Sarvam Arya
A multi-agent orchestration stack that makes autonomous AI systems reliable enough for production-scale enterprise workflows.
Sarvam Arya is an agent orchestration infrastructure built around eight composable primitives: LLM, Agent, MCP, Node, Ledger, Task Graph, Code Interpreter, and Artefact. The platform separates the control plane from the data plane, where code handles iteration, branching, error recovery, and scheduling while the model handles judgment and reasoning. It manages state through an immutable append-only ledger, enabling clean checkpoint restarts without data corruption when processes fail. Arya is designed for complex multi-agent workflows such as structured data extraction from documents, backend automation, and ETL pipelines at enterprise scale.
Use cases
Extracting structured financial metrics and management commentary from company reports
Automating document processing and data extraction pipelines
Orchestrating multi-step backend workflows requiring judgment and reasoning
Building production-grade autonomous agents for regulated industries
Integrations
MCP (Model Context Protocol) compatible tools
Expected outcomes
Higher accuracy in automated data extraction at lower cost compared to agent swarm approaches
Reduced manual intervention in document processing and ETL workflows
Production-grade reliability for autonomous agent systems operating at scale
Reviews
GCC buyer feedback
Reviews are written by GCC buyers and published after moderation.
No reviews yet
Buyer reviews will appear here once published.
Company
Sarvam AI
Bengaluru, India
Founded 2023
Primary Verticals
Financial Services (BFSI)Government & Public SectorRetail & E-commerceHealthcare & Life SciencesEducation
Functions
Data & AnalyticsIT OperationsOperations & Supply ChainFinance & AccountingLegal & Compliance
Industries
Financial Services (BFSI)Government & Public SectorHealthcare & Life SciencesManufacturing
1
Integrations
4
Use cases
Is this your company? Claim & customize your profile
This profile was created using publicly available information.