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Evidence-led portfolio

AI-first outcomes you can read on a dashboard

From a 95+ site US hyperlocal network serving millions of readers to ERP and infrastructure programs in Ecuador—these engagements share one bar: production-shaped decisions, measurable ROI, and platforms that stay governable as AI and automation expand.

Scale

US franchise media, Latin American logistics, and multi-tenant platforms worldwide—engineered for load and scrutiny.

Economics

Cloud rightsizing, performance budgets, and automation where unit cost and risk profiles justify it.

Governance

AI and automation with approvals, observability, and rollback paths leadership can defend.

Featured case study

TAPinto: A faster, leaner platform for 95+ local news sites

Real partnership, real results—a platform that costs less, loads faster, and holds up when the business scales.

Michael Shapiro, Founder and CEO, TAPinto
Senirop didn't just rebuild our platform; they transformed how we scale. By optimizing our infrastructure, they dramatically improved page speed and strengthened our SEO. Our publishers can now solely focus on local…
Michael ShapiroFounder and CEO, TAPinto

Key outcomes

  • ~33% lower cloud costs
  • ~45% faster pages
  • 1.6M+ monthly readers
Read the full story

What we optimize across these engagements

  • ROI clarity: initiatives scoped to measurable operating leverage—not experimental sprawl.
  • Governed AI: agents and models wired with approvals, traces, and rollback paths your risk team can defend.
  • Durable velocity: standards and observability so speed compounds instead of creating silent debt.

Next engagement

Your story belongs in the same evidence-led portfolio

If you are navigating AI adoption, platform economics, or a high-stakes rewrite, we start like we did on these cases—with clarity on risk, ROI, and what “done” means in production.

  • Bring a live problem: latency, cost curve, AI governance, or delivery risk—we respond with a concrete read.
  • NDA-friendly deep dives before you expose sensitive architecture or data.
  • Engagement options from fractional CTO to embedded engineering—aligned to evidence, not vanity scope.

What we look for

Teams ready to pair AI leverage with engineering discipline—clear decision makers, realistic horizons, and willingness to measure outcomes in production, not slideware.

Global

US-scale media, Latin American logistics, enterprise platforms worldwide—one delivery bar

AI-ready

Governed automation, observability, and economics baked into the plan

Trusted engineering

Teams across media, logistics, and enterprise platforms—where reliability and clarity matter as much as velocity