I still remember the slide from our 2013 roadmap: “Pick a lane.” The safe bet was a mainstream stack like JavaScript, PHP, maybe even Rails. But we chose Clojure. At the time, it felt like an odd way to compete. A decade in, that choice shapes how we scope work, hire, and run projects for clients across e‑commerce, healthcare, fintech, and SaaS. This is the story of that choice, the risks we took, what didn’t work, and the numbers that convinced us we were on the right path. The bet and the why Generalist shops compete on price and headcount. We believed the more durable advantage was leverage: fewer lines of code to maintain, faster feedback loops, fewer bugs that escape to production, and faster time-to-change when clients need to pivot. We chose a programming paradigm – functional programming – that delivers those things. It meant: Speed without chaos. Our development model turns around working features in days, not weeks, because we can test ideas immediately.
Correctness by design. The way we structure code (immutable data, pure functions) catches entire classes of bugs before they reach users.
Small teams, big scope. We don't need 20 engineers to ship what takes a generalist shop 50.
Change without fear. When clients need to pivot their product, we can rewrite significant parts of the codebase without fear of cascading failures. Speed without chaos. Our development model turns around working features in days, not weeks, because we can test ideas immediately. Correctness by design. The way we structure code (immutable data, pure functions) catches entire classes of bugs before they reach users. Small teams, big scope. We don't need 20 engineers to ship what takes a generalist shop 50. Change without fear. When clients need to pivot their product, we can rewrite significant parts of the codebase without fear of cascading failures. Specializing also clarified who hires us. Clients don't come for headcount. They come for small teams that actually unblock their roadmap. Don't Sell Opinions. Sell Outcomes. "Why functional programming instead of X?" In services, opinions don't win. Delivery metrics do. We learned to lead with measures buyers recognize: Time-to-first-working-feature – how fast can we show something real?
Cycle time per change – when you ask us to modify something, how quickly does it ship?
Bugs that escape to production – are we reliable?
Mean time to restore – when something breaks, how fast do we fix it?
Cost of change – does pivoting destroy our budget? Time-to-first-working-feature – how fast can we show something real? Cycle time per change – when you ask us to modify something, how quickly does it ship? Bugs that escape to production – are we reliable? Mean time to restore – when something breaks, how fast do we fix it? Cost of change – does pivoting destroy our budget? We pair those metrics with process transparency. We show clients exactly how we work: Requirements gathering
Risk assessment
Security planning
Communication cadence
Acceptance criteria and testing Requirements gathering Risk assessment Security planning Communication cadence Acceptance criteria and testing This matters because the client can see how the work will actually run. It's not "trust us." It's "here's how we work, here are the outcomes we deliver, and here's how you'll know we're on track." The Technical Principles (Without the Jargon) We could have picked any functional programming language. What mattered was building guardrails around it so that: The core stays flexible while the boundaries stay strict. We design strict contracts at the edges (where your system talks to ours, where we talk to databases, where third-party integrations happen). Inside, we stay nimble. This keeps experimentation fluid while making sure broken contracts get caught early. We standardize the toolbox, not the hammer. Every team uses the same editor setup, the same test frameworks, the same way to handle logging and monitoring. We maintain a "blessed" list of libraries and an upgrade playbook. This prevents the chaos of "every developer uses a different tool" while avoiding the trap of frameworks that become so "stable" they age into anchors nobody can upgrade. We test in layers. Unit tests for core logic. Integration tests for cross-component flows. Real-world scenarios that mimic what users actually do. Security is built in from day one, not bolted on at the end. We onboard aggressively. New team members start with standardized templates, pair programming with an anchor engineer, and can be productive in 4-6 weeks. Fully independent in 12 weeks. What the Numbers Say Internal metrics over the past 24 months: Time-to-first-working-feature: Down from 3–4 weeks to 7–10 days for typical projects
Cycle time per change: 28–35% faster than our pre-specialization baseline
Bugs that escape to production: 22% reduction quarter over quarter after we standardized our testing approach
Mean time to restore: Down 31% with better monitoring and system design
Developer ramp time: New hires are productive in 4–6 weeks, fully independent in ~12 weeks
Developer retention: +14 points vs. our company average before specialization (people genuinely like how they work)
Win rate in sales: +19 points when we lead with a case study showing our approach in the same domain Time-to-first-working-feature: Down from 3–4 weeks to 7–10 days for typical projects Cycle time per change: 28–35% faster than our pre-specialization baseline Bugs that escape to production: 22% reduction quarter over quarter after we standardized our testing approach Mean time to restore: Down 31% with better monitoring and system design Developer ramp time: New hires are productive in 4–6 weeks, fully independent in ~12 weeks Developer retention: +14 points vs. our company average before specialization (people genuinely like how they work) Win rate in sales: +19 points when we lead with a case study showing our approach in the same domain These aren't vanity metrics. They're how we decide if the strategy is working. If any trend the wrong way, we change the playbook. Three Real Examples Marketing Platform (Fortune 100 client) Marketing Platform (Fortune 100 client) The client was shipping one custom campaign every 6–8 weeks and the setup process was drowning them in manual work. We built a declarative language—essentially, a simple way to describe business rules in code. Result: campaign setup time dropped 60%, new variants in hours instead of days, and fewer errors because the rules were now testable instead of buried in spreadsheets. Healthcare Analytics Healthcare Analytics Their charts looked right but told the wrong story. The problem wasn't the visualization; it was the logic underneath. We rebuilt the calculation layer using pure functions and added tests to catch edge cases (scale, binning, overlapping data). Result: zero regression bugs across three releases, and analysts got correct charts on the same day instead of days later. Data Pipeline Modernization Data Pipeline Modernization A legacy system relied on custom workarounds to compensate for old database limitations. It worked—until it didn't. We refactored into well-documented, maintainable modules with explicit transaction handling and multi-source validation. Result: zero transaction errors, and when the client upgraded their database later, our system worked without modification. What Didn't Work (And What We Changed) Over-abstraction. Early on, we made code clever in ways that were hard for new people to understand. We learned to favor simplicity and clear naming over cleverness. Framework lock-in. "Stable enough" frameworks can become concrete anchors. We added upgrade policies and deprecation calendars to avoid inheriting ten-year-old decisions. Over-testing. Teams sometimes added so many tests everywhere that changes became slow. We shifted to testing the boundaries and the core logic, letting tooling catch the rest. Tooling chaos. When every developer uses a different editor, setup time balloons. We ship opinionated templates and let teams only diverge when they have a real reason. The next chapter We’re doubling down on compounding knowledge: more internal libraries, more shared DSLs, more open‑source contributions. We’re also pragmatic: Clojure isn’t the only tool in our shop, but it remains the backbone where concurrency, correctness, and changeability matter most. Ten years in, I don’t think of Clojure as our “unconventional choice.” I think of it as the decision that let usbuild a company where engineers do their best work, and clients see that in the results. build

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