The memory infrastructure
for agents you can trust

Ashnode sits between your vector store and your LLM — the layer plain RAG is missing.

RAG retrieves documents. Ashnode returns what is true.

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Your LLM & Agent
GPT-4 · Claude · Gemini · any model
clean · current · auditable context
Memory Layer
ASHNODE
Current facts only Conflicts surfaced Full provenance Reproducible
raw vectors & documents
Your Vector Store
Pinecone · Weaviate · pgvector · Qdrant · any
~3ms
p95 recall latency
14 / 14
benchmark sections passing
0
determinism failures
0 vs 3
errors vs plain RAG
Why Now

Agentic AI just crossed
from demos into production

For years, AI agents were single-session, stateless, and short-lived. That era is ending. Teams are now running agents for months — handling evolving patient records, live contract data, and continuously updated policies. The memory tooling has not kept up. Plain vector RAG was designed for document retrieval, not for agents that need to know what is currently true. The failure modes that didn't exist 12 months ago are now causing real production incidents. Ashnode is built for this moment.

2022–23
Single-session agents Agents were stateless. Memory didn't matter — every session started fresh.
2024
Long-lived agents emerge Agents start running across sessions. Teams duct-tape vector DBs as memory. Stale data becomes a problem.
2025
Production incidents begin Agents act on superseded facts. Contradictions silently pass to the LLM. Audit trails are nonexistent. Debugging is guesswork.
Now
Ashnode fills the gap A purpose-built memory layer. Current facts, conflict detection, full provenance, reproducible recall — as a single API call.
The Consequence Chain
Your agent
Needs to act
on evolving facts
→
Queries memory
Plain RAG
all versions returned
no current signal
→
LLM receives
Contradictory facts
stale · unverified
no audit trail
→
Outcome
Wrong decision
liability · debugging
no reproducibility
Your agent
Needs to act
on evolving facts
→
Memory Layer
Ashnode
current · verified
auditable
→
LLM receives
Verified context
current only · conflicts
flagged · provenance
→
Outcome
Correct decision
reproducible · auditable
defensible

RAG retrieves documents. Ashnode returns what is true.
The difference is every wrong answer your agent has ever given.

The Problem

RAG gives you documents.
Not what is currently true.

Vector similarity finds what is relevant. It has no concept of what is current, what has been superseded, or whether two retrieved facts contradict each other. That gap is where agents fail.

What this looks like in production

A voice agent quotes a drug dosage updated 3 weeks prior. A legal agent cites a contract value that was amended. A clinical copilot surfaces an allergy note and its contradicting prescription — both. Your RAG retrieved correctly. Your agent answered wrong. And you have no audit trail to explain why.

Plain Vector RAG
  • Returns all versions of a fact — old and new — equally ranked
  • No concept of what is current or what has been replaced
  • Contradictions silently passed to the LLM
  • No audit trail — impossible to explain why an agent answered as it did
  • Same query can return different context on different runs
  • No signal when retrieval is capped or incomplete
Ashnode
  • Only current facts returned — old versions automatically retired
  • Full history available on demand when trend queries need it
  • Contradictions detected and surfaced before the LLM sees them
  • Full provenance per item — source, timestamp, freshness, sequence
  • Same query, same policy, same data — identical response, always
  • Explicit flag set when a policy cap is hit — no silent gaps
Core Capabilities

Every failure mode of long-lived
agents, addressed by design

These are not configuration options bolted onto a vector store. They are first-class primitives in the Ashnode memory architecture.

Facts Stay Current
New fact on the same topic? The old version is automatically retired. Your agent only sees what is true right now — with the full history available for audit if you need it.
Conflicts Caught Before the LLM
A background process continuously scans for facts that disagree. When conflicts exist, they are attached to the response — never silently passed to the model.
Every Decision Auditable
Source, timestamp, freshness score, and replacement status on every fact, every time. Know exactly what your agent knew when it made a decision — and reproduce it on demand.
Facts Age Naturally
Set a half-life per fact. Recent updates outweigh older ones automatically. No manual curation. Freshness is a first-class signal in every recall.
Same Question, Same Answer
Same query, same policy, same data snapshot = identical response. Always. Zero non-determinism across all benchmark runs. Decisions are reproducible and defensible.
No Silent Gaps
Hard caps on what recall returns, defined by policy. When a cap is hit, an explicit flag is set. Your agent always knows if its context was limited — no guessing.
Who It's For

Any agent running in production
longer than a single session

If your agent accumulates facts over time and those facts evolve, you need Ashnode. The stakes are highest in regulated domains — but the problem is universal.

Voice AI
Always current, never stale
Policy updates, pricing changes, product details — voice agents retrieve only what is currently true. ~3ms p95 latency keeps real-time conversations snappy.
Healthcare
Auditable clinical memory
Medication doses, diagnoses, allergies — superseded automatically, contradictions flagged, full audit trail for every agent decision. Required in regulated environments.
Legal
Contract lifecycle tracking
MSA amendments, governing law changes, litigation holds — agents always see the current contract state, not all historical versions simultaneously.
Compliance & Finance
Reproducible decisions
Same query, same policy, same store revision = identical output. Every decision is inspectable, reproducible, and defensible to regulators.
Early Access

Request access to
the private beta

Ashnode is in private beta. We're working with a small number of teams building long-lived agents in production. Access is granted under NDA.

We respond within 48 hours. Access is granted under NDA.

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