This article was written by Leke Ojo, Product Manager at Rank Capital. Rank Premium is your personal investment banker that gives you access to expert wealth building strategies. Rank Premium is coming soon.
I built a full Investment Dashboard in a few days. PRD, data model, wireframes, tested working prototype. Then I handed it over to engineering for deployment.
The product is a client-facing portal where customers can track their holdings, gains and losses, and cash balances. There is also an internal dashboard for wealth managers to manage client accounts. Auth, email alerts, audit trails, CSV uploads. The full V1 scope.
I used AI as a partner all through the process. Not to avoid thinking, but to reduce the time between having an idea and actually shipping it.
What the process looked like
If you have ever been part of a product team, you know the drill. Someone has a great idea. That idea gets turned into a document. The document gets circulated. Meetings happen. The document gets revised. More meetings. By the time engineering actually starts building, three months have passed and the original idea has been polished to within an inch of its life or watered down into something nobody is particularly excited about.
Speed is not just a nice-to-have. In a fast-moving financial market, the difference between shipping something in a week versus a quarter can be the difference between leading and catching up.
Seven Things I Learned Along the Way
- Bring your real problems. AI becomes genuinely useful when you give it real constraints — a real user flow, a real data model, a real edge case. Toy examples produce toy results. The moment I stopped treating it like a demo and started treating it like a work session, everything changed.
- Go back and forth, don’t just ask once. The best output came from iteration — pushing back, adding constraints, asking “what if we approached this differently?” Think of it as a conversation, not a command.
- Structure your instructions well. A well-organised brief, clearly broken into sections, produces dramatically better results than a wall of text. Front-loading your context saves time for everyone, human or AI.
- Tell it about your design system upfront. If you share your colour palette, typography, and component patterns at the start, the output looks like your product, not a generic template. This one habit alone saved hours of corrections across every screen I built.
- Get your engineers to share the technical context early. When AI knows your actual tech stack, your framework, your folder structure, your API conventions, the handoff from prototype to production becomes much smoother. Without this, you end up rebuilding decisions that were already made.
- Break the product into modules, not one big blob. I treated auth, the client dashboard, and the wealth manager interface as separate chunks, each with its own focused session. Problems surfaced earlier. Context stayed sharper. Trying to build everything in one pass is a recipe for chaos.
- Review everything like it came from a junior colleague. AI can produce output that looks polished but contains assumptions you never signed off on. The product decisions — what to build, what to cut, what actually matters to the user — those still sit with you. Do not outsource your judgment just because the output looks professional.
The Bigger Picture
This whole experience reminded me of the first time I saw a computer in primary school. Just clicking around, not really understanding it but knowing it could do anything. Building with AI brought that feeling back. I had not felt that in a long time.
The Product Manager role is not getting smaller. The cycle time is. And the Product Managerss who figure out how to build at this pace will set the standard for what comes next.
What I Would Do Differently Next Time: Using Get Shit Done (GSD)
After shipping the dashboard, I came across Get Shit Done (GSD), a spec-driven development system built for Claude Code. It is a lightweight context engineering and meta-prompting layer that makes AI coding tools reliable and repeatable. Looking at my process in hindsight, GSD would have solved several friction points I ran into and made the entire build more structured.
Here is what I would change.
Structure the build as phases, not one long session
My process was largely one continuous thread with AI. It worked, but by the time I was deep into the wealth manager dashboard, context from earlier decisions like auth logic, data models, and validation rules was getting diluted. AI responses started losing precision.
GSD calls this context rot, and it solves it with phased execution. Each phase gets its own planning, execution, and verification cycle with a fresh context window. No accumulated noise.
This is exactly the “scope before you prompt” lesson I learned the hard way, but turned into a system. With GSD, I would have run /gsd:new-project to capture the full vision, requirements, and roadmap upfront, then /gsd:plan-phase for each module. Each phase would execute in a fresh context with the full token budget dedicated to that module alone.
Lock implementation decisions before building
One thing I did well was iterate on decisions with AI before building. But those decisions lived in conversation history, not in a structured document. When I revisited a module later, I sometimes had to re-explain constraints I had already worked through.
GSD has a /gsd:discuss-phase command that captures implementation decisions (layout preferences, interaction patterns, empty states, error handling) into a CONTEXT.md file. That file feeds directly into the planning and execution steps. My decisions would not just be somewhere in the chat. They would be structured context that every subsequent agent reads automatically.
Let parallel agents handle research and verification
I did all the research and testing within the same conversation. The same context window was doing requirement analysis, code generation, debugging, and verification simultaneously. That is a lot of load on one session.
GSD spawns parallel agents for different stages. Researchers investigate implementation approaches. Planners create atomic task plans. Executors build in fresh contexts. Verifiers check the work against goals. The orchestrator stays light. My main session would have stayed fast and responsive while the heavy lifting happened in dedicated sub-contexts.
Get atomic commits from the start
My git history from the dashboard build is functional but not surgical. Some commits bundle multiple changes because the AI was building across concerns in the same pass.
GSD enforces atomic commits per task. Each task gets its own commit with a clear message. If something breaks, git bisect finds the exact failing task. Each change is independently revertable. For a financial product where auditability matters, this discipline would have been worth it from day one.
Use structured verification instead of manual testing
I tested the dashboard manually and caught issues through my own review. But there was no structured step that checked the output against the original requirements systematically.
GSD’s /gsd:verify-work walks you through testable deliverables one at a time. Can a client see their portfolio balance? Does the CSV upload validate column headers? If something fails, it spawns debug agents to diagnose root causes and creates fix plans ready for re-execution. No manual debugging, no guessing where things went wrong.
The bottom line
My process worked. I shipped a tested prototype in days. But it was powered by my own discipline in structuring prompts, scoping modules, and reviewing output carefully. GSD codifies that discipline into a repeatable system.
The context engineering, phased execution, and spec-driven planning are not nice-to-haves. They are the difference between a good outcome that depended on personal effort and a reliable process that works consistently every time.
Next build, I am running GSD from day one.
Now Here Is the Part That Should Excite You
Everything you just read — the dashboard, the architecture, the speed of building — all of it exists in service of something bigger.
We built this infrastructure because we believe that the kind of investment expertise that used to be reserved for wealthy clients with private bankers should be available to everyone. Not a generic robo-advisor that moves money around based on a five-question quiz. Not a dashboard full of numbers with no one to help you understand what they mean.
Rank Premium is something different.
Think of it as having your own investment banker — someone (and something) that knows your financial situation, tracks your portfolio, spots opportunities, flags risks, and helps you make decisions with confidence. Not instead of you. Alongside you. The wealthy have had access to this kind of personalised financial guidance for decades. We are changing that.
Rank Premium is not live yet. But it is coming — and when it launches, the waiting list is going to be the place you want to be. Early access means getting into a system that is built to grow with you: smarter recommendations, deeper insights, and the kind of wealth management that used to require a minimum investment most of us will never see. If you have ever looked at your savings and thought “I know I should be doing more with this, I just do not know where to start” — Rank Premium is being built for YOU.
