Monitor billing data from multiple clouds, check usage and emissions, and create a consolidated FinOps report with automated distribution.
It ingests daily billing exports from AWS, GCP, and Azure. It analyzes resource utilisation, idle assets, and cost drivers across clouds. It quantifies carbon impact by workload and delivers a consolidated, stakeholder-ready report.
Orchestrates data collection and analysis across cloud providers.
Ingests daily billing exports from AWS, GCP, and Azure.
Parses and normalizes CSV data into a structured model.
Identifies idle and over-provisioned resources.
Surfaces savings opportunities and right-sizing options across providers.
Quantifies workload-based carbon footprint and emissions.
Generates a consolidated FinOps narrative report.
Automates daily Cloud spend and carbon data collection, analysis, and reporting end-to-end. It transforms fragmented data into a single, auditable FinOps narrative.
A simple 3-step flow that non-technical users can follow.
Fetch daily CSV billing exports from AWS, GCP, and Azure and convert them into a unified data model.
Orchestrates four sub-agents to analyze utilisation, costs, and carbon for each workload.
Parse outputs with the Structured Output Parser and format into a consolidated report, then distribute to stakeholders.
A realistic daily scenario showing concrete task, time, and outcome.
At 06:00 UTC, the AI agent fetches yesterday's billing exports from AWS, GCP, and Azure. It runs utilization and carbon analyses through the four sub-agents, compiles a concise FinOps narrative, and delivers the report to Slack and via email to the CFO. The run completes in under 20 minutes, producing a stakeholder-ready document with clear cost-saving opportunities and carbon metrics.
Roles that rely on timely, accurate FinOps and carbon insights.
Needs a reliable daily digest of multi-cloud spend and waste.
Requires auditable, structured reports for governance.
Wants cost and waste insights to optimize pipelines and deployments.
Demands a clear, workload-based carbon footprint and ROI narrative.
Needs concrete right-sizing suggestions and optimization opportunities.
Benefits from consistent reporting for renewals and negotiations.
Tools wired into the AI agent to enable end-to-end automation.
Fetches daily CSVs from AWS, GCP, and Azure and feeds them into the AI agent.
Orchestrates four sub-agents to perform cost, usage, and carbon analyses.
Performs cost calculations, scenario comparisons, and ROI assessments inside the AI agent.
Executes data transformations and advanced modelling for cost and carbon analysis.
Validates and maps sub-agent outputs into a single reporting schema.
Common scenarios that benefit from end-to-end FinOps and carbon reporting.
Practical answers to common concerns about using the AI agent.
The AI agent is designed to ingest standard CSV billing exports from AWS, GCP, and Azure. It normalises varying field names into a common schema for analysis. If your export uses a different delimiter or a non-CSV format, the agent can be configured to parse it with a small schema adjustment. Any format changes are mapped in the Structured Output Parser to ensure consistent reporting. For non-standard exports, a one-time schema mapping can be provided to preserve continuity in daily runs.
The daily run is designed to run once per day at a chosen time. You can adjust the schedule to align with your business hours or data availability. If a run fails, the agent will retry according to a configured retry policy. Notifications are sent if persistent failures occur so remediation can be performed promptly. Historical runs can be replayed to reproduce results for a given date range.
The AI agent supports AWS, Google Cloud Platform, and Microsoft Azure by default. It fetches daily billing exports from these providers and normalises them into a single view. If additional providers are needed, a lightweight adapter can be added to the ingestion layer. Cross-provider comparisons are performed within the unified data model to ensure consistency across sources.
All data is transmitted using standard encryption in transit and stored with encryption at rest. Access is controlled by least-privilege credentials and role-based access controls. The AI agent operates within a secure workspace with audit logging for all actions. Credentials are stored securely and rotated according to your security policy. Data retention and deletion policies follow your organisation’s governance requirements.
Yes. The report schema can be adapted to match your internal fields and terminology. You can configure distributions to Slack, email, or storage targets, and you can set recipients per report. Custom templates can be created to align with governance and branding standards. Any changes can be tested in isolation before going live in daily runs.
If a required export is missing, the AI agent will flag the issue and attempt a retry. It will continue monitoring for the data to arrive and notify the designated channel if the data remains unavailable. Once data is received, the run will complete the remaining steps and generate the report. A fallback summary can be produced if partial data is available to maintain visibility.
Yes. The agent can be configured to analyse historical data over a selected time window and generate trend insights. You can specify the range (e.g., 7, 14, or 30 days) and the report format for trend visuals. Trend analyses can be included in the same narrative report or delivered as a separate daily/weekly summary. This makes it easier to track progress against budgets and sustainability targets.
Monitor billing data from multiple clouds, check usage and emissions, and create a consolidated FinOps report with automated distribution.
