A working AI prototype being developed and evaluated

Flagship engagement

AI Prototype Sprint

Turn possibility into evidence.

Build a working AI prototype that answers the questions worth answering.

A focused four- to eight-week engagement that designs, builds and demonstrates a working AI prototype.

Test the opportunity in a realistic context, generate meaningful evidence and leave with the confidence to proceed, refine or walk away.

Duration

4–8 weeks

Investment

A$30,000–A$60,000 + GST

Primary outcome

Prototype-driven evidence

Decision supported

Should we build this for real?

What we do

Most organisations do not need production AI as their next step.

They need enough evidence to know whether production AI should exist at all. The AI Prototype Sprint is designed to generate that evidence.

Once an opportunity has been identified, the next challenge is rarely building a complete system. It is understanding whether the opportunity genuinely works when confronted with real workflows, real data and real users.

Over four to eight weeks, Flink Labs designs, builds and evaluates a working prototype focused on learning and reducing uncertainty. Depending on the opportunity, the prototype may become an AI-native product, an intelligent internal tool, a decision-support system, a retrieval and knowledge environment, an agentic workflow, an interactive simulation or another purpose-built intelligent system.

The sprint begins by identifying the assumptions on which the opportunity most depends. Where an AI Opportunity Review has preceded it, those findings provide a head start. Throughout the engagement we test technical feasibility, user interaction, workflow integration and the quality of the resulting outputs to determine where the opportunity succeeds, where it struggles and what should happen next.

This is not a production software project. It is a research-led prototype designed to generate evidence, reduce uncertainty and support confident investment decisions before committing to implementation.

What you leave with

A working prototype and a clear understanding of what it demonstrates.

You will understand:

what works,
what still requires refinement,
where the technical limits lie,
how users interact with the concept,
what implementation would realistically require,
whether the opportunity deserves further investment.

Most importantly, assumptions have been replaced by evidence.

Although the prototype is built for learning rather than deployment, its architecture, technical discoveries and interaction patterns provide a strong foundation for future implementation.

Every AI Prototype Sprint is principal-led, from technical architecture through design, implementation and evaluation.

The work combines modern AI engineering with product thinking and applied research. Every technical and product decision is guided by a single question: "What evidence do we need to make the next decision with confidence?"

Rather than assembling the largest possible feature set, the objective is to learn as much as possible with the least unnecessary complexity.

This engagement is the best choice when:

You have identified a promising AI opportunity and want to prove it in practice.
You need evidence before committing to a larger implementation.
A concept appears technically feasible but important questions remain unanswered.
You want to test workflows, user experience or model behaviour using realistic data.
Internal stakeholders need something tangible before approving further investment.
The cost of building the wrong system is significantly higher than the cost of learning first.

Prototype sprint outputs

Every AI Prototype Sprint is shaped around the opportunity being explored.

Every sprint is customised to the engagement and typically includes:

Prototype Design Review

A clear definition of the prototype's objectives, scope, learning goals and success criteria.

Working Prototype

A functional prototype demonstrating the core capability in a realistic operating context.

Technical Review

An assessment of the architecture, models, integrations, data considerations and implementation implications.

Evidence Review

An evaluation of the prototype against its objectives, documenting what was learned, what remains uncertain and where further investigation would create the greatest value.

Decision Review

A final demonstration and discussion summarising the evidence, trade-offs, recommended next steps and whether the opportunity should progress toward implementation.

A decision-grade AI prototype made tangible for evaluation

AI Prototype Sprint

Turn an AI opportunity into something real.

Answer the important questions through a carefully designed prototype, generating the evidence needed to make confident implementation decisions.

Outcome

Prototype-driven evidence

Duration

4–8 weeks

Investment

A$30,000–A$60,000 + GST

Decision

Should we build this for real?

If the prototype demonstrates a compelling opportunity, the work can become the foundation for implementation by your internal team, an external delivery partner or an ongoing AI Lab Partnership with Flink Labs.

Discuss an AI Prototype Sprint