Investor Pitch

The Problem

AI coding tools are being adopted rapidly, but they still break in real-world workflows because most agents run with fragile, stateless context.

The result is low trust, high cost, and limited production adoption.

• Restart tasks with little or no memory
• Waste tokens rediscovering the same codebase
• Ignore architecture and security rules
• Operate in isolated tools instead of shared systems
• Cannot be governed or audited

The Impact

The problem is not model quality. The problem is missing context infrastructure.

• 80–90% reduction in context acquisition cost
• 40–75% token savings in multi-file engineering tasks

Market Opportunity

AI adoption in software development is already massive, but trust, governance, and context remain unresolved.

• 180M+ developers on GitHub
• 20M+ GitHub Copilot users
• 84% of developers use or plan to use AI tools
• 46% do not trust AI outputs
• Every engineering team adopting AI agents is a potential customer

Why Now

AI agents are entering production without the infrastructure required to support them. The first platform to solve this layer becomes embedded.

• Workflows are forming
• Standards are not defined
• No dominant infrastructure layer exists

Category

Elastra defines a new category: Governed Context Infrastructure for AI Agents.

Model quality improves outputs. Context infrastructure makes agents usable in production.

• Positioned between the model layer, the agent or IDE layer, and company knowledge

The Solution

Elastra is the context and governance layer for AI agents, moving teams from isolated assistants to governed agent systems.

• Semantic indexing of code and documentation
• Persistent shared memory across sessions and agents
• Organization-level rules and policies
• Auditability, usage controls, and billing
• Cross-agent interoperability across 17+ MCP environments

Why Elastra Wins

Existing tools solve fragments. Elastra integrates the full stack and owns the infrastructure every agent depends on.

• Retrieval
• Memory
• Governance
• Sync
• Audit
• Usage control
• Interoperability

Initial Wedge

Start with engineering teams already using AI coding tools, where pain is immediate and ROI is visible quickly.

Expansion

After proving the wedge, Elastra expands into broader enterprise agent workflows anywhere memory, context, and governance matter.

Product Reality

This is not a concept. Elastra already has a production-grade foundation.

• Multi-tenant Go backend
• Async indexing and retrieval system
• CLI for onboarding and configuration
• MCP server
• Web app and dashboards
• Billing and SCU infrastructure
• GitHub and GitLab integrations

Current State

The product is live, functional, and raising to validate demand and scale go-to-market.

• Live product: app.elastra.ai, api.elastra.ai, help.elastra.ai
• Functional capabilities: semantic retrieval, persistent memory, indexing and sync, usage tracking and billing, governance and audit layers
• No customers yet by design

Business Model

Software as a Utility. Customers pay for actual AI workload across memory, search, and inference.

Fundraising

EUR 100k pre-seed to move from product readiness to market traction.

• Go-to-market execution
• Design partners and pilots
• First customers
• Onboarding optimization

Current Challenges

The main risk is commercial execution, not technical feasibility.

• Entering the market with a clear GTM motion
• Converting product depth into demand
• Securing first pilots and customers
• Defining onboarding for a new category