How It Works
We perform a structured diagnostic of your Azure AI deployment — reviewing architecture decisions, prompt design, retrieval configuration, token usage, latency profiles, and security posture to identify every root cause.
We fix the identified problems — restructuring RAG pipelines, rewriting system prompts, optimising chunking strategies, patching security gaps, and implementing cost controls across your deployment.
We run a full evaluation suite post-remediation — benchmarking accuracy, latency, and cost against pre-fix baselines, and configuring monitoring dashboards so you can track performance going forward.
What's Included
Systematically identify why your agent is hallucinating — whether the cause is poor chunking, weak retrieval, insufficient grounding, or model temperature misconfiguration.
Rebuild or reconfigure your retrieval pipeline — fixing chunk sizes, overlap, embedding models, reranking, and index schema to dramatically improve answer accuracy and groundedness.
Audit every LLM call in your deployment for token waste — implementing prompt compression, response caching, model downtiering where appropriate, and usage dashboards to maintain control.
Profile end-to-end latency across retrieval, LLM inference, and post-processing — identifying and eliminating bottlenecks to bring response times within acceptable user-facing thresholds.
Identify and close security vulnerabilities in your Azure AI deployment — including open endpoints, overpermissioned identities, missing content safety filters, and unprotected prompt injection vectors.
Configure Azure Monitor, Application Insights, and Log Analytics for full observability — alerting on hallucination spikes, cost anomalies, latency regressions, and failed agent actions.
Who It's For
Teams who have deployed an Azure AI agent or RAG pipeline that is giving wrong answers, confusing users, or failing to complete tasks reliably in production.
Organisations who approved an Azure OpenAI budget that is being exceeded month after month — token costs that seemed manageable at prototype scale have become unsustainable in production.
Engineering teams handed responsibility for an Azure AI system they didn't build — you need an external team to diagnose what went wrong and fix it properly without starting from scratch.