JouleDB.
The knowledge substrate for energy-optimized AI.
One energy-aware engine for transactional, analytical, and semantic workloads. Phasor-encoded hyperdimensional computing gives O(1) similarity, a deterministic-first substrate keeps work cheap, and every query carries a Merkle-anchored energy receipt.
- > "energy-aware schedulers"
- ✓ exact-key probe L0 · index 0.000 J
- ✓ HDC bind + bundle L1 · phasor 0.001 J
- ↳ rank by resonance, O(1) L1 · phasor 0.001 J
A query, metered. Similarity in O(1).
The substrate
One engine, every workload
Transactional, analytical, and semantic, with energy as a first-class column.
Hybrid by design
Transactional, analytical, and semantic search in one engine. No bolt-on vector store, no copies shuttled between systems.
O(1) similarity with HDC
Phasor-encoded hyperdimensional computing turns similarity into a constant-time bind-and-bundle, not a brute-force scan over every vector.
Energy receipts, anchored
Every query carries a Merkle-anchored receipt of the joules it spent. Auditable energy, measured, not estimated.
The discipline
Your data layer, on a SWaP-2C budget
A database is pure SWaP-2C: how much hardware it pins, how much power it burns per query, what it costs to run, and how much heat it throws. JouleDB optimizes the whole budget.
Size
One engine, not a stack of specialized stores.
Weight
No copies shuttled between OLTP, OLAP, and vector systems.
Power
O(1) similarity and a deterministic-first path cut joules per query.
Cost
Fewer systems to license, run, and move data between.
Cooling
Less compute per query means less heat to pull out of the rack.