Conversational intelligence infrastructure for 100M+ conversations per day.
Hudhud distributes customer operations across processing, AI execution, messaging, data, recovery, and regional infrastructure - so growth does not depend on a single application instance, provider, or database bottleneck.
Illustrative data, not current production traffic
The system at scale
A multi-panel analytical view of what operational indicators look like when millions of conversations are managed at once.
Illustrative Capacity SimulationConversation Throughput: 24-hour illustrative cycle, 40-95%. AI Processing Load: 68%. Regional Distribution: MEA 46%, EU 27%, NA 17%, APAC 10%. Tenant Load Distribution: T-1 34%, T-2 27%, T-3 21%, T-4 18%.
What "100M conversations/day" actually means
Reference workload model - not measured production dataProvider calls and internal system events often multiply beyond a single message - this model does not assume a specific, unverified amplification ratio.
One global control plane, independent operating cells
Each cell carries its own ingress, event processing, workers, AI execution, data, and recovery. Hover a cell to see its load. Try the simulation to see how the other cells keep running when one degrades.
Hover a cell
An abstract global topology
An architectural illustration of tenant placement, routing, processing, failure isolation, and data boundaries - across multiple regions, as the infrastructure expands.
An architectural illustration of the product's final scope - not an announcement of current physical deployment in every region unless explicitly stated.
From provider event to business effect
Every inbound event is durably accepted before entering the event backbone, where it's partitioned and isolated across conversation streams.
One customer's growth shouldn't become another customer's outage
When one tenant's load spikes sharply, the other tenants stay within their own workload envelope, unaffected.
AI routing doesn't depend on a single provider
The routing layer chooses which provider executes the task, and reroutes automatically when one degrades or hits a quota limit.
Click a provider to preview its state
Hyperscale systems should slow gracefully before they fail chaotically
Instead of collapsing suddenly, the system steps through explicit states, controlling new admission until it recovers.
An illustrative walkthrough of the state sequence, not a live load gauge.
Pick a scenario to see the architecture respond
An architectural illustration, not a live outage simulator. Selecting a scenario updates the visualization to reflect the real operational sequence.
A second view into the system
Latency, queue lag, AI health, provider health, and worker utilization - read together, never in isolation.
Illustrative system viewLatency Distribution: p50 120ms, p95 340ms, p99 780ms. System Health: all illustrative subsystems nominal.
The top tier: global, multi-region operation
For organizations that manage risk across multiple regions, not one place.
Capacity isn't proven by one synthetic benchmark
This is the final product qualification model - a set of real dimensions, not one simplified test.