How It Works
We run a structured diagnostic across your Vertex AI deployment — auditing models, retrieval pipelines, prompt chains, and infrastructure to identify every root cause of underperformance before touching a line of configuration.
We implement the fixes — architectural changes, prompt optimisation, RAG pipeline improvements, and GCP resource right-sizing — with each change validated against your performance benchmarks as we go.
We benchmark the improved system against the pre-rescue baseline, configure monitoring and alerting so issues surface before users notice, and hand over a runbook so your team can operate it confidently.
What's Included
Systematic audit of your Vertex AI configuration — model versions, endpoint settings, agent playbooks, grounding configuration, and API usage patterns — to locate every source of underperformance.
Identify and eliminate wasteful token usage, misconfigured compute resources, and over-provisioned endpoints — typically reducing GCP AI spend by 30–50% while maintaining or improving quality.
Debug retrieval accuracy issues in Vertex AI Search — diagnosing poor chunking strategies, suboptimal embedding configurations, missing hybrid search tuning, and stale or misindexed documents.
Identify why your Gemini deployment is hallucinating or generating unsafe outputs — analysing grounding gaps, prompt construction issues, and safety filter misconfigurations causing the problem.
Assess whether your current Gemini model choice — Flash, Pro, or custom fine-tuned — is appropriate for your latency, quality, and cost requirements, and recommend changes with evidence.
Configure Cloud Monitoring dashboards, latency alerts, error rate thresholds, and model drift detection so your team knows about issues before they affect end users or escalate costs.
Who It's For
Engineering teams whose Vertex AI agents produce poor output quality, high hallucination rates, or retrieval failures — you need a structured rescue that diagnoses and fixes the root cause.
Teams whose Gemini or Vertex AI spend has ballooned beyond budget without a clear reason — you need a cost audit that identifies the waste and right-sizes your GCP AI infrastructure.
Enterprises whose Gemini-powered products suffer from inconsistent responses, timeout errors, or unpredictable latency — you need a production reliability fix with monitoring baked in.