a buildable AI roadmap
Three teams, three stacks, and nothing that connects.
We turn AI ambition into a roadmap you can build: a use-case map scored on value and feasibility, a reference architecture for how the pieces fit, and a risk review covering data, cost, and governance — including the honest verdict on which ideas aren't worth building.
Reference architecture
Rev C · one drawing
The product your people already open
The part nobody sells you, and the part that's yours
Swappable on purpose, so no vendor can hold the roadmap
Your corpus, your indexes, your freshness rules
Already exists. Do not build a second one
Governance runs through every layer or it runs through none of them. That's why it's drawn as structure instead of added as a note.
Use cases scored, not assumed
Architecture before the build
Honest on what not to build
The brief
AI strategy that sequences ambition into a buildable plan
Break of gauge
Three answers to one question, and every join between them costs a translation.
Where two railways met at different gauges, every passenger and every ton of freight had to be physically moved between trains. The track worked. The trains worked. The join was the problem, and it was nobody in particular's fault.
The hard part of AI adoption isn't the models — it's deciding what to build, in what order, and what to leave alone. We map your candidate use cases, score them on value and feasibility, design the reference architecture that would support them, and run a risk review on data, cost, and governance, so you leave with a sequenced roadmap rather than a pile of enthusiasm.
The hard part was never the models. It's agreeing the gauge before anyone lays track.
What the drawing is for
A ranked, honest use-case map
Candidate ideas scored on value and feasibility, including the frank verdict on which ones aren't worth building — so effort goes where it pays off.
Architecture before you build
A reference architecture shows how models, data, and guardrails fit together, so the first project is built on a plan rather than improvised into a corner.
Risks surfaced early
A review of data readiness, cost, and governance names the obstacles up front, so they shape the roadmap instead of derailing a build halfway through.
What the engagement covers
Four pieces, and the drawing is the one that holds
Use-case discovery & scoring
We surface candidate AI applications across your operations and score each on business value and feasibility to rank what's worth doing.
- Opportunity mapping
- Value/feasibility scoring
- Prioritized shortlist
Reference architecture
A blueprint for how models, data flows, retrieval, and guardrails fit together, so builds share a coherent foundation.
- Architecture blueprint
- Data flow design
- Build-vs-buy calls
Risk & governance review
An assessment of data readiness, cost exposure, and the governance and oversight your AI use will need.
- Data readiness
- Cost modeling
- Governance guardrails
Adoption roadmap
A sequenced plan that orders the work into a first project and the phases after it, tied to what each depends on.
- Phased sequencing
- Dependency mapping
- First-build scope
The engagement
From a wishlist to something a team can build from
A wishlist, not a plan
AI ideas scattered across departments, no shared way to compare them, and a budget conversation stalled on opinion because nobody has evidence to bring.
A scored use-case map
Every candidate scored on value and feasibility, sorted into build now, foundation first, quick win, and not worth it.
The drawing
Models, data flow, and guardrails documented for the first project, before a line of code is written and before three teams start three versions of it.
A sequenced roadmap
The first project and the phases behind it, ordered by dependency, ready to hand to a build team. Ours, yours, or somebody else's.
The method
Discover, score, draw, sequence
Discover the use cases
Surface candidate AI applications across your operations, from the obvious to the overlooked.
Score and shortlist
Rank each candidate on value and feasibility, and name the ones not worth building.
Design the architecture
Draft the reference architecture and run the risk review on data, cost, and governance.
Sequence the roadmap
Order the work into a first build and the phases after, mapped to their dependencies.
The calls on the drawing
Every layer gets one of three answers
Build, buy, or reuse. Most architecture arguments are really one of these three, decided by accident, in a hurry, by whoever was in the room, and then lived with for years.
Build
Orchestration, retrieval, the data rules
The parts that encode how your business actually works. Nobody sells this, and if you buy something adjacent you'll spend longer bending it than you would have spent writing it.
Buy
Models, and most infrastructure
Where a vendor is genuinely better than you and the switching cost is manageable. The architecture keeps this layer swappable on purpose, so a pricing change is an inconvenience instead of a crisis.
Reuse
Auth, storage, the interface
The most common expensive mistake in the drawing. A second identity system, or a second place customer data lives, is a liability with a roadmap, whatever the project plan calls it.
What the drawing changes
What one shared gauge buys you
Priority stops being a volume contest
A scored shortlist replaces the loudest voice in the room as the mechanism by which projects get picked and funded.
The foundation exists before the first build
The first project is built to a drawing instead of improvised into a corner the second project then has to live with.
Governance is structural from day one
Data handling and access boundaries are drawn through every layer from the start, instead of being bolted on after a security review flags them.
Nobody is holding your roadmap
The model layer is swappable by design and the recommendations are vendor-neutral, because the architecture is scoped to your needs instead of to whoever got the meeting first.
Selected work
Drawings that ended an argument
What gets issued
Three documents a build team can work from
Written with enough detail that your team, another partner, or us could act on them without the person who wrote them in the room.
Scored use-case map
The ranked shortlist of AI opportunities with value and feasibility assessed for each.
Reference architecture
The blueprint for how models, data, and guardrails fit together across your intended builds.
Roadmap & risk review
The sequenced adoption plan alongside the data, cost, and governance assessment behind it.
Where the gauge breaks
Every sector has its own version of three pilots
Healthcare
Where three well-meaning pilots each carrying patient data is the actual risk, and consolidating them is most of the value.
Financial services
Where the drawing has to survive an audit, so the boundaries matter as much as the boxes inside them.
Professional services
Usually document-heavy, so retrieval is the layer that gets built and the one that decides whether any of it works.
Multi-site operations
Where every site has quietly solved the same problem differently, and the gauge was never agreed.
Six weeks
Have AI ambition but no clear first move?
Tell us where you think AI could help — we'll map the use cases, score them, and hand you a sequenced roadmap.
Choosing Flaidex for this
Four things the drawing has to be
Scored, not guessed
Every candidate gets a value and feasibility score, so effort follows evidence instead of whoever pitched hardest. The scoring is also where we name what isn't worth building.
The drawing comes before the build
A reference architecture for models, data, and guardrails exists before any project starts. The first build gets a foundation instead of a corner to improvise into.
Vendor-neutral by construction
The model layer is designed to be swapped. We don't resell anyone's platform, and the architecture should survive you changing your mind about one.
Honest about what not to build
Some ideas cost more than they return, and some problems are better solved by conventional software, a process change, or cleaner data. We say so in the scoring instead of nodding along to keep the meeting pleasant.
Working with us
What the engagement itself is like
One team across strategy and build
If you want the roadmap executed, the team that drew it can build the first project with the context already in their heads. If you'd rather not, that's genuinely fine.
A fixed, scoped engagement
A defined engagement, a defined set of deliverables, and an end point you can see from the start. No open-ended retainer.
Written to be built from without us
The map, the drawing, and the roadmap carry enough detail for your team or another partner to act on independently. The consulting stands on its own.
Questions
What people ask before the drawing starts
Q1We know we should use AI but don't know where to start. Can you help?
That's exactly what this is for. We work with you to surface candidate use cases across your operations, then score each on business value and feasibility so the list becomes a ranked shortlist instead of a wish list. You leave with a clear first move — the use case with the best ratio of impact to effort — rather than a vague sense that you ought to be doing something with AI.
Q2Will you tell us if AI isn't the right answer for something?
Yes, and that candor is part of the value. Plenty of problems are solved better and cheaper by conventional software, a process change, or simply cleaner data — and some AI ideas carry risk or cost that outweighs the benefit. We name those in the use-case scoring rather than nodding along, because steering you away from a bad build is as useful as pointing you toward a good one.
Q3What do we actually walk away with?
Three things: a scored use-case map that ranks the opportunities, a reference architecture showing how the models, data, and guardrails would fit together, and a sequenced roadmap that orders the work into a first project and the phases after. Plus the risk review behind it. It's a plan you can hand to a build team — ours or your own — not a slide deck of possibilities.
Q4Do you cover the risks, not just the opportunities?
The risk and governance review is a core part of the work. We assess whether your data is ready to support the use cases, model the cost exposure so a project doesn't surprise you with its running bill, and define the governance and oversight your AI use will need. Naming those obstacles early lets them shape the roadmap, rather than surfacing halfway through a build and stalling it.
Q5Do we have to build with you afterward?
No. The roadmap is yours to execute however you choose — with your own team, another partner, or us. We design the strategy and architecture to be buildable independently, with enough detail that a competent team can act on it without us. If you do want us to build the first project, we can, but the consulting stands on its own and isn't a lead-in you're obligated to follow.
Elsewhere in AI Transformation & Consulting
Have AI ambition but no clear first move?
Tell us where you think AI could help — we'll map the use cases, score them, and hand you a sequenced roadmap.
