A terminal-native and IDE-integrated autonomous software engineering platform designed for classified, sensitive, and mission-critical development environments. AirgapAI Code delivers state-of-the-art agentic coding inside your secure perimeter with zero third-party cloud code exposure.
AirgapAI Code is engineered to deliver measurable software velocity gains while preserving strict security and compliance controls across sensitive software portfolios.
| Metric | Operational Impact |
|---|---|
| Data Exposure Risk | Zero third-party API dependency for source code analysis when deployed inside approved infrastructure. |
| Developer Productivity | 40-70% acceleration potential for feature delivery based on macro commercial agentic coding baselines. |
| Code Rework | Shift quality left with autonomous analysis and pre-commit validation to reduce remediation effort. |
| Security Scanning | Autonomous code risk analysis can occur against sensitive repositories without external telemetry leakage. |
| Infrastructure Alignment | Designed for on-prem, secure enclave, and regulated private cloud model hosting with bring-your-own-model flexibility. |
| Workforce Coverage | Supports terminal, IDE, virtual desktop, and browser workflows for broad engineering adoption. |
A full-stack autonomous software engineering agent that works across CLI, IDEs, and secure desktop/browser environments.
Full agentic execution directly in secure shell environments.
Inline diffs, architectural planning, and workspace context.
Interactive coding and review support across IDE family tools.
Thin-client friendly browser workflow for virtual desktop fleets.
Purpose-built autonomous capabilities for secure software modernization, compliance-aware delivery, and mission-critical development throughput.
Plans and executes multi-step engineering tasks from natural language objectives, including implementation, testing, and validation.
Semantic vulnerability reasoning with optional auto-remediation workflows and human approval checkpoints.
Fits existing Git-based workflows with support for secure, on-prem repository and pipeline ecosystems.
Spawns specialized agents for architecture, security, and QA tasks in parallel workstreams.
Connects AI workflows to on-prem systems such as Jira, internal docs, and secure messaging platforms.
Policy-driven instruction frameworks and reusable command templates standardize engineering behavior enterprise-wide.
AirgapAI Code operates through a deterministic agentic loop with human-in-the-loop controls for sensitive operations and governance boundaries.
Heavy reasoning workloads run in centralized secure infrastructure, while developer endpoints focus on orchestration and local tooling execution.
This approach supports high model intelligence without requiring specialized endpoint hardware for every developer seat.
Allow: approved low-risk actions.
Ask: explicit user approval for write or privileged operations.
Deny: policy-blocked actions centrally enforced by administrators.
The platform is designed so organizations can leverage existing approved security boundaries rather than extending sensitive code into external AI clouds.
| Security Feature | Description |
|---|---|
| Data Sovereignty | Code and reasoning can remain in enterprise-controlled environments with zero vendor cloud dependency. |
| No External Training on Customer Code | Deployment model isolates customer repositories from external model training pipelines. |
| Identity Integration | Supports enterprise SSO patterns (SAML/OIDC) and role-based policy enforcement. |
| Auditability | Centralized logs can capture user actions, AI-assisted changes, and adoption signals. |
Supports the dominant enterprise and defense engineering deployment patterns.
| Pattern | Description |
|---|---|
| On-Prem Data Center | Recommended model hosting pattern with endpoint-based agent operation. |
| Virtual Desktop Infrastructure | Thin-client access for secure workforce scale with centralized controls. |
| Disconnected Environments | Supports air-gapped and restricted network enclaves for high-assurance programs. |
| Secure CI/CD Automation | Automates review, triage, and compliance checks inside controlled pipelines. |
The primary value case combines speed, quality, and security while reducing cloud lock-in risk.
| Area | Business Value |
|---|---|
| Velocity | Faster mission capability delivery through autonomous development execution. |
| Cost Avoidance | Alternative to recurring per-seat external cloud AI coding subscriptions. |
| Quality and Security | Earlier defect and vulnerability detection before production deployment. |
| Risk | Mitigation |
|---|---|
| Data Exfiltration | Mitigated via secure infrastructure placement and strict network boundaries. |
| Bad AI Output | Human approval gates plus mandatory test/verification loops for high-risk changes. |
| Toolchain Lock-In | Uses open engineering standards and existing source control ecosystems. |
It brings the autonomous power of modern AI coding agents into security-first environments that cannot compromise on code sovereignty, compliance posture, or operational control.
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