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Designing AI-First Systems for Customer Access
Key Takeaways
- Adronite raised $5 million in Series A funding led by Gatemore Capital Management.
- The platform supports 20+ programming languages and offers deterministic, explainable outputs.
- Deployment options include on-premises, private cloud and air-gapped environments for regulated enterprises.
Adronite, a Seattle-based provider of AI-powered codebase intelligence technology, announced on Feb. 19, 2026, the completion of a $5 million Series A funding round led by Gatemore Capital Management. Liad Meidar, Gatemore's managing partner, was appointed chair of Adronite's board of directors.
According to company officials, initial commercial deployments are expected to commence in Q1 2026.
"Adronite's codebase-level intelligence, combined with its security-first deployment model, positions the Company to become foundational infrastructure for some of the world's largest and most complex software environments."
- Liad Meidar
Chair of the Board, Adronite
Table of Contents
What Adronite Does
Enterprise software modernization increasingly depends on AI systems that can analyze entire codebases while maintaining security and governance at scale.
Founded in 2023 by CEO Edward Rothschild, the 15-person company built a platform that aims to solve that challenge. The Adronite platform ingests entire codebases rather than analyzing files or snippets in isolation. The system supports more than 20 programming languages and has been tested on a 2.5 million-line codebase.
The company emphasized its security-first deployment model, offering on-premises, private cloud and air-gapped options designed for regulated environments.
A Look at the Adronite Platform
According to Adronite, its platform offers distinct advantages for enterprise code management.
| Platform Feature | How It Works |
|---|---|
| Full-codebase ingestion | Analyzes entire codebases end-to-end, not isolated files |
| Multi-language support | Supports more than 20 programming languages |
| Deterministic outputs | Produces explainable results for audit trails |
| Flexible deployment | On-premises, private cloud and air-gapped options |
| LLM-agnostic design | Operates independently of specific foundation models |
AI Agents Meet Legacy Codebases
Modern AI coding platforms enable developers to supervise multiple AI agents working across the full software lifecycle, from design through deployment and maintenance. The Model Context Protocol (MCP) provides foundational standardization for how AI applications connect to external tools and data sources.
Organizations increasingly favor hybrid cloud environments supporting AI workloads across on-premises, private-cloud and air-gapped deployments. IT leaders report that driving these strategic shifts are:
- Data security (50%)
- Integration with existing systems (48%)
- Cost savings (44%)
Only 22% of companies are "future ready" with their data infrastructure, according to industry research, while 51% remain stuck with disconnected systems. Enterprises seeking to deploy agentic AI capabilities face similar architectural challenges when integrating autonomous systems into legacy code environments.