What
happens
next.
Consequence is a real-time consequence engine. It ingests behavior, decisions, performance and capital as a single stream, models every modeled thing as a living digital twin, and answers — continuously, at industrial scale — the only question that matters.
Most platforms wait for you to ask. We watch the world move, update twins of every actor inside it, and answer before the question forms. The result is a quiet kind of luxury — software that already knows.
Inference predicts the present. Simulation rehearses the future. Twins remember it all. Three subsystems, one shape: layered, event-driven, twin-centric.
Built on Kubernetes-native compute, Kafka as the nervous system, GPU inference at the millisecond, and parallel Monte Carlo at ten thousand pods on demand.
Two atriums. One house.
The ledger
underneath
everything.
A single banking core that ingests balances, rails, risk, policy and treasury positions — and answers, in continuous time and at institutional scale, the only question that matters: what happens next?
Home → wire → settlement in three phone-sized surfaces. Same glass stack, scaled for the hero rail.
Portfolio
$128,430
+2.1k · 24h
Treasury · USD
···· 4921
Avail.
$94.2k
APY
4.12%
Activity
Atlas Mfg
Wire
−$12.4k
NYC Ops
ACH
+$48k
Amount
$12,400.00
To
Atlas Manufacturing LLP
ABA ·····281 · Checking
Memo
INV-2044 · steel shipment
Settled
FedWire · Atlas Manufacturing
−$12,400.00
Ref FW-9C2‑88104
May 2, 2026 · 9:41 AM ET
Entity-shaped records with atomic updates to one document at a time. Each change fans out to multiple replicas; nothing is admitted until enough copies acknowledge. Indexes warm asynchronously so ingestion stays ahead of reads.
Balances live in accounts; movement is packaged as debits and credits that succeed entirely or roll back together. Executors process in parallel yet agree on a single ordering. Confirmations arrive quickly for operations; stronger finality gates exist when policy requires them.
Coordinated work spreads across fleets of ephemeral workers, then collapses into summarized outcomes pushed back into durable state—for stress paths, scenario libraries, and rehearsals beyond what an interactive API can carry.
Projections fuse document writes and ledger movements into hot, queryable entity surfaces. Secondary indexes serve lookups; timestamps and lineage tie every snapshot to what produced it.
Predict. Rehearse.
Remember.
The room sees the same picture updating, not a snapshot from yesterday.
Live presence on shared workspaces, comments anchored to specific records and policy lines, and controlled invites so partners only infer what they are allowed to see. Proposals queue as reviewable diffs before they land in production; attribution rides every change so teams reconcile intent from the trail—not from side e‑mail.
- Sub‑second presence across shared canvases and ledgers
- Threads pinned to objects, balances, and wire instructions
- Reviewer‑gated proposals with shadow comparison to live state
- Exportable attribution log for internal and external audit
Rehearse the future, then act on it.
Monte Carlo for distributions of outcomes, agent-based for emergent behavior, discrete-event for trajectories, RL for policy improvement. Ten thousand pods on demand, results aggregated and emitted back into the bus as new state.
- 10,000-scenario Monte Carlo runs
- Agent-based models with Ray actors
- Tiered priority scheduling on spot
- Outputs feed back into the live system
priority
Every modeled thing, continuously alive.
Twins are not records — they are continuously-updated probabilistic models. Sharded by entity, queryable through a sub-10ms API, historical to the second, with explicit uncertainty so consumers can treat them as distributions, not point estimates.
- 8.6M live twins, sub-second update lag
- Probabilistic representation, not a record
- Sub-10ms median read for hot twins
- Time-travel queries via ClickHouse
Roll‑up for the primary payee lane: beat packs, stem SKUs, featured‑artist lines, and sample clearance accruals—scoped so catalog twins stay shard‑fair while publishing routes stay separable from the mesh.
- Beat / stem twins
- 14.2k
- PRO / publisher paths
- 6
- Guest feature queue
- 23
- Statement tie‑out
- T+4h
| Entity | Streams | Accrual | ± band | Source |
|---|---|---|---|---|
| Cluster ent‑ak‑2044 | 184.2k | $2,214 | ±$118 | DSP mix |
| Cluster ent‑mx‑8891 | 96.8k | $1,089 | ±$74 | Radio + on‑demand |
| Cluster ent‑sg‑3302 | 52.1k | $624 | ±$41 | Catalog |
Twins fuse raw stream events with rate cards · ClickHouse time‑slice for audit
Seven layers,
one breath.
Voice note from the green room; hums sketched in, tempo tapped on a knee
Guide bump hit the group chat; everyone has the same dirty board mix
Choir stacks printed in passes; breaths trimmed, the lift is all manual
Two dates of the tour married on the timeline; one take leads, one shadows
String lift mocked from MIDI; chairs arrive Thursday, bows marked in red
Count-offs staged for the full band pass; lights cue held on bar nine
Last chorus recall printed back into the main session; rides copied by hand
GitOps. Observability. No surprises.
Every cluster change is a Git commit. Every deployment is an Argo CD sync. Every model is a registered version with shadow comparison and an automatic rollback path. Prometheus, Grafana, Loki and Tempo are wired from day one — because retrofitting observability is much harder than building it in.
1import { Engine } from "@consequence/core";2 3const engine = Engine.connect({4 cluster: "prod-ams",5 region: "eu-west",6});7 8// Subscribe to a user twin9const twin = await engine.twins.user("u_18a4...");10 11for await (const update of twin.stream()) {12 if (update.kind === "behavioral.next-action") {13 await engine.simulate.monteCarlo({14 twinId: twin.id,15 scenarios: 10_000,16 horizon: "24h",17 });18 }19}1M → 10M → 100M users.
- 1 primary cluster · 20-40 nodes
- Kafka 6 brokers · sub-100k ev/s
- Behavioral model live, single class
- Mongo at 10 shards
- Kafka 12-24 brokers, repartitioned
- Multi-cluster, regional split begins
- Inference at hundreds of GPU pods
- ClickHouse cluster broadens
- Multi-region active-active
- Edge inference caches
- Federated databases, graph store added
- Dedicated platform team per pillar
No single failure
takes the room.
Circuit breakers in the mesh trip on elevated error rate. Callers fall back to cached predictions or simpler heuristics.
Three-replica, ISR ≥ 2. Partition leaders re-elect. Zero data loss. Cluster operates degraded.
Mongo replica sets, Qdrant cluster, ClickHouse replicated tables, PG streaming replication — all auto-failover.
Shadow comparison detects regression on candidate. Traffic snaps back to incumbent. Owning team paged.
Each region runs a complete stack. Local users continue to be served from local data. Cross-region replication catches up on heal.
Documented runbooks. Bounded recovery time. GitOps-restorable cluster state.
Build on the
consequence.
The engine is currently in private deployment for the HBM & Company music vertical. Partner integrations open Q4. If you are building a vertical that needs a real-time consequence substrate, we would like to hear about it.
Industrial scale,
domestic feel.
“It feels less like a tool and more like a fast, quiet collaborator that already knows what I'm about to do.”
“Tiffany blue, Mondrian bones, Apple restraint. The first time enterprise software has been allowed to be beautiful.”
“HBM & Company built the consequence engine the rest of us were too timid to imagine.”
Step inside the
consequence.
Stack in motion