Full source code ownership. Air-gapped deployment. Autonomous agents that act — not chatbots that answer. Built for agencies that cannot compromise on sovereignty or security.
Government agencies don't need another AI pilot. They need a production-grade platform that operates inside their perimeter, under their control, with zero dependency on any vendor's cloud. ibl.ai delivers exactly that — a complete AI platform, source code included, deployed on your infrastructure.
With 1.6M+ users across 400+ organizations — including NVIDIA's global AI training platform — ibl.ai is not a prototype. It is a proven system built for scale, security, and operational continuity. Federal, state, and local agencies receive the full codebase, not a SaaS subscription with hidden dependencies.
From autonomous agents that monitor compliance and coordinate interagency workflows, to AI-powered training and credentialing for the public sector workforce, ibl.ai addresses the full spectrum of government AI needs — with complete audit trails, role-based access, and no telemetry leaving your perimeter.
1.6M+ users across 400+ organizations including NVIDIA, Kaplan, and Syracuse University. This is not a proof of concept — it is a platform with a live track record at enterprise and institutional scale.
Agencies receive the complete codebase at delivery. No black-box SaaS. No runtime dependencies on ibl.ai infrastructure. Your team owns, audits, and modifies every line.
Runs entirely on your infrastructure — on-premises, classified networks, or government cloud. Zero external calls. Zero telemetry. Data never leaves your perimeter under any condition.
Works with Claude, GPT-4, Gemini, Llama, Mistral, or any custom or classified model. Swap models without rebuilding. No lock-in to any AI provider's ecosystem.
If you never call ibl.ai again after delivery, the platform keeps running. No license keys. No usage-based billing. No forced upgrades. Operational continuity is guaranteed by design.
Every capability is accessible via RESTful APIs. Integrates with existing government systems, legacy databases, identity providers, and interagency platforms through MCP connectors.
Autonomous agents continuously scan agency documents, policy updates, and regulatory databases. They flag non-compliant records, generate remediation reports, and route findings to responsible officials — without human initiation.
Agents monitor shared data repositories across agencies, detect coordination gaps, trigger notifications, and draft inter-departmental communications. They execute multi-step workflows spanning multiple systems and stakeholders autonomously.
MentorAI agents deliver personalized training paths to government employees, track completion, assess competency, and issue verifiable credentials — all within the agency's secure environment with no external data exposure.
Agents ingest FOIA requests, search classified and unclassified document repositories, apply redaction rules, generate response packages, and log every action for legal review — operating within defined authorization boundaries.
Agents monitor procurement pipelines, analyze vendor submissions against requirements, flag anomalies, cross-reference past contract performance data, and surface risk indicators to contracting officers in real time.
Agents ingest feeds from sensors, reports, and databases during incidents. They correlate data, generate situation reports, recommend resource allocations, and push alerts to command structures — executing continuously without manual prompting.
Traditional chatbots answer questions. Autonomous AI agents take action, reason over context, and deliver measurable outcomes.
ibl.ai deploys autonomous AI agents that go beyond simple Q&A. Our agents reason, plan, and execute multi-step workflows while you retain full code ownership and infrastructure control.
The platform runs entirely on agency-controlled infrastructure with zero external network dependencies at runtime. Operates on classified networks, on-premises data centers, or government-designated cloud environments with no calls to ibl.ai or any third-party service.
No usage data, model inputs, outputs, or behavioral telemetry ever leaves the agency's perimeter. There are no analytics callbacks, no crash reporting to external servers, and no hidden data flows of any kind.
Every agent action, API call, data access, user interaction, and system event is logged with full provenance and timestamps. Audit logs are stored locally, queryable by security officers, and exportable for FISMA, IG, and oversight reporting.
Multi-tenant architecture with granular RBAC enforces strict separation between agencies, departments, clearance levels, and user roles. Access policies are configurable and enforceable at the data, agent, and API layer.
Agencies receive the complete source code, enabling internal security teams, IGs, and third-party assessors to audit every component. No black-box binaries. No obfuscated dependencies. Full transparency at every layer of the stack.
Integrates with existing government identity infrastructure including CAC/PIV authentication, SAML 2.0, OAuth 2.0, and Active Directory Federation Services — ensuring access control aligns with existing agency security architecture.
With full source code in hand, agency security teams, IGs, and authorized third-party assessors can conduct comprehensive code reviews, penetration testing, and vulnerability assessments without any dependency on ibl.ai's cooperation or timeline.
Agencies can extend, customize, or modify any component of the platform to meet mission-specific requirements, emerging policy mandates, or classified operational needs — without submitting change requests to a vendor.
The codebase can be deployed to any infrastructure the agency controls — on-premises, GovCloud, classified environments, or future infrastructure — with no license validation, no runtime checks, and no expiration.
If ibl.ai ceases operations, changes pricing, or is acquired, the agency's platform continues operating without interruption. Source code ownership eliminates the single point of failure that SaaS contracts create.
Source code delivery supports government procurement requirements for software escrow, open architecture mandates, and data rights clauses under FAR/DFARS. Agencies retain full data rights and technical data rights from day one.
ibl.ai delivers the complete platform source code and works with agency technical teams to deploy on designated infrastructure — on-premises, GovCloud, or air-gapped environments. Includes identity integration, RBAC configuration, and baseline security hardening aligned to agency requirements.
ibl.ai engineers work alongside agency teams to configure autonomous agents, connect to internal data sources via MCP, build mission-specific workflows, and integrate with existing government systems. Agents are trained on agency-specific policies, procedures, and data — all within the secure perimeter.
The agency's team takes the platform to production with full operational independence. No ongoing dependency on ibl.ai for the system to run. Internal teams manage, extend, and operate the platform using the delivered source code, documentation, and knowledge transfer completed during joint development.
Autonomous compliance monitoring agents replace manual document review cycles, reducing FTE hours dedicated to routine regulatory audits and reporting across federal and state agencies.
AI agents processing FOIA requests reduce average response preparation time from 30+ days to under 5 days, reducing backlog and improving public transparency obligations.
AI-powered personalized training at scale eliminates per-seat licensing costs for external training platforms and reduces instructor-led training hours required for mandatory government workforce development programs.
Autonomous procurement intelligence agents accelerate vendor evaluation, contract risk analysis, and acquisition documentation — compressing review cycles and reducing contracting officer workload.
Full source code ownership eliminates future migration costs, re-procurement cycles, and operational disruption risk associated with SaaS vendor changes, acquisitions, or service discontinuation.
The Federal Information Security Modernization Act requires agencies to implement risk management frameworks for all federal information systems, including AI platforms processing government data.
Air-gapped deployment, complete audit trails, full source code for independent assessment, and RBAC controls directly support FISMA compliance. The platform's architecture aligns with NIST SP 800-53 control families including AC, AU, CM, and SI.
Federal Risk and Authorization Management Program establishes cloud security requirements for systems operating in federal environments. Agencies must evaluate AI platforms against FedRAMP authorization requirements.
On-premises and air-gapped deployment options allow agencies to operate the platform entirely outside FedRAMP-scoped cloud boundaries. For GovCloud deployments, the platform's architecture supports agency-led ATO processes with full documentation and source code transparency.
Federal Acquisition Regulations and Defense Federal Acquisition Regulation Supplement govern data rights, technical data rights, and software rights in government contracts — critical for AI platform procurement.
Full source code delivery with unlimited rights ensures agencies meet FAR 52.227 and DFARS 252.227 requirements. No proprietary black-box components. Agencies retain complete technical data rights from contract execution.
Federal AI governance requirements mandate transparency, auditability, safety testing, and human oversight for AI systems deployed in government contexts.
Complete audit trails of every agent action, model-agnostic architecture enabling approved model substitution, full source code for independent review, and configurable human-in-the-loop controls directly address federal AI governance mandates.
See how ibl.ai deploys autonomous AI agents you own and control — on your infrastructure, integrated with your systems.