ffsdb

production use cases

FFS use cases for high-throughput app and agent workloads.

The true north is not a local demo database. FFS is aimed at running the data path for thousands of apps and hundreds of thousands of agents that need graph traversal, vector retrieval, provenance, and durable write-back in one engine.

Production Use Cases

top-down priority

01 / ERP.ai, Neo Agent, Proto workload substrate

Run the shared data path for thousands of apps and hundreds of thousands of agents: app state, agent memory, tenant context, graph relationships, vector retrieval, provenance, query history, and durable write-back in one engine. Engine path: ffsd query/load commands + Cypher + HNSW + qlog/WAL + snapshots/export/import.

02 / Agent fleet query layer

Serve runtime reads for large agent fleets that constantly ask: what does this app know, which entities are connected, what changed, which source produced it, and what should be retrieved next? Engine path: indexed Cypher reads + relationship expansion + vector candidate lookup + metrics/profile paths.

03 / Operational write-back systems

Power ERP.ai-style workflows where approvals, disputes, procurement, finance, customer records, tool calls, and model outputs continuously read, derive, index, and write back state. Engine path: MVCC read + durable mutation batch + secondary/vector index maintenance + qlog receipt + reopenable commit.

04 / High-throughput app and agent ingest

Load and replay app events, agent traces, entity updates, tool outputs, node files, edge files, and mutation logs without an application sidecar owning correctness. Engine path: whole-file preflight + endpoint checks + manifest loads + checkpointed resume + qlog status.

05 / Graph + vector retrieval with permissions

Retrieve the right context under tenant, app, role, workflow, and graph predicates. The eval harness shows why post-filtering vectors can be fast but wrong under sparse predicates. Engine path: predicate-aware ANN + graph expansion + typed filters + top-k recall checks.

06 / Feature, model, and audit workflows

Keep feature views, sampled neighborhoods, embeddings, model-derived facts, source links, confidence scores, and audit trails next to the operational data they came from. Engine path: DenseF32 feature views + sampled CSR neighborhoods + model-derived writes + source/model provenance edges.

internal proof paths