Turn AI answers into customer trust<br>Skip to content<br>Grounded memory for production AI<br>Turn AI answers into<br>customer trust.<br>Give your existing AI a memory it can defend: persistent context, authorized evidence and a fail-closed boundary before unsupported claims reach customers.<br>Prove it with 1,000 queries ↗See the boundary in action<br>R$ 99 once · 1,000 queries · no subscription · reader BYOK

grounding.query<br>01query.accepted12 ms<br>02memory.retrieved8 candidates<br>03policy.authorized3 evidence<br>04claims.validated6 / 6<br>DELIVERprovenance_complete: true

Evidence-bound Namespace-isolated Reader-independent Fail-closed delivery

18/18 deterministic controls passedFrozen failure-mode manifest<br>45/45 unsafe deliveries containedReader replay · zero escapes<br>2K evidence-token budgetFrozen evaluation protocol<br>~311 ms warm query meanMeasured hosted profile

Final security suite<br>The reader can fail.<br>The boundary cannot.

Reader success varies by task. Bridge containment is measured separately.<br>Safety enforcement Answer quality Feedback transfer

050100%<br>Failure modes passed18 / 18<br>100%

Unsafe output contained45 / 45<br>100%

050100%<br>Matrix safe delivery26 / 86<br>30.2%

Transfer baseline8 / 258<br>3.1%

Transfer after feedback56 / 258<br>21.7%

Language modes safe26 / 45<br>57.8%

050100%<br>Semantic activation175 / 258<br>67.8%

Rank 1 after feedback258 / 258<br>100%

Frozen evaluation · 2K evidence-token budget · reader outcomes are integrator-dependent

Completed retrieval result<br>The right evidence.<br>Not the whole history.

On 979 frozen support questions, ASM-CM + Bridge 8.1 delivered 93.6% Recall@5 and a 66.5% diagnostic answer score while sending about 1.09K input tokens per question to the reader.<br>Read the measured results →

Frozen support benchmarkMultiWOZ<br>ReaderQwen3 14B · 979 questions

System<br>Recall@5<br>Diagnostic answer score<br>Reader input / question<br>ASM-CM + Bridge 93.6%<br>66.5%<br>1.09K<br>Vector RAG 70%<br>49.7%<br>2.04K<br>BM25 75.9%<br>56.8%<br>2.20K

Protocol-scoped results. Quality depends on workload, reader and retrieval profile; the Bridge is measured separately as the delivery boundary.

What the system protects<br>22 failure classes.<br>One threat model.

The matrix organizes the documented risks across feedback, retrieval, authorization, delivery and lifecycle. It is a threat taxonomy—not a fabricated 22-test denominator.<br>Measured evidence: 18/18 deterministic controls passed, and 45/45 unsafe deliveries were contained across nine bounded language scenarios. Reader quality remains a separate metric.

Feedback integrity<br>Corrections improve retrieval without silently becoming source truth.<br>Incorrect feedback and revocationCovered<br>Malicious feedbackCovered<br>Stale corrections and temporal supersessionCovered<br>Accumulated and contradictory feedbackCovered<br>Feedback-ledger scale and false positivesCovered<br>Self-referential validation loopsCovered

Semantic retrieval<br>The system distinguishes retrieval failures from grounding failures.<br>False semantic activationCovered<br>Legitimate ambiguityCovered<br>Cross-language feedback transferCovered<br>Ellipsis and indirect referencesCovered<br>Missing correct retrieval candidateCovered<br>Transfer to previously unseen paraphrasesCovered<br>Repeated failures across retrievers and context budgetsCovered

Grounded delivery<br>Retrieved evidence must actually support the claims being delivered.<br>Correct evidence followed by an incorrect answerCovered<br>Valid citations covering unsupported claimsCovered<br>Partially grounded answersCovered<br>Conflicting authorized evidenceCovered<br>Grounded-delivery improvement after validated feedbackCovered

Isolation and lifecycle<br>Authority, deletion and operational behavior remain explicit boundaries.<br>Cross-namespace leakageCovered<br>Memory deletion, feedback invalidation and snapshot reconstructionCovered<br>Prompt injection stored inside memoryCovered<br>Token usage, latency, API cost and isolation behaviorCovered

The grounding layer<br>Retrieval is useful.<br>Evidence is defensible.<br>AletheionAGI turns memory retrieval into a controlled delivery path. Your reader may change. The evidence contract does not.

01<br>Write<br>Canonical events stay under your authority.

02<br>Retrieve<br>ASM-CM, BM25 and vector retrieval work as one route.

03<br>Authorize<br>Namespace and labels constrain every candidate.

04<br>Ground<br>Unsupported output is blocked before delivery.

A clean boundary<br>Bring any reader.<br>Keep one source of truth.<br>The reader is an integration choice. AletheionAGI owns the harder boundary: canonical memory, retrieval provenance, policy enforcement and grounding.<br>Discuss the architecture →<br>YOUR STACKREADER<br>ALETHEIONAGI GROUNDING BRIDGEAuthorized evidence<br>Claim validation<br>Feedback ledger

CANONICAL MEMORYYOUR DATA

Built for buyer-facing systems<br>Useful memory.<br>Controlled consequences.

Use AletheionAGI when remembered context can improve the experience—and an invented, stale or cross-account claim can damage it.

01Customer support<br>Keep account history useful without letting stale or unauthorized memory shape the answer.<br>Fewer unsupported...