Regulated fintech — 10K to 100K users without a rewrite
- ContextA regulated fintech platform under PCI-DSS and SOC 2 obligations, scaling from roughly 10K to 100K users over an 18-month period. Engagement ran 2018–2020.
- Business problemGrowth was outrunning release reliability. Each release required a named senior engineer to babysit the deploy, and incidents in a regulated environment carried audit consequences, not just downtime. The internal assumption was that the team needed more QA headcount.
- DiagnosisThe CI history said otherwise. The defects reaching production were concentrated in API contract changes and environment drift — neither of which the existing UI regression suite touched. The suite was slow, expensive and structurally aimed at the wrong layer. This was a quality architecture problem, not a coverage problem.
- InterventionRebuilt the automation architecture around contract and integration tests; put CI quality gates in front of the five journeys carrying revenue; fixed staging/production environment parity so a green run predicted real behaviour; added performance engineering ahead of the growth curve; and moved release control from a named individual to a documented, gated process.
- Outcome10× user growth carried on the existing architecture with no rewrite, 99.9% uptime sustained, and release cycle time down approximately 40% against the pre-engagement baseline.
- What changed permanentlyRelease ownership stopped depending on one person. The team hired one QA engineer into a system that could support them, instead of two onto a treadmill.
Attribution note: the release-cycle and uptime figures are directly attributable to the quality architecture work and measured against the six months before the engagement. User growth is a business outcome the engagement enabled by removing the scaling constraint — it was not caused by it. Source figures come from the client's close-out review; I can walk through the measurement basis on a call.
B2B SaaS — moving a collaboration product upmarket
- ContextA B2B collaboration product with strong SMB traction attempting to move upmarket into enterprise accounts. Engagement ran 2021–2023 in a product leadership capacity.
- Business problemEnterprise deals were reaching late stage and then stalling. The roadmap was being rebuilt every quarter around whichever prospect had complained most recently, and engineering was shipping features that did not move a single deal forward.
- DiagnosisThe blockers were not feature gaps — they were trust gaps: audit logging, SSO/SCIM, permission granularity, data residency and a security review the company could not pass. Sales was selling around them; product was building past them.
- InterventionNamed the assumptions behind each enterprise bet and ran time-boxed spikes with pass/fail criteria written in advance. Rebuilt roadmap sequencing around deal blockers rather than feature requests, including the decision to kill two heavily-requested features. Installed prioritisation logic and roadmap governance the team could re-run without me.
- OutcomeAverage contract value roughly tripled, enterprise win rate reached approximately 70% in the period following the enterprise capability release, and the company raised a $10M Series B.
- What changed permanentlyRoadmap decisions became defensible in a board meeting. The prioritisation mechanism survived my departure and a change of Head of Product.
Attribution note — read this one carefully. The ACV and win-rate movement followed the enterprise capability sequencing I led, and I am comfortable claiming direct influence on which capabilities were built and in what order. I do not claim causation for the Series B: a funding round reflects market, team and traction far beyond any one advisor's contribution. Win rate is stated over the four quarters following the enterprise release; the sample size and baseline are available on request with client permission.
Founder & operator — what building teaches that advising cannot
- ContextQuickVikalp Technologies (2023–2026) folded into Aarohii AI Solution Private Limited, now an AI product house with three live products and one launching: PixellPeep (AI-powered visual regression testing), Captverse (a business operating system with CRM, sales, inventory, payments and AI agents), ViraQueue (AI-drafted social posts held for human approval) and Auvora (enterprise vendor management and source-to-settlement procurement, coming soon).
- What was actually hardEvaluating AI features honestly. A model-backed feature can demo beautifully and still be unshippable, because the failure distribution is not visible in the happy path. Building PixellPeep forced me to design evaluation and observability for non-deterministic output rather than theorise about it.
- Second lessonScope discipline under real cost. Folding QuickVikalp into Aarohii was a decision to stop spreading a small team across too many bets. It is the same decision I now ask clients to make about roadmaps, with more credibility for having taken the loss myself.
- Third lessonQuality economics from the paying side. When the cost of a flaky pipeline comes out of your own runway, the argument for quality architecture stops being philosophical.
- How this informs the advisory workEvery recommendation I make is one I have had to live with commercially. That is the difference between advice and opinion — and it is why the ventures stay, rather than becoming a logo wall.
What leaders have said
“He found three architectural gaps our team missed in two years. That report is what we showed investors.”
What Rohann specifically changed: the architecture assessment became the technical section of the fundraise narrative.
“Six-week releases became weekly. The framework has run 18 months without a rewrite.”
What Rohann specifically changed: moved quality gates to the integration layer and removed the single named owner from the release path.
“He told us two of our top features were the wrong bet. Killing them is how we landed enterprise.”
What Rohann specifically changed: rebuilt roadmap sequencing around enterprise deal blockers instead of feature request volume.
On anonymity: these quotes are published with permission; names and companies are withheld under NDA. Stage, team size and engagement type are stated so the context can be verified in conversation, and I can arrange a direct reference call for serious enquiries where the client agrees.
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