Meet Agent Sherlock.
Real-time AI insights
for autonomous FinOps.

Agent Sherlock is Agentic AI for Cloud FinOps, purpose-built to work exclusively with cloud and AI financial data on top of the AndromedaFinAI Cloud & AI Financial Platform. Unlike a general-purpose AI assistant, Sherlock has no scope outside cloud financial operations: it ingests usage and cost signals from multi and hybrid cloud environments, detects anomalies and waste, and returns prioritised FinOps recommendations. It understands the structures of cloud billing: reserved instances, commitment coverage, tag hierarchies, chargeback allocations, Kubernetes namespaces, Databricks job costs, and GenAI/LLM GPU usage, rather than general-purpose topics. This deep domain focus is what makes Sherlock’s answers actionable rather than generic.

Agent Sherlock reads real-time usage and cost signals from GenAI, LLM inference, and GPU compute workloads running on AWS, Azure, GCP, and private cloud. When spend deviates from expected patterns. For example,, a GPU cluster running at full utilisation without auto-scale limits, Sherlock surfaces the anomaly, identifies which teams or projects are responsible, and generates concrete recommendations such as rightsizing GPU node types, moving non-latency-sensitive LLM jobs to spot or preemptible instances, or flagging untagged AI workloads that are breaking chargeback accuracy. Teams can ask Sherlock questions like “Which GenAI projects are over-budget this month?” or “Why did our LLM inference costs spike last week?” and receive answers backed by live AndromedaFinAI data, with no manual query or report required.

Agent Sherlock works across the full breadth of environments that AndromedaFinAI ingests: AWS, Azure, GCP, private cloud, Kubernetes, Databricks, and AI/LLM workloads, all in a single unified view. It continuously monitors cost and usage signals across every provider, surfaces anomalies and optimisation opportunities wherever they occur, and applies consistent governance policies: budget alerts, tag policy compliance, allocation rules, regardless of which cloud a workload sits on. For FinOps teams managing hybrid estates, Sherlock eliminates the need to context-switch between provider-specific tools; a single question returns a cross-cloud answer with provider-level detail attached.

Agent Sherlock is specifically designed to bridge the gap between technical cloud data and financial language. Finance and FP&A teams can ask Sherlock about budget variances, forecast overruns, and cost-centre allocation without needing to understand cloud billing structures. Sherlock translates usage signals into financial terms: if three GenAI GPU clusters are on track to exceed a project’s quarterly budget by 18%, Sherlock surfaces that as a margin risk with the underlying workload context attached, not as a raw utilisation metric. This means CFO offices can engage with cloud and AI spend data directly through Sherlock, without waiting for an engineering team to prepare a report.

AndromedaFinAI is Aquila Clouds’ Cloud & AI Financial Platform. It ingests, normalises, and governs cloud billing, usage, allocation, and chargeback data across every environment an organisation runs. Agent Sherlock is the agentic AI layer that sits on top: it reads that structured financial data, reasons over it, and surfaces insights and recommendations through natural-language conversation. Sherlock does not store or manage cloud data independently; it relies entirely on AndromedaFinAI’s data foundation, which means the answers it returns are grounded in the same numbers your FinOps dashboards, finance reports, and chargeback workflows use. Think of AndromedaFinAI as the platform and Sherlock as the agent that makes it conversational and autonomous.

Agent Sherlock is designed to answer the full range of questions that FinOps, finance, and engineering teams face around cloud and AI financial operations. Concrete examples include: “Which GenAI projects exceeded their budget this month and by how much?” “Where are we overspending on GPU instances relative to actual model inference demand?” “Which Kubernetes namespaces are over-provisioned and what’s the rightsizing opportunity?” “Which of our Databricks jobs have the worst cost-per-run trend?” “Which customers in our MSP portfolio have the weakest cloud margin this quarter?” “Are our reserved instance commitments covering current usage patterns across AWS and Azure?” Every answer references live AndromedaFinAI data and is scoped to cloud & AI financial operations. Sherlock will not answer questions outside that domain.

Agent Sherlock is well-suited for MSPs and cloud resellers who manage cloud financial outcomes across a portfolio of customers on AndromedaFinAI. Sherlock can surface customer-level anomalies, identify billing irregularities and margin erosion risks, and highlight optimisation opportunities, rightsizing, waste reduction, and commitment coverage that can be taken back to clients as concrete recommendations. Rather than manually reviewing each customer’s spend data, MSP FinOps teams can ask Sherlock cross-portfolio questions like “Which of our customers have the highest idle spend this month?” and receive a ranked, actionable answer across all accounts simultaneously.

Agent Sherlock inherits the enterprise access controls and data governance posture of the AndromedaFinAI Cloud & AI Financial Platform. It operates on a least-privilege, role-based access model, meaning each user or team can only query and receive insights on the cloud financial data they are already authorised to see within AndromedaFinAI. Sherlock does not introduce a separate data layer or bypass existing governance policies, working within the permission boundaries, allocation hierarchies, and tenant isolation that AndromedaFinAI enforces. For enterprise customers and MSPs with strict data separation requirements, this means Sherlock respects the same boundaries as every other interaction with the platform.