When it is relevant
A product or use case has been prioritised and you need stronger evidence of user value, technical viability and business value before committing to scale. Or you already have a prototype or working concept and need to determine what it will take to make it suitable for real users and real operating conditions.
When this is not the right engagement
This is not a rebuild of work produced elsewhere. If the direction is already proven and the question is how to get it into production, Product Build & Scale is the closer fit. If the priority itself is not yet settled, start with AI Strategy & Readiness. If the system is already live and the question is how well it performs, that is Evaluation & Assurance.
Experience, intelligence and control
- Experience
- Test the workflow with the people who will use, supervise or operate it.
- Intelligence
- Prove the important model, agent or system behaviour using representative conditions and data.
- Control
- Establish how quality will be evaluated and where human review, failure handling or escalation is required.
What the client receives
- Working product or workflow slice
- User or operator evidence
- Technical feasibility and architecture decisions
- Representative evaluation baseline
- Known failure conditions and constraints
- Value case and recommendation for the next stage
How this engagement continues
Shape: Fixed or milestone-staged engagement
Duration: Typically 1.5-2 months
The next stage proceeds only where the evidence supports it. That may mean further iteration, Product Build & Scale, an internal client build, or stopping. Stopping is a valid outcome where the evidence does not support additional investment.
Tooling included in this engagement
Capability embedded in the work, not software sold separately. How each fits the method is on the Approach page.
- Audra Eval - CI/CD quality gates for agentic AI.
- Audra Vibe - The delivery operating system behind the build work.
Relevant work
Examples of the product, technical and evaluation decisions involved. Each case describes its own scope and delivery context.
Transforming loan acceptance predictions using artificial intelligence
From credit data to an explainable acceptance model wrapped in a usable decision experience.
Proof of concept · synthetic-data evaluation
Read the case →KYC/Onboarding re-imagined with AI agents
Agentic pipeline for KYC and onboarding - documents, checks, and explainable handoff.
KYC workflow design and engineering
Read the case →Prepare for the first conversation
Bring the current workflow, the people involved and the decision you need to make. We agree scope, client inputs, fees and acceptance criteria before work starts.
For banks, wealth managers and financial platforms, explore financial-services work and engagement options.
Discuss the work in front of you
Tell us about the initiative, product or operating question you are working on. We will route the conversation to the appropriate senior lead.