Workload Catalog

Find your workload. See it proven.

Aggregation and reduction workloads that gate on throughput at correctness — hitting your rate while the result stays exactly correct no matter the arrival order or how many cores feed it. Each archetype below is checked against the silicon-measured parallel-bank engine. Pick the one that's yours.

Representative archetypes — not named customers. Verdicts use measured engine facts (AX7020, 100 MHz, byte-identical across 1/2/4/8 banks).

Recognize yours? Become a founding design partner → — we run it live on silicon behind a conditional LOI you only honor if we hit your bar.

Observability

High-cardinality metric rollup

SURPASS · 32× headroom

Counters, gauges and histogram buckets from thousands of agents, rolled up exactly per series.

25 M/s required → 800 M/s measured, byte-identical · see the proof →

Risk & Fraud

Real-time per-entity counters

SURPASS · 8× headroom

Per-IP / per-user / per-card event counters for fraud and rate-limit decisions, exact under concurrency.

100 M/s required → 800 M/s measured, byte-identical · see the proof →

Ad-Tech

Impression & click aggregation

SURPASS · 4× headroom

Per-campaign impression, click and spend counters aggregated across bid/serve nodes in real time, exact for pacing and capping.

200 M/s required → 800 M/s measured, byte-identical · see the proof →

IoT / Edge

Fleet telemetry rollup

SURPASS · 20× headroom

Per-device telemetry deltas from a large fleet, rolled up exactly despite late, out-of-order delivery — no windowing, no watermarks.

40 M/s required → 800 M/s measured, byte-identical · see the proof →

Product Analytics

Distinct-count cardinality rollup

SURPASS · 40× headroom

Unique users / devices / sessions estimated with sketches and merged across shards and windows — byte-identical regardless of merge order, no serialized reducer.

20 M/s required → 800 M/s measured, byte-identical · see the proof →

Distributed State

Replica state convergence

CONVERGES · 92% less data

Replicas converge to identical state by exchanging deltas — no coordinator, no consensus, a fraction of the bandwidth.

160 B vs 2048 B/tick · converges with no coordinator · see the proof →

Local-First / Mobile

Offline-first client sync

CONVERGES · 92% less data

Field / mobile clients work offline, then converge on reconnect by exchanging deltas — no server merge pass, no last-writer-wins data loss, a fraction of the bandwidth.

160 B vs 2048 B/tick · converges with no coordinator · see the proof →