Insights · Solution Design
From a product idea to a costed solution proposal.
An established advisory business wanted to turn a consulting offer into a digital product. Elevion AI ran the discovery and solution design that supported preparation of a product funding application.
Clarified
Product scope
Users, purpose and boundaries agreed
Assessed
Technology options
Existing platform and alternatives
Costed
Phased proposal
Deliverables, timeframes and scope
Prepared
Application material
Approach, innovation and readiness
The client
Proven expertise, delivered one client at a time.
The client is an established advisory business. Its core offer combines structured assessments with practical advice. Delivering that work involves reviewing documents, gathering input and preparing reports with recommendations.
The challenge
A good idea needed a defined scope and credible costings.
Every assessment was delivered manually. Growth depended on the owner's available time, and a digital offer was being considered as a way to reach more clients.
- Valuable expertise tied to the owner's time
- Limited capacity to grow the offer
- An idea for a digital version without a defined build
- A funding deadline that called for clear scope and costs
The owner knew what the product should achieve. What was missing was a written plan for how to build it, in what order and at what cost.
The process
Four stages from idea to proposal.
Stage 1
Understand the business model and intended users
We worked through how the advisory offer is delivered today, who it serves and what a digital version would need to do for them.
Stage 2
Document the requirements
The assessment, the scoring logic and the customer experience were worked through with the owner, giving the proposed product a defined scope.
Stage 3
Assess platform capabilities and alternatives
We assessed the existing membership platform alongside assessment tools and alternative form approaches, with the requirements guiding the recommendation.
Stage 4
Develop a staged, costed proposal
The recommended approach was set out with clear deliverables, timeframes, phased scope and costings for each stage.
Have one product idea or workflow to work through? A Targeted Discovery provides a way to agree requirements, assess options and receive a Solution Proposal before committing to implementation.
The proposal
Two delivery phases, with AI held back for later.
The design separated the assessment and reporting engine from membership, onboarding and paid support. A rule-based foundation was recommended first, with the commercial experience scoped as the next delivery phase.
Assessment and reporting foundation
A structured assessment with rule-based scoring and consistent reporting, so results are predictable and easy to check.
Membership and commercial experience
Access, customer flow and the paid offer built around the foundation, using the existing membership platform where it fits.
Possible AI layer
AI-generated insights and a chatbot were considered for later, once the foundation is proven. They were not part of the two delivery phases.
These were design recommendations. The proposal describes what would be built, and nothing in it has been implemented as part of this work.
Grant support
Material the owner could use in the application.
Elevion AI connected the business with a marketing specialist, who contributed a separate commercial plan alongside the technical proposal. Together, these materials described how the product could be developed and brought to market. This case study records proposal delivery, not a funding decision.
Outcomes
A defined project, ready for the next decision.
The completed work was planning. The business finished with a clear product scope, the technology options worked through and a phased, costed proposal. Any gains in capacity or revenue depend on future implementation and have not been measured.
Requirements before technology
Agreeing the assessment and scoring logic first meant every option was judged against the same written needs.
Existing platform considered first
The recommendation started with the membership platform already in use, with other tools assessed against the product requirements.
Rule-based before AI
A reliable rule-based foundation was recommended ahead of AI, so any future AI features would sit on tested logic.
A scope a funder can read
Phased deliverables, timeframes and costings gave the application a defined project rather than an idea.