Your platform is not ready for AI. It will fail in production.
I diagnose and fix the cloud, observability, data, and architecture failures that make AI slow, expensive, and unreliable in production.
Bring your architecture diagram, cloud bill, incident postmortem, or AI workflow.
Proven in production
See what breaks first
This is what usually breaks when AI hits an existing platform. Your platform is already being diagnosed.
High risk
Overall platform risk
Top risks identified
Retrieval is the AI bottleneck
CriticalSlow embedding lookups, no caching, full re-index on every update
LLM gateway is not production-hardened
CriticalNo rate limiting, no prompt caching, no response validation
Vector store is a single point of AI failure
CriticalOne index, no redundancy, index corruption means full rebuild
If this sounds familiar, the platform is already under pressure.
Hard truths about your systems
All insightsThe patterns I see before systems fail.
What I fix
All servicesPlatform Engineering
Fix platforms that break under load
20k to 80k req/s
AI Systems Integration
Make AI work in real production systems
59% to 96% accuracy
Cloud & Infrastructure
Cut cloud costs without reducing capability
£50K/mo removed
SRE & Observability
See what is actually breaking in your system
28 issues caught pre-outage
Data Engineering
Turn slow pipelines into minutes
1hr to 20min pipeline
DevOps & Automation
Ship without causing incidents
Automated compliance
Latest insights
What I see breaking in production across AI governance, platform failures, and cloud infrastructure.
163 restarts in 8.6 hours. The application never failed once.
Three pods restarting every few minutes. No errors in the logs, no OOM kills, no crashes. The application was healthy every single time it was killed. The probe timeout was the fault, and the fix shares a mechanism with the failure.
Read moreClaude and OpenAI went down on the same afternoon. Here is what I changed in my own code.
Two providers degraded for the same 85 minutes. One endpoint and one key is not a plan. Here is the fallback chain I built while waiting.
Read moreFour theories. Four measurements. The obvious fix would have destroyed a million jobs.
Redis at its memory ceiling, login down, millions of keys with no expiry. The obvious fix was to delete the keys with no TTL. I sampled them first. They were not cache. They were a million pieces of unrun work.
Read moreHow every engagement works
Diagnose
Review your architecture, incidents, cloud spend, observability, and AI workflow. Find what is actually breaking.
Fix
Prioritise the changes that remove risk, waste, and instability fastest. Ship the fixes that matter most.
Handover
Leave the team with clearer systems, better visibility, and a next-step plan they can execute without me.
Who you work with
Senna Semakula
I built Atruvo because I kept seeing the same pattern: companies spending months on AI initiatives that failed because the platform underneath could not carry them.
I fix the platform first. Then I make AI work in production. Every engagement is direct, senior, and focused on the part of the system that is actually breaking.
- 10+ years fixing production platforms at scale
- AWS, Azure, GCP, Kubernetes, Kafka, Prometheus
- 80k req/s stabilised. £50K/mo cloud waste removed.
- You work directly with me. No juniors. No handoffs.