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AI Library Raises $560K Pre-Seed to Scale AI-Native Software Delivery for Enterprises

May 2026 | AI & Enterprise Software | Startup Funding News –Outcome-driven AI startup AI Library has raised $560,000 in a pre-seed funding round at a valuation cap of $7.5 million, backed by a group of prominent investors.

The funding will be used to accelerate product development, research and development (R&D), and market expansion, as the company builds AI-driven systems for enterprise software delivery.

Building AI Autopilots for Software Delivery

Founded in November 2023 by Arani Chaudhuri, AI Library is focused on creating AI-native “autopilots” that automate the software delivery lifecycle.

The platform enables enterprises to:

  • Automate development workflows using AI agents
  • Improve speed from development to production
  • Reduce operational costs
  • Deliver measurable business outcomes across functions such as finance, operations, sales, and customer support

The company’s core proposition is to help organisations move beyond basic AI adoption to real, outcome-driven execution.

AI-Native Infrastructure for Enterprise Workflows

A key component of the platform is AI Library MCP, a unified infrastructure layer designed to streamline how AI agents operate within enterprise environments.

MCP functions as a central system that:

  • Provides structured access to tools, data, and workflows
  • Eliminates fragmented integrations across APIs, storage, and retrieval systems
  • Reduces redundant processes and noisy inputs
  • Improves efficiency and reliability of AI deployments

This approach aims to simplify enterprise AI adoption by creating a single, integrated environment for agent-driven workflows.

Delivering Faster, Scalable Software Systems

According to the company, its platform enables:

  • Faster time-to-production
  • Continuous system improvement through feedback loops
  • Scalable delivery models that function like a “flywheel”

By combining AI automation with human oversight, AI Library is positioning itself as a solution for enterprises seeking efficient, scalable, and cost-effective software delivery.

Enterprise Adoption and Use Cases

The startup has already been deployed across several organisations, including:

  • Tally Solutions
  • Times Group
  • Burger Singh
  • DeKoder

These deployments highlight growing interest in AI-led automation of enterprise workflows.

Market Context: Rise of Agentic AI in Enterprises

AI Library’s growth comes amid a broader shift toward:

  • Agent-driven automation
  • Outcome-based enterprise software
  • AI systems that execute tasks rather than assist

As businesses look to improve efficiency and reduce costs, demand for AI-native infrastructure and automation platforms is rising rapidly.

Conclusion

With fresh funding and a focus on outcome-based execution, AI Library is building a new category in enterprise software—one where AI agents drive delivery from end to end.

As enterprises move toward automation at scale, platforms like AI Library could play a key role in shaping the future of AI-powered software development and operations.

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