At Empyreal, sprint planning is our first line of defense against chaos. It ensures the whole team agrees on what, when, and how we’ll deliver features. In the very first meeting for our SaaS project, ideas were flying, the CEO added new features every five minutes, the lead engineer fretted about our timeline, and designers sketched interfaces on whiteboards. Without structure, everything threatened to spiral. 

Classic scope creep set in: every sideline request risked blowing up our timeline and budget. Founders and PMs often underestimate how easily projects derailed at this stage. As one Atlassian guide warns, uncontrolled scope changes “affect the schedule, budget, costs, and resource allocation” and can compromise key milestones. 

Even as voices rose on Slack and a critical decisions thread grew tangled, we held firm to our agile fundamentals. We recall one designer’s message at 2 AM: “Without clear requirements, we’re running blind!” That was exactly the point: agile’s short cycles and constant collaboration would become our lifeline. Agile methodology, by definition, helps teams deliver faster with short development cycles and frequent releases, while adapting flexibly to change. 

It’s a cliché on fast-moving projects: keep the team in lock step. Right after chaos hit, we ran a strict sprint planning session. We wrote every idea into a living backlog, estimated the effort, and set clear goals for the sprint. This clarity was crucial: it turned frantic Slack debates into a one-page plan everyone agreed on.  

Even with planning underway, blockers still popped up daily. To catch these early, we instituted a brief daily stand-up each morning. As Atlassian puts it, a stand-up is “a short, daily meeting to discuss progress and identify blockers”. Our stand-ups became mini-alignment huddles, in the spirit of sports teams briefing before a big play. Developers, PMs, and QA shared what they did yesterday, will do today, and flagged any roadblocks. These quick syncs kept everyone aware of the sprint’s state and prevented last-minute panics. 

By the end of the first week, the difference was stark. The early chaos gave way to focused energy. As one PM put it: “We may not have a completed product yet, but at least we all know where we’re headed.” In practical terms, that meant we had:  

Sprint Checkpoints & Goals: 

We defined a clear goal for each sprint. Every feature request went onto the backlog and was prioritized together .

Controlled Backlog: 

We stopped ad-hoc changes. All new ideas and “urgent” features went through a review to prevent uncontrolled scope creep.  

Daily Stand-ups: 

Quick daily syncs kept the team aligned on progress and blockers.  

These structured steps didn’t eliminate drama; on the contrary, now we channeled it productively. The founder and CTO still debated features loudly, but now their fights happened in scheduled planning meetings, not random Slack threads at midnight. Founders saw that our process could accommodate new ideas without derailing everything. By pulling rough plans into organized sprints, we turned initial chaos into a repeatable routine. 

Figma Reviews 

Next came design, and with it a new round of friction. Our product designer unveiled the first Figma mockups, a sleek dashboard and onboarding flow, and we were stunned by its depth. But as soon as the team viewed it, new questions flooded in. “That nav tab should be on the left,” “We need a settings page,” “I think the color scheme’s off-brand.” 

Suddenly, the Slack design channel was alive with annotations and emoji reactions. The CEO loved the mockup except for one aspect; the lead dev fretted over implementation details.  

We learned fast that sharing designs early and often was key. In fact, Figma’s research shows that successful teams are 67% more likely to share in-progress work at set checkpoints with design, dev, and product.

So we built recurring design review sessions into our sprint cycle. At the end of each two-week sprint, we hosted a Figma walkthrough with all stakeholders. This was more than a casual look: it was a formal “sprint review” for UI. 

In those sessions, designers scrolled through the live Figma file while PMs and developers chimed in, asking questions and adding comments.  

These Figma meetings became a safety valve. Designers could justify UX choices, developers could request specs, and PMs could tie designs back to user stories. When a feature got pushed back to “needs revision,” we traced it to one of these reviews, catching a misalignment early. One of the backend engineers even learned Figma’s developer mode on the fly, so he could inspect layouts in real-time. 

Soon, a culture of collaboration took root: there were no “sneak changes.” Every visual tweak went through the same sprint checkpoint process. As this collaborative process matured, the drama turned inspiring. Developers began to appreciate the polished designs and suggested technical improvements before code was written. Designers, for their part, respected that some elements had to be reworked for performance or feasibility. 

The whole team got comfortable tagging each other in Figma, debating padding and color swatches like pros. By launch, our Figma files were tightly annotated, with clear notes on interactions and data flows. 

What underpinned this was a smooth tech handoff. Once designs were approved, the handoff to developers was seamless: the Figma files included style guides, UX flows, and notes on business logic. (In design terms, we followed the industry definition of a handoff: transferring a designer’s intent and specifications to implementation .) 

Our handoff checklists included items like icon file formats, component naming, and form validation rules, so no detail was lost. This design-to-dev handoff bridged the gap between creative vision and code, ensuring that by the time the engineers opened Figma, they already understood the why and how behind each screen.

By mid-project, we were humming. Each sprint’s end no longer brought dread about UI chaos, but excitement for the upcoming design reveal. The earlier fights in Slack became collaborative sketch sessions. The founder even praised the process: “We built these UIs together, sweat and all.” 

Share Designs Early: 

We built in frequent design demos. Top teams share work in defined sessions up to 67% more often, leading to fewer late surprises.

Structured Design QA: 

Every mockup goes through a sprint-end review with the whole team. No design is “final” until it passes that checkpoint.  

Detail-Rich Handoffs: 

Our Figma files double as spec docs. They include colors, fonts, flows, and comments so developers get the full picture.  

These steps kept creative tensions from spiraling into rework nightmares. When the product CEO spotted an odd gradient at the 11th hour, the fix was quick: the designer updated Figma, and the Git branch auto-deployed a new build. With good processes, even angry emoji threads turned into rapid improvements.

Backend Debates 

With UI coming together, we turned to the backbone: our server architecture. And holy cow, did opinions clash. It started innocently enough with a vote: Node.js or Python? Then we argued REST vs GraphQL. One afternoon, a Slack thread blew up about whether to build a monolith or a bunch of microservices. It felt like being back at planning, but now with syntax highlighting.

As Atlassian describes, a monolithic app is a single unified codebase and can be great early on for simplicity, while microservices break things into independent pieces. We were at that classic fork-in-the-road: start simple or build for scale? The debates got heated, and some shouted that microservices were the future, while others warned of needless complexity.  

Here, Empyreal’s structured-but-flexible mindset helped. We paused formal arguments and sketched out tradeoffs. We recalled an example: Netflix famously outgrew its monolith and went microservice, but often monoliths are just fine at first. Indeed, Atlassian notes that the main advantage of a monolithic architecture is fast development speed from having one codebase.

Starting with a monolith meant we could iterate rapidly without managing dozens of repos. We decided on a simple Node.js monolith for the MVP, with a plan to modularize later. This decision was documented in an architecture doc, shared on Confluence, and agreed upon as a team.  

Once the tech stack was chosen, internal debates shifted to coding style and standards. Some engineers wanted to adopt ESLint rules for everyone, while others feared slowing down. We handled this through code guilds and checkpoints: before a big feature branch merged, we held mini architecture demos so others could review the proposed design. 

Every major change got an “architecture review” akin to a mini-sprint planning for code. This cut down midnight arguments, no one wanted to refactor broken code.  

Another key practice was our Git workflow. To keep a large team from tripping over each other, we adopted a feature-branch strategy. In this workflow, all feature development happens in dedicated branches, keeping the main branch clean. We would never commit experimental code directly to main. 

Instead, developers opened pull requests for each feature branch. Pull requests became our discussion forums: teammates reviewed the code, left comments, and signed off before merging. In Atlassian’s words, these pull requests made it “incredibly easy for your team to comment on each other’s work”. 

This helped immensely with the tech debates. If an approach was questionable, someone would call it out in the PR. If we disagreed, we held a quick meeting. But by then, the stakes were smaller; we were just improving specific code, not rewriting the entire plan.  

By aligning with these practices, the arguments became productive. The CTO said after one heat-of-the-moment Slack call: “We argue less because we agree more, thanks to these rules.” For example, when a backend dev wanted to introduce a new caching layer, he created a branch and a PR. 

In the PR discussion, we hashed out concerns about consistency and performance. Everyone could see code diffs instead of shouting in a channel. We ultimately tweaked the design to meet all needs. 

Balanced Architecture: 

We chose the simplest backend that worked. A monolithic app lets us build fast (initial simplicity is a real advantage ). The plan was always to refactor later as needed.  

Feature Branch Workflow: 

Every change lived in its branch. As Atlassian says, this means the main never gets broken code. Combined with pull requests, it kept us merging only tested, reviewed work.  

Code Reviews & Checkpoints: 

No code goes live without someone else’s eyes. Pull request discussions became mini-reviews that resolved disputes before they reached production.  

Even with these safeguards, bugs still slipped through (as they will). But because our Git process was strong, patches came swiftly. For example, during a late sprint, a miscommunication caused two features to merge conflicts. 

The conflict was caught immediately by our CI tests, and the fix branch was ready within hours. The debates over how to integrate two APIs turned into a productive pairing session, instead of a blame game.

Bug Discoveries 

Just when we thought we were cooking along, the bugs appeared. And they came in droves. The night before a major demo, our internal tester uncovered a show-stopping issue in the user signup flow: new accounts were getting created but not logging in properly. 

Suddenly, the team was back to late-night Slack, Ping-Ponging blame (the designer said it looked like a UX glitch, the dev said it was a code bug, and QA said their steps were precise). Someone joked, “New sprint goal: Find the bug before 3 am.”  

This is where Empyreal’s internal QA process truly kicked in. Rather than letting frustration win, our QA lead called a bug triage session. We replicated the issue, pinned it as a high-priority defect, and assigned it to the developer on call. By morning, a fix was in review. 

The whole sprint had a reality check: no matter how well you plan or design, bugs happen, but having a plan for them is key. To systematize this, we rely heavily on automated testing and continuous integration. Our CI pipeline runs whenever code is pushed, executing a suite of unit and integration tests. 

Industry advice emphasizes this: version control integration like Git and CI/CD pipelines is critical for SaaS QA. Every build running to staging triggers our test suite, so most bugs are caught almost instantly. In this case, our pipeline alerted us before the demo that the login flow was broken, saving face for the sprint review.  

Alongside automation, we practice a disciplined bug life cycle. When a defect is found, it’s logged in our tracking tool with steps to reproduce. We then assign a status (New → Assigned → Open → Fixed → 11 Retested → Closed) for every bug. This mirrors the classic defect life cycle used in software engineering: its purpose is to track and communicate each issue until it’s resolved. 

In practice, this means that when the signup bug was logged, every stakeholder could see its status change from “new” to “in progress” to “closed.” That transparency prevented any dispute about whether the bug was getting attention.  

Our testers also adhere to “shift-left” principles: they get involved early and often. Rather than waiting until the end of development, QA attends grooming sessions to ask tough questions about new features. This allowed them to identify potential issues before the first code was even written. 

For instance, early in one sprint, the tester noticed that a planned pagination feature needed corner-case tests (like when there are zero results). We added those tests to the definition of done, and by sprint’s end the code handled that case cleanly. 

The combination of automated checks and proactive QA reduced sleepless nights. Yet we still prepared for the worst. Before every release, we ran a “bug bash”: the whole team, including founders and PMs, clicked through every feature for 24 straight hours, hunting edge cases. It could be grueling (eyes bloodshot, coffee flowing), but morale was high, mostly because if bugs were found, they got fixed on the spot, rather than piling up to derail launch.  

Automated Testing & CI: 

Our CI server builds every branch and runs tests on each push. As experts note, integrating tests with Git and CI/CD means bugs get caught immediately. We even have daily smoke-test scripts in Slack to double-check core paths.  

Clear Defect Workflow: 

Every bug followed a tracked life cycle with statuses. This “defect pipeline” means nothing falls through the cracks. Developers could see exactly who was working on what fix.  

Early QA Involvement: 

QA participated from the start of each sprint. We wrote test plans in parallel with development, so testers weren’t blindsided. By catching likely issues early, we avoided last last-minute scramble.  

In the end, the final week of development still had late-night adrenaline, but no one felt helpless. When the team leader said, “Looks like it’s gonna work after all,” it was because our process had already corrected most mistakes by then. Founders saw QA not as a bottleneck but as a safety net that let us pivot with confidence. 

Launch Week 

Finally, launch week arrived like the calm before a storm. Our team had grown accustomed to the rollercoaster, but nothing beat the mix of excitement and panic that the first production release brings. Monday morning, we had a big release checklist: final code freeze, all migrations ready, UAT (user acceptance testing) sign-offs, and a production deployment plan. 

By Wednesday, it was go-time: the version was tagged in Git, CI ran one last full suite of tests, and Ops got the alert to promote the build to production.  

Our deployment leveraged continuous delivery (CD) best practices. In continuous delivery, teams use automation to release software in short, reliable iterations from the source repository to production. This was not lip service for us. Each sprint had left the product in a “shippable” state, a release candidate on standby. The day before launch, the engineering team and product owner formally confirmed: everything in the main was test-green and documented. We hadn’t even needed a last-minute hack; our pipeline had already merged several hotfix branches ahead of time in response to previous bug bashes.  

On launch day, the first round of deployments went out with no incidents. We monitored error logs and user sign-ups like hawks, ready to roll back if needed. (We had a rollback plan, but happily it wasn’t triggered.) 

By Thursday afternoon, our CMS user was live. Users in a beta group started posting feedback, a mix of praise and minor issues, all expected. We took that feedback into the next sprint as enhancements.  

As we celebrated with pizza (and yes, a few victory beers), the team took a moment to reflect. The final retrospective was a mix of relief and pride. The product manager noted that we’d achieved “continuous deployment rigour: shipping quality predictably” as promised by Atlassian’s definition of continuous delivery.

We’d also logged over 50 user-reported ideas during launch, prioritized many as quick wins, and planned for them. Everyone agreed: we had a full pipeline from code commit to happy user, and it worked.  

In that retrospective, our process shone. We discussed what to improve next time, from better analytics to smoother marketing coordination. This is exactly the point of retrospectives: reflecting helps teams learn and “process information more thoroughly, reduce errors and failures, and be more agile when things change”. 

Now that the product was live, we carried that lesson forward.  

Automated Deployment Pipeline: 

Our CI/CD pipeline did the heavy lifting. We had a fully automated staging-to-production workflow, so the release was “just press the button”, no human copying files needed.  

Shippable After Every Sprint: 

By keeping the product releasable at all times, we avoided any giant integration nightmare. Per best practices, we kept a shippable version ready after every sprint for the final go/no-go decision.  

Retrospectives & Learnings: 

Immediately after launch, we held a sprint retrospective. Reviewing our journey made us stronger. As research shows, this reflection ensures we catch any lingering problems and adapt for the future.  

Launch week was chaotic, thrilling, and yes, a little scary. But when customers finally started logging in and saying, “This app solves our problem,” every sleepless night felt worth it. The founder toasted the team: “We delivered custom software under real pressure, hats off to us.” 

Wrapping Up

Building a SaaS product from scratch was not a straight road for Empyreal. There were muddy stretches of planning confusion, steep climbs of heated debates, and plenty of unexpected potholes. But our structured yet flexible system acted like guardrails. Sprint checkpoints turned wild ideas into coherent goals. 

Cross-discipline reviews (from Figma critiques to code walkthroughs) turned fights into feedback. Rigorous QA and Git practices caught detours before we lost the map. And in the end, that system let the team survive the journey and even enjoy parts of it.  

For founders and project managers wondering if this level of drama is normal, it is. Every great product is born from some mess. What makes the difference is having a process you trust. 

At Empyreal, we know that the road to great custom software is full of twists, but with disciplined sprints, thorough QA, clear handoffs, and smart version control, we can drive through it. The view from the launch pad is worth every bump along the way. 

Click on the link here to learn in detail about Empyreal’s services.