From ScrumBuddy to Brunelly: Bad Requirements Are Still Killing Software Projects
A note from the CEO:
ScrumBuddy started with a problem every seasoned developer or tech lead still runs into: bad requirements quietly killing otherwise capable teams.
If you’ve built software in the real world, this will sound familiar.
Alignment Breaks Long Before Code Fails
Most projects kick off with what looks like shared understanding, until delivery reveals the team never aligned on the real users’ needs.
The result? Predictable: rework.
Closely behind came another frustration: estimation failures caused not by inexperience, but by underspecified thinking.
Even in mature Agile or Scrum environments, teams miss estimates roughly 70% of the time. Planning becomes fragile, delivery unpredictable, and trust harder to maintain.
The tools don’t fix this. Clarity does.
Jira.
Azure DevOps.
Agile coaches.
Ceremonies.
Retrospectives.
The tooling is there. The process is there. But outcomes rarely change.
It isn’t the people. It isn’t the tools. It’s missing clarity at the foundation.
Iteration Without Clarity Just Accelerates Churn
Iteration is vital. But iteration without clarity isn’t progress, it’s faster churn.
Vague requirements build assumptions into your foundation, accumulate technical debt, and increase expensive course corrections.
Every product team hits the same tension: when is it safe enough to start building?
Momentum alone can’t rescue broken thinking. Speed moves you faster, but potentially in the wrong direction.
That’s what led us to build ScrumBuddy.
ScrumBuddy Started as a Fix, Not a Product
Good requirements change everything.
Clear requirements lift the entire delivery chain: improving estimation, planning, quality, and decision-making.
ScrumBuddy surfaced gaps, contradictions, and assumptions before code was written. Teams moved faster, waste declined, and planning became grounded.
But over time, we realized a deeper truth: requirements don’t just define scope, they define everything downstream.
Context Loss Is the Real Scaling Problem
Requirements often get written once, split into tickets, copied across tools, re-explained in meetings, and reinterpreted by different people.
As work moves through modern delivery systems, context erodes faster than teams realize. Changes trigger compensations: more meetings, more process, more coordination. Less progress. The system itself becomes the bottleneck.
Non-Functional Requirements Are Where Products Fail
Functional features are just the surface. Most failures come from missed non-functional requirements (NFRs):
- Performance
- Scaling
- Security
- Resilience
- Operational realities
- Data growth and compliance
NFRs are expensive to bolt on later and every new requirement interacts with existing systems. Without clear understanding, “small” changes destabilize the system. Technical debt, more often than not, accumulates from incomplete understanding.
Requirements must stay connected to architecture, code, and quality throughout the lifecycle.
We Didn’t Need Another Tool. We Needed a System
Improving requirements alone wasn’t enough. Fragmentation was a problem.
What teams really need is clarity that travels: from planning to architecture to implementation to review.
Not another plugin. Not another Jira layer. A connected system that keeps context intact.
Enter Brunelly
ScrumBuddy helped fix requirements in Scrum teams. Over time, we realized the same problems exist across planning, architecture, code, and tests far beyond Scrum. The old name was boxing us in. We needed something bigger: Brunelly.
Brunelly remembers requirements as they flow into architecture, code, and tests. It keeps non-functional requirements visible. It shows what a new requirement touches before approval. It maintains clarity, so teams can act confidently.
Brunelly is a semi-autonomous engineering system. Humans set direction, validate assumptions, and apply judgment. Brunelly takes on the structured, repetitive, execution-heavy work that slows teams down.
It’s AI for teams who care about longevity, structure, and clarity, and not just living under the illusion that momentum equates to progress.
The name? Inspired by Isambard Kingdom Brunel, one of history’s most ambitious engineers. He built systems that scaled with purpose and endured. That’s what Brunelly represents.
Vibe Coding Works. Until It Doesn’t
Fast, fluid, AI-assisted development is what we call “vibe coding”. Its a great way to experiment. But when it’s time to build for real users, real scale, and real change, momentum isn’t enough.
We explored this in depth in my next article → stay tuned
A Clearer View of the Next Phase of Software
Software teams need clarity, structure, and the ability to evolve. Brunelly is built for that next phase: building software that lasts.
Hello.
Every senior engineer has lived this movie: the codebase gets blamed, but the real failure happened weeks earlier when requirements were really just vibes, half-sentences, and assumptions nobody wrote down. Then Jira fills up, ceremonies multiply, velocity charts look busy… and the team ships rework.
What I like about this note is that it calls out the actual bottleneck: context decay. Requirements don’t die because people are lazy—they die because modern delivery is a game of telephone across docs, tickets, Slack, PRs, and handoffs. By the time work hits implementation, “what we meant” has been replaced with “what we inferred.” That’s where missed estimates come from. Not incompetence. Missing constraints.
And the NFR angle is the one most" AI for devs" tools conveniently ignore. Features are cheap to describe. Performance, security, scaling, resilience, data growth, compliance—that’s where products quietly fail, because those constraints aren’t visible in the ticket, and they don’t show up until load, audits, or outages. If you don’t keep NFRs attached to the work, you’re basically choosing future incidents.
The interesting promise here isn’t "another Jira layer." It’s the idea of a system that keeps requirements connected to architecture decisions, code touch points, and tests—so you can answer the questions teams always ask too late:
If Brunelly can reliably surface contradictions, missing NFRs, and impact radius "before" implementation—and do it in a way that fits how teams actually work (PRs, CI, ADRs, threat models, runbooks)—that’s not hype. That’s compounding leverage. Because the expensive part of software isn’t typing code; it’s correcting decisions made with incomplete context.
"Vibe coding works until it doesn’t" is exactly right. Speed is great for exploration. But when you’re building something meant to survive users, scale, and change, clarity is the real accelerator. Tools that preserve clarity don’t just improve delivery—they reduce the hidden tax every engineering org pays: rework, risk, and fragile systems.
This is exactly the failure mode we kept seeing.
The codebase becomes the visible scapegoat, but the fracture happened upstream when requirements were loosely phrased, constraints were implied, and non-functional expectations lived in someone’s head instead of the system. By the time it hits implementation, you’re no longer building from intent, you’re building from interpretation.
You’ve articulated the core issue well: context decay. Modern delivery isn’t linear. It’s fragmented across tools, conversations, and artifacts. Every hop strips nuance. By the time a PR is opened, the original reasoning is often gone. Estimates fail not because engineers can’t estimate, but because they’re estimating against incomplete constraints.
The NFR point is where most “AI for devs” tooling becomes superficial. Generating code for a feature is easy. Surfacing that this change increases latency by 40ms, violates a data retention policy, touches a shared contract, or increases blast radius across services, that’s where engineering judgment lives. If NFRs aren’t structurally attached to requirements, they become postmortems.
The goal with Brunelly isn’t to create more documentation. It’s to preserve intent continuity. When a requirement is introduced, the system should help answer: what does this touch, what constraints apply, what contracts are affected, what quality gates should be triggered? Not weeks later, immediately.
You’re right that the expensive part of software isn’t writing code. It’s unwinding decisions made without full context. If we can surface contradictions and impact radius before implementation, that compounds over time; fewer reversals, fewer emergency patches, fewer “how did this slip through?” moments.
Speed is a tool. Clarity is a multiplier. The teams that preserve clarity systematically are the ones that scale without accumulating invisible fragility.