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What is Blaze Cosmos AI?

Blaze Cosmos AI is FSS’s enterprise-grade, governance-first AI platform that enables banks, payments companies, and fintechs to build, deploy, govern, and scale AI safely and compliantly across their organization.

It is purpose-built for regulated industries, especially Payments & BFSI, where trust, security, and regulatory alignment are non-negotiable.

How to Position Blaze Cosmos AI 

The AI Operating System for Regulated Enterprises
Key positioning statements:
  • An Enterprise AI Management System (AIMS) aligned with ISO/IEC 42001
  • Regulatory-ready AI aligned with RBI Responsible AI (FREE-AI), EU AI Act, SDAIA, ISO 27001, ISO 27701
  • A cloud-agnostic and on-prem deployable platform
  • Built to support AI at scale – ML, GenAI, Agents, Agentic AI, SLMs, LLMs
  • Designed for Payments, Banking, Fraud, Risk, Compliance, and Operations This is not just model hosting – it is AI governance + AI engineering + AI operations, all in one platform

What Problems Cosmos AI Solves?

AI Governance & Regulatory Risk

  • Customers struggle with model risk, explainability, audits, and compliance
  • Cosmos AI provides policy-driven AI governance, full traceability, and audit readiness

Security & Trust Concerns

  • Data leakage, model misuse, prompt injection risks
  • Cosmos AI embeds security, privacy, compliance, and trust-by-design

Scaling AI Beyond PoCs

  • Many clients are stuck at PoC stage
  • Cosmos AI enables production-grade AI at enterprise scale

Fragmented AI Tooling

  • Disconnected notebooks, models, pipelines, and vendors
  • Cosmos AI provides a unified AI lifecycle platform

Core Capabilities

Describe the platform using these four pillars:

AI Governance & Trust Center

  • Model risk management
  • Explainability (XAI)
  • Bias & fairness checks
  • Audit trails & approvals
  • Policy enforcement
  • Regulatory alignment (RBI, EU AI Act, ISO 42001)

AI Engineering Platform

  • ML Studio (training, evaluation, deployment)
  • GenAI Studio (RAG, prompt management, fine-tuning)
  • Agent Studio (agentic workflows & orchestration)
  • Feature Store & Data Pipelines

Enterprise AI and ML Operations (ML/AIOps)

  • Model monitoring & drift detection
  • Performance & cost observability
  • Versioning & rollback
  • SLA & reliability management

Deployment Flexibility

  • Cloud, on-prem, hybrid
  • Works with customer’s existing infrastructure
  • No vendor lock-in

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