One intelligent entry point for every AI interaction — across every channel, every user, every agent in your organization.
Modern enterprises don't interact with AI through a single interface. Users are on Slack, Teams, WhatsApp, mobile apps, web portals, and email — all at once. The ibl.ai AI Gateway is the unified infrastructure layer that receives every inbound message, authenticates the sender, resolves context, and routes the request to the right agent.
This isn't a chatbot widget. It's the message routing backbone of your entire AI operating environment — the equivalent of an API gateway, but purpose-built for agentic AI workloads. Every channel is normalized into a single message format, every request is logged, and every response is traceable.
With load balancing, rate limiting, credential enforcement, and real-time audit trails built in, the ibl.ai Gateway gives platform teams the control and visibility they need to run AI at production scale — without building that infrastructure themselves.
Without a dedicated AI gateway, organizations end up with a fragmented mess of point integrations — one bot wired directly to Slack, another embedded in a web app, a third triggered by email. Each has its own auth logic, its own logging (or none), and its own failure modes. There's no unified view of who is talking to what, no way to enforce consistent rate limits or policies, and no single place to update routing logic when agents change.
This fragmentation compounds fast. As AI usage scales across departments and channels, the lack of a central routing layer creates security gaps, inconsistent user experiences, runaway API costs, and debugging nightmares. Platform teams spend more time maintaining glue code than building value. The AI Gateway is the infrastructure primitive that eliminates this entirely — a single, policy-enforced, observable entry point for all AI traffic in your organization.
Teams build separate, one-off integrations for each channel — Slack bot here, web widget there, email handler somewhere else — with no shared logic, no shared auth, and no shared observability.
Duplicated engineering effort, inconsistent behavior across channels, and no unified audit trail for compliance or debugging.Without a gateway layer, each integration must independently implement identity verification, role checks, and access policies — leading to gaps, inconsistencies, and security vulnerabilities.
Users can bypass intended restrictions, sensitive agents become accessible to unauthorized roles, and compliance audits fail.Direct integrations with no rate limiting or quota management allow runaway usage — a single misconfigured agent or a spike in traffic can exhaust LLM API budgets in hours.
Unexpected cost overruns, degraded service for all users, and no mechanism to prioritize traffic by user tier or business criticality.Without a central routing layer, there is no single place to see what messages are flowing, which agents are responding, how long responses take, or where failures occur.
Incidents are hard to detect, impossible to diagnose quickly, and compliance teams have no audit log to satisfy regulatory requirements.When routing decisions are hardcoded into individual integrations, changing which agent handles a request requires touching multiple codebases, redeploying multiple services, and hoping nothing breaks.
Slow iteration cycles, high risk of regression, and inability to dynamically shift traffic as agent capabilities evolve.The Gateway receives inbound messages from every supported channel — web, mobile, Slack, Microsoft Teams, WhatsApp, email, and SMS. Each message is normalized into a unified internal format regardless of origin, stripping channel-specific noise and preserving sender context.
Every inbound message is authenticated against your identity provider — SSO, OAuth, API key, or session token. The Gateway resolves the user's identity, tenant, and role before any routing decision is made, ensuring no unauthenticated request reaches an agent.
With identity resolved, the Gateway evaluates access policies — which agents this user can reach, what data scopes are permitted, and whether rate limits or quotas apply. Requests that exceed thresholds are queued, throttled, or rejected with a structured response.
The Gateway consults the routing registry to determine which agent — or agent pipeline — should handle this request. Routing decisions factor in message intent, user context, tenant configuration, agent availability, and load. Traffic can be split, mirrored, or cascaded across agents.
Requests are distributed across available agent instances using configurable load balancing strategies. If an agent instance is unavailable or exceeds latency thresholds, the Gateway automatically reroutes to a healthy instance or fallback agent without user-visible disruption.
Agent responses are formatted for the originating channel and delivered back to the user. Every request-response pair is written to the audit log with full metadata — timestamp, user identity, channel, agent ID, latency, token usage, and outcome — for compliance and observability.
Ingests messages from web, mobile, Slack, Teams, WhatsApp, email, and SMS. Normalizes all formats into a single internal schema so agents receive consistent, structured input regardless of where the user is.
Integrates with SSO, OAuth 2.0, SAML, and API key systems. Resolves user identity and tenant context on every request before routing, with configurable enforcement rules per channel, agent, or user role.
Routing rules are managed centrally and updated without redeployment. Route by intent, user role, tenant, message content, or agent availability. Supports A/B routing, canary deployments, and cascading fallback chains.
Define per-user, per-tenant, and per-agent rate limits. Enforce token budgets and request quotas to control LLM API costs. Prioritize traffic by user tier or business unit with configurable queue strategies.
Distributes traffic across agent instances with round-robin, least-connection, or weighted strategies. Continuously monitors agent health and automatically reroutes away from degraded instances.
Every message, routing decision, and response is logged with complete metadata. Feeds into your SIEM, data warehouse, or ibl.ai's built-in analytics dashboard. Satisfies HIPAA, FERPA, SOX, and FedRAMP audit requirements.
Serves hundreds of organizations from a single Gateway deployment with strict data and routing isolation between tenants. Each tenant's traffic, policies, and logs are fully separated at the infrastructure level.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Channel Coverage | Each channel requires a separate, custom-built integration with its own auth, logic, and maintenance burden. | All channels — web, mobile, Slack, Teams, WhatsApp, email, SMS — connect through one Gateway with a single integration model. |
| Authentication | Auth logic is duplicated or inconsistent across integrations, creating security gaps and compliance risk. | Every request is authenticated centrally at the Gateway before routing, with consistent policy enforcement across all channels. |
| Cost Control | No rate limiting means a single spike or misconfigured agent can exhaust LLM API budgets with no warning. | Per-user, per-tenant, and per-agent rate limits and token quotas prevent runaway costs and enable accurate cost attribution. |
| Observability | No unified view of AI traffic — incidents are invisible until users complain, and debugging requires tracing through multiple disconnected systems. | Every request, routing decision, and response is logged centrally with full metadata, latency, and token usage for real-time monitoring and audit. |
| Routing Flexibility | Routing logic is hardcoded in individual integrations — changing which agent handles a request requires multi-service code changes and redeployments. | Routing rules are managed centrally and updated instantly without redeployment, supporting dynamic, intent-based, and A/B routing strategies. |
| Reliability and Failover | If an agent instance goes down, the connected channel goes dark — there is no automatic failover or load distribution. | Health-aware load balancing automatically reroutes traffic away from degraded instances, maintaining availability without manual intervention. |
| Compliance Readiness | Audit logs are incomplete, inconsistent, or nonexistent — failing HIPAA, FERPA, SOX, and FedRAMP requirements. | Immutable, tamper-evident audit logs on every interaction satisfy regulatory requirements out of the box, exportable to any SIEM. |
Each channel requires a separate, custom-built integration with its own auth, logic, and maintenance burden.
All channels — web, mobile, Slack, Teams, WhatsApp, email, SMS — connect through one Gateway with a single integration model.
Auth logic is duplicated or inconsistent across integrations, creating security gaps and compliance risk.
Every request is authenticated centrally at the Gateway before routing, with consistent policy enforcement across all channels.
No rate limiting means a single spike or misconfigured agent can exhaust LLM API budgets with no warning.
Per-user, per-tenant, and per-agent rate limits and token quotas prevent runaway costs and enable accurate cost attribution.
No unified view of AI traffic — incidents are invisible until users complain, and debugging requires tracing through multiple disconnected systems.
Every request, routing decision, and response is logged centrally with full metadata, latency, and token usage for real-time monitoring and audit.
Routing logic is hardcoded in individual integrations — changing which agent handles a request requires multi-service code changes and redeployments.
Routing rules are managed centrally and updated instantly without redeployment, supporting dynamic, intent-based, and A/B routing strategies.
If an agent instance goes down, the connected channel goes dark — there is no automatic failover or load distribution.
Health-aware load balancing automatically reroutes traffic away from degraded instances, maintaining availability without manual intervention.
Audit logs are incomplete, inconsistent, or nonexistent — failing HIPAA, FERPA, SOX, and FedRAMP requirements.
Immutable, tamper-evident audit logs on every interaction satisfy regulatory requirements out of the box, exportable to any SIEM.
Students get consistent, context-aware responses across every channel without institutions managing separate bot deployments per department.
Platform teams gain full visibility into AI usage across the organization and can enforce spend controls without touching individual integrations.
Healthcare organizations deploy multi-channel AI without exposing PHI to unauthorized agents or channels, with every interaction logged for compliance.
Financial institutions meet regulatory audit requirements automatically while delivering consistent AI-assisted service across all client touchpoints.
Government agencies serve constituents across channels without building separate, siloed AI systems per program or department.
Retailers maintain consistent customer experience during high-volume periods without over-provisioning agent infrastructure year-round.
Startups ship production-grade AI infrastructure without building gateway, auth, or routing systems from scratch, compressing months of engineering into days.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.