AndromedaFinAI is Aquila Clouds' Cloud and AI Financial Platform, a purpose-built financial control plane that unifies FinOps, BillOps, and AI/LLM/GPU cost intelligence in a single system of record. Unlike generic cloud cost tools that focus narrowly on infrastructure spend across one or two clouds, AndromedaFinAI is designed to handle the full financial complexity of modern enterprises and MSPs: multi-cloud cost allocation and forecasting, usage-based billing and margin analysis for resellers, and dedicated tracking of generative AI and GPU workload costs that generic tools weren't built to handle. The platform also includes Sherlock, a conversational FinOps agent embedded within AndromedaFinAI, not a standalone product, that lets finance and engineering teams query their cloud and AI financial data in natural language and trigger workflows directly from the answers. The result is a financially grounded platform where visibility, optimization, and operational automation exist in one place rather than across five disconnected dashboards.

Generative AI and LLM workloads introduce a cost structure unlike traditional cloud services: GPU clusters, inference endpoints, token-based consumption, and training jobs can spike dramatically and unpredictably. AndromedaFinAI addresses this with dedicated AI/LLM/GPU cost tracking that attributes spend by workload, team, product, or customer, giving finance and FinOps teams the granularity they need for accurate forecasting and chargeback. The platform's ML-based anomaly detection flags unexpected GPU cost spikes in real time, before they compound into large billing surprises at month end. AndromedaFinAI also surfaces AI cost optimization opportunities, such as right-sizing over-provisioned GPU clusters, migrating batch training jobs to spot or preemptible instances, and identifying inefficient LLM inference patterns, all expressed as concrete dollar savings rather than abstract technical metrics. For organizations scaling generative AI initiatives across multiple teams and clouds, AndromedaFinAI provides the financial governance layer that prevents AI cost sprawl.

Yes, this unified view is one of AndromedaFinAI's core design goals. The platform ingests cost and usage data from traditional cloud services, Kubernetes clusters, and AI/LLM/GPU workloads simultaneously, presenting them in a single financial control plane. FinOps teams no longer need to reconcile a separate AI cost report against their standard cloud bill; AndromedaFinAI handles allocation, tagging normalization, and showback/chargeback for both in one workflow. This means a FinOps leader can build a budget that covers both their Kubernetes data platform and their generative AI inference fleet, set unified guardrails, and receive consolidated budget-vs-actual reporting across all workload types. The platform is designed so that as an organization's AI footprint grows, it doesn't require a separate toolchain, it simply extends the same FinOps practices already in place for traditional cloud spend.

For MSPs and cloud resellers, AndromedaFinAI functions as a BillOps engine alongside its FinOps capabilities, covering usage rating, invoice generation, reconciliation, and per-customer margin analysis in one platform. Rather than relying on spreadsheets or fragmented billing exports, MSPs can configure custom rate cards, markups, and bundled service definitions inside AndromedaFinAI and have the platform automate the calculation and reconciliation of customer invoices each billing cycle. Anomaly detection specifically tuned for billing data surfaces unbilled services, misapplied pricing rules, and usage patterns that don't match contracted terms, protecting revenue that would otherwise leak silently. Margin dashboards give account managers and finance leaders visibility into profitability per customer, per cloud, and per service line, including the increasingly important AI and GPU workloads that MSPs are now reselling or managing on behalf of customers. Automated reconciliation workflows in AndromedaFinAI have reduced billing cycle times from two weeks to under four days for MSPs managing 100+ customer accounts.

AndromedaFinAI uses machine learning across three financial intelligence layers. First, anomaly detection continuously monitors cloud and AI workload spend patterns to identify statistically significant deviations. Second, the platform's forecasting models learn from historical usage patterns across multi-cloud, Kubernetes, and AI workloads to produce more accurate forward-looking spend projections than rule-based tools. Third, AI cost optimization surfaces prioritized rightsizing, commitment, and efficiency recommendations based on actual usage data, ranked by financial impact so teams work on what delivers the most savings. All of these insights are accessible through AndromedaFinAI's dashboards and through Sherlock, the conversational agent embedded within the platform, which can explain anomalies, summarize forecast variances, and walk teams through optimization opportunities in plain language.

Sherlock is a conversational FinOps agent that lives inside the AndromedaFinAI platform; it is not a separate product or standalone AI assistant. It has direct access to the platform's full financial data model, which means its answers are grounded in real cost, usage, billing, and anomaly data rather than general knowledge. Teams can ask Sherlock questions like "What's driving our GPU spend increase this month?", "Which customers are approaching their budget limits?", "Show me the top three optimization opportunities by savings potential", or "Summarize last month's billing anomalies and how they were resolved." Beyond answering queries, Sherlock can initiate platform actions from within the conversation, such as drafting an optimization brief, creating a Jira ticket for an anomaly, or flagging a billing discrepancy for escalation through a connected ITSM integration. This makes AndromedaFinAI's financial intelligence actionable in real time, without requiring users to navigate multiple dashboards to find and act on insights about their cloud and AI spend.

AndromedaFinAI is designed to complement and accelerate a mature FinOps practice, not replace the processes a Cloud Center of Excellence has already built. The platform maps directly to the Inform-Optimize-Operate framework: it provides the unified financial data layer many teams currently assemble manually, adds ML-based intelligence on top of that data, and automates recurring operational tasks. For organizations earlier in their FinOps journey, AndromedaFinAI accelerates maturity by establishing consistent cost allocation taxonomies, showback/chargeback practices, and governance policies across multi-cloud and AI workloads from day one. Role-based views ensure that finance leaders, engineering teams, and executive stakeholders each see the financial data relevant to their decisions without requiring every persona to become an expert in cloud billing data structures.

On the cloud cost side, AndromedaFinAI ingests billing and usage data from AWS, Microsoft Azure, and Google Cloud Platform, as well as Kubernetes cost data via cluster-level integrations. For AI and LLM workloads, the platform connects to GPU compute billing from major cloud providers and can attribute costs to specific training jobs, inference endpoints, and generative AI pipelines based on resource tags and workload identifiers. On the BillOps side, AndromedaFinAI integrates with common billing systems, CRMs such as Salesforce, and ITSM tools such as ServiceNow and Jira to push financial alerts, anomaly notifications, and optimization recommendations into existing operational workflows. The platform is built for environments where financial data is inherently fragmented across clouds, teams, and business units, and its core function is to normalize, unify, and make that data actionable for cloud financial management.