NDM (Neuroplasticity-based Decision Memory) plugs into the systems your business already runs on — email, Slack, ticketing, shared drives — and self-learns the business logic your senior people apply every day. Every adopted rule is traceable to its source, auditable, and reversible. The same memory is queried by your team directly and navigated by the AI agents acting on their behalf. Instant value across finance, legal, medical, claims, compliance — anywhere business rules sit over a structured ontology. Pair with the neuroplast ontology builder to start from a turnkey domain model on day one.
The hard problem in regulated decision support is not generating text. It's representing accumulated judgment in a way you can interrogate, audit, and update without retraining. NDM solves that, three layers deep.
Every claim is a node. Every relationship a typed edge. Every fact has a valid_at (when it was true in the world) and a tx_at (when the system learned it). Memory has a shape, not just a token count.
New facts enter as proposed. Repeated corroboration promotes them to adopted. Contradictions don't overwrite — they supersede, leaving the audit trail intact.
A periodic consolidation job replays activity, strengthens co-fired patterns (Hebbian), runs PageRank + Louvain to surface centrality, decays stale rules, and prunes evidence-poor edges. The substrate gets sharper over time, not bigger.
Click any stage — or press play — to walk through how a single claim moves through NDM. The audit log preserves every transition. Pruned never means gone.
A passive extractor watches a memo or a Slack thread, proposes a claim, and tags it with the source span. Nothing is acted on yet. NDM can see it; it just can't cite it.
"Cap exposure 20–25 % below request when top-3 concentration exceeds 60 %."
Each mechanism is implemented as a discrete primitive — testable, citable, parameterized against calibration sets. None of it is metaphor.
Patterns that fire together strengthen their connection. The substrate learns "this signal pair predicts this decision" without supervision.
Rules with no recent corroboration fade. Confidence is a function of evidence freshness. The substrate forgets the same way experts do — slowly, in the background.
When a new claim contradicts an old one, the old one is marked superseded — never erased. The audit trail survives the update. You can always rewind.
A periodic job runs PageRank + Louvain across the memory graph, clusters co-fired patterns, and surfaces emergent rules. Sharper, not bigger.
New NDM deployments start in a high-plasticity phase — rules form fast. Once enough are corroborated, plasticity dampens. Mature memories don't get overwritten by noise.
Edges with thin or stale evidence are dropped, freeing the substrate from low-signal connections. The graph stays interpretable as it grows.
BM25 + dense vectors + graph centrality. Each query lights a path through the substrate; NDM cites every node it walked.
Every claim has two timestamps: when it was true in the world (valid_at) and when the system learned it (tx_at). Audit questions like "what did NDM know on date X" become a single query.
Every action that touches memory goes through this stack. Each layer is pluggable; each layer is observable. The audit log threads through all of them — every read, every write, every retraction.
Each existing pattern handles part of the problem. None handle accumulated judgment in regulated environments. That gap is what NDM closes.
| RAG / vector store | Fine-tuning | Context engineering | NDM | |
|---|---|---|---|---|
| Adds new knowledge without retraining | Yes | No | Yes | Yes |
| Distinguishes proposed vs adopted | No | No | No | Yes — first-class |
| Bitemporal — answers "what did it know on date X" | No | No | No | Yes |
| Audit log of every memory change | Partial | No | No | Yes — by construction |
| Reversibility — supersede > delete | No | No | No | Yes |
| Captures judgment, not just facts | No | Implicit | No | Yes — patterns first-class |
| Cost to add a corrected rule | Re-embed, re-index | Re-train | Edit prompt | One supersede transaction |
A thirty-minute scoping call. We sketch what NDM looks like running on one of your regulated decision flows — credit, claims, radiology, compliance, vendor risk, or your call.