Māori data principles such as those outlined by Te Mana Raraunga.
Can Pūtake Labs help with implementation?
Yes. Once recommendations are accepted, implementation support can be scoped separately. Changeable can also support practical AI, process and automation delivery.
How is AI used responsibly?
AI supports analysis and simulation. It does not own accountability or make final decisions. The Pūtake Labs Responsible AI Policy governs all use. Human judgement remains responsible throughout.
Ready to test a decision before it becomes commitment?
Start with the decision, not the service. The first conversation costs nothing, commits nothing, and helps clarify whether the fit is right. Both principals.