Step 01
Signal capture
Revenue-flow map: latency, errors, saturation, and spend hotspots
The first layer captures revenue journeys, AI workloads, and telemetry, so the work starts from business pressure instead of random metrics.
AI Infra / Performance / FinOps
AI-managed infrastructure for revenue-critical teams
Control the systems AI makes expensive
AI-assisted infrastructure advisory for teams that need faster funnels, safer launches, and lower cost per request.
AI cost intelligencePerformance budgetsOwner-ready guardrails
MarketplaceDeliveryFintechSaaS
Speed = revenue
Deloitte × Google researchMilliseconds make millions.
A 0.1-second improvement in mobile site speed has a direct, measurable effect on conversion, average order value, and session depth. In the AI era, the same logic expands: latency, inference cost, and reliability become one business metric.
- Retail8.4%conversionAverage order value grew by 9.2% from the same 0.1s mobile speed gain.
- Travel10.1%conversionAverage order value grew by 1.9% — every faster funnel step sells more.
- Luxury8.6%pages per sessionEngagement and browsing depth rise without spending on extra traffic.
- Lead Gen8.3%bounce rateInfo pages hold visitors longer and stop leaking qualified leads.
What to do next
Three moves to turn speed and AI cost into revenue
The report-style Deloitte logic still holds: know the status, set the budget, then build the culture. The modern scope now includes infra, AI workloads, and release gates.
- 01
Know your speed status
Where you stand in absolute numbers and against competitors — across real mobile journeys, APIs, inference paths, and user-facing flows.
- 02
Introduce a performance budget
Hard budgets for LCP, TTFB, JS weight, request SLOs, and unit cost — wired into CI/CD and release gates, not just nice goals.
- 03
Make speed and unit economics first-class KPIs
Speed, reliability, and cost per useful transaction as primary metrics for leadership, product, engineering, marketing, and finance.
Source: Deloitte Ireland × Google — "Milliseconds Make Millions", 2020. Data collected by Fifty-Five over 4 weeks across retail, travel, luxury, and lead-generation brands in Europe and the US. The study isolates speed as the single performance variable.
Market pressure
Performance, scalability, and infra cost are now one conversation
AI workloads add inference cost, governance pressure, and new failure modes. Serious performance consulting now has to manage latency, unit economics, spend anomalies, and launch risk together.
What you receive
AI infrastructure control loop
A compact decision pack for execution, not a long generic report. Each item connects technical signals to money, risk, ownership, and the next operating review.
Signal map
critical journeys, AI workloads, and spend owners in one view
Guardrails
thresholds for latency, errors, saturation, and unit cost
Owners
next actions for engineering, product, finance, and leadership
Step 02
AI cost map
AI/cloud waste map: idle capacity, noisy observability, and inference pressure
Then bottlenecks and waste are ranked by impact, urgency, unit cost, and implementation effort.
Step 03
Control loop
Top-10 actions ranked by impact, risk, urgency, and implementation effort
The final layer turns findings into guardrails, owners, and a backlog for the next sprint, launch review, or FinOps cadence.
Fast lane: starter track, focus, request in a minute
Two starter shapes on the home page; the full catalog is on Pricing.
Next move
Ready to find the infrastructure money leaks?
Start with a fast request. The first conversation should identify the critical journey, the AI/cloud spend pressure, and the one decision you need to make next.