Case study
Scaling engineering execution with AI-first workflows
I joined Sequifi in March 2024 to own engineering execution behind $50M+ ARR. Over two quarters I scaled the org from ~30 to 60+ engineers across six teams, cut customer-impacting incidents 40%, moved on-time delivery from ~60% to 95%+, and added ~25% velocity through AI-assisted workflows — faster and still correct, with reliability held at 99.9%+.
- ↓ 40%Production incidents
- 99.9%+Platform uptime
- ↑ 25%Engineering velocity
- 60+Engineers · 6 teams
- ~60% → 95%+On-time delivery
- 2 quartersTo scale the org
The challenge
What I walked into.
- Delivery ran on last-minute saves. On-time delivery sat near 60%, so commitments to product, the CEO, and the board were guesses. There was no reliable way to say what would ship, or when.
- Production was unstable. Customer-impacting incidents were rising against enterprise SLAs, and the platform behind $50M+ ARR could not absorb that drift.
- Key-person risk was concentrated. Critical production paths lived in the heads of a few engineers. When they were out, the system was exposed.
- The org was about to double. Growing from ~30 to 60+ engineers without execution systems would have multiplied the dysfunction, not the output.
- Process had outgrown its scaffolding. Headcount was climbing faster than delivery governance, observability, and operational discipline could keep up.
- Leadership needed certainty, not optimism. The executive team had to trust that engineering could carry growth without trading away reliability.
The approach
I treated AI as leverage on disciplined engineering, not a replacement for it. Copilot, Cursor, and Claude went into daily workflows to absorb repetitive work and shorten debugging and review cycles. Governance came first, because velocity without controls erodes reliability. Every gain had to clear the same quality and security bar. The goal was capacity for judgment, not faster transcription of risk into production.
The systems
How I built it.
Planning & prioritization
Sprint scope with hard boundaries and a prioritization frame tied to business outcomes, not activity. Leadership could see what was committed and what was at risk before the sprint, not after it slipped. Execution stopped being a surprise.
Delivery health
Engineering KPIs anchored to predictability, tracked across all six teams. Status moved on a transparent cadence so product and the executive team worked from the same picture. On-time delivery climbed from ~60% to 95%+ once health was measured and acted on.
Release & accountability
Release governance and deployment discipline with named owners at the team and EM level. Incident reviews were blameless and produced follow-through that actually closed, so the same failure did not return.
Operational maturity
Observability, alerting hygiene, and clear on-call rotations turned reliability from firefighting into a system. Runbooks, cross-training, and rotated ownership pulled critical paths out of individual heads.
The outcomes
Measured.
What it taught me
- Reliable beats remarkable. An org that ships what it commits earns more trust than one that occasionally pulls off miracles. Consistency is the deliverable.
- AI sharpens discipline, it doesn’t make the call. The velocity gain held only because review, security, and architectural ownership stayed intact.
- Reliability is a leadership responsibility, not a rotation someone else covers. Making it uneventful is the point — and that comes from systems, not late nights.
- Scale systems before process. Doubling the team without execution systems would have amplified the dysfunction. The systems came first; the headcount followed safely.
- Spread the knowledge so no one is a single point of failure. Written handoffs, cross-training, and rotated ownership turn fragile individual knowledge into durable team capability.
- Execution is a communication problem first. Most delivery misses trace back to unclear ownership and scope, not to engineers who cannot build.