confluenceCURRENTLEXICAL RESTATEMENTconflict pair
Deal acceleration playbook and enterprise messaging + security FAQ
Volume discounts: predefined breaks at 100k, 1M, and 5M monthly tokens for hosted volume.
dsid_1214ee9ab5e44de487c800f7a4771d7d
qst_0428
Hosted enterprise_playbook
CANONmonthly token volume discount breakpoints
All truth changes
Retired
100k / 1M / 5M
EXPLICIT_SUPERSESSION
Current
250k / 2M / 10M
Temporal quality
T1
Canon events
2
Verified structured residue
0
Lexical restatements
3
Evidence
Exact spans from the benchmark corpus
Superseded for current grounding
Current
google_driveCURRENTconflict pair
Enterprise deal acceleration playbook + security FAQ (working draft)
predefined breaks at 250k, 2M, and 10M monthly tokens (old doc says 100k/1M/5M — that’s outdated).
dsid_10559147d3014931898864f22c311e47
Residue
Every document that still carries the retired value, including the superseded source itself
confluenceCURRENTLEXICAL RESTATEMENTconflict pair
Deal acceleration playbook and enterprise messaging + security FAQ
Volume discounts: predefined breaks at 100k, 1M, and 5M monthly tokens for hosted volume.
dsid_1214ee9ab5e44de487c800f7a4771d7d
gmailUNCERTAINLEXICAL RESTATEMENTcorpus scan
Scallion Health — pricing recap & invoice estimate
Next steps I propose: I will put together an invoice-style estimate (line-itemed) showing: unit cost scenarios at 100k / 1M / 5M monthly token volumes, recommended model/quantization options, and an expected monthly invoice range plus one-time setup fees. I will include a simple token-breakdown CSV so your engineers can sanity-check the volumes.
dsid_7f2d4f76c82140dbb07285a00f06644f
gmailUNCERTAINLEXICAL RESTATEMENTcorpus scan
Billing units & prompt-cache impact — anchoring on $/1M tokens
- Your replies to the 5 items above -> I’ll send a quick calculator and an estimate showing sample unit economics at 3 volume tiers (100k / 1M / 5M monthly tokens).
dsid_86474759fa7b4699ab78cb1f2b357b43
gmailUNCERTAINNOT AN ASSERTIONcorpus scan
Post-demo: FastHealth buying workflow & latency/cost guardrails
- Cost: the $0.003/token equivalent is within range, but our finance team will need a predictable blended rate (inference + egress) for forecasting. Can you provide sample blended monthly scenarios for 100k, 1M, and 5M tokens?
dsid_a728a23512ae4590a96ddf3536912389
HydraDB queries
Every query that produced this page
MATCH ClaimKey by question_id1 rows · 2.1 ms round trip
MATCH (c:ClaimKey {question_id: $question_id}) RETURN c.id AS id, c.entity_id AS entity_id, c.key AS key, c.predicate AS predicate, c.question_id AS question_idengine: HydraDB OSSquery_id: canon-0d72ef113872-21749-claim_keys_by_questionparameters: {"question_id":"qst_0428"}
MATCH Entity -HAS_CLAIM-> ClaimKey1 rows · 1.1 ms round trip
MATCH (e:Entity)-[:HAS_CLAIM]->(c:ClaimKey {id: $claim_key_id}) RETURN e.id AS id, e.name AS name, e.entity_type AS entity_typeengine: HydraDB OSSquery_id: canon-0d72ef113872-21750-entity_of_claim_keyparameters: {"claim_key_id":180377681553640220}
Claim neighborhood: ClaimKey -HAS_VALUE-> Proposition2 rows · 1.2 ms round trip
MATCH (c:ClaimKey {id: $claim_key_id})-[:HAS_VALUE]->(p:Proposition) RETURN p.id AS id, p.value AS value, p.status AS status ORDER BY p.idengine: HydraDB OSSquery_id: canon-0d72ef113872-21751-claim_neighborhoodparameters: {"claim_key_id":180377681553640220}
Canon events: ClaimKey -HAS_VALUE-> Proposition <-SELECTS- CanonEvent2 rows · 2.2 ms round trip
MATCH (c:ClaimKey {id: $claim_key_id})-[:HAS_VALUE]->(p:Proposition)<-[:SELECTS]-(ev:CanonEvent) OPTIONAL MATCH (newer:CanonEvent)-[:SUPERSEDES]->(ev) RETURN ev.id AS id, ev.claim_key_id AS claim_key_id, ev.selects_proposition_id AS selects_proposition_id, ev.transition AS transition, ev.temporal_quality AS temporal_quality, ev.evidence_doc_id AS evidence_doc_id, ev.occurred_at AS occurred_at, p.id AS proposition_id, newer.id AS superseded_byengine: HydraDB OSSquery_id: canon-0d72ef113872-21752-claim_eventsparameters: {"claim_key_id":180377681553640220}
Supersession lineage: CanonEvent -SUPERSEDES*1..10-> CanonEvent -SELECTS-> Proposition1 rows · 41.7 ms round trip
MATCH (ev:CanonEvent {id: $event_id})-[:SUPERSEDES*1..10]->(old:CanonEvent)-[:SELECTS]->(p:Proposition) RETURN old.id AS id, old.transition AS transition, old.temporal_quality AS temporal_quality, old.evidence_doc_id AS evidence_doc_id, old.occurred_at AS occurred_at, p.id AS proposition_id, p.value AS valueengine: HydraDB OSSquery_id: canon-0d72ef113872-21753-supersession_chainparameters: {"event_id":396636090564782340}
Reverse traversal: Proposition <-ASSERTS- Assertion -IN_ARTIFACT-> Artifact1 rows · 2.1 ms round trip
MATCH (p:Proposition {id: $proposition_id})<-[:ASSERTS]-(a:Assertion)-[:IN_ARTIFACT]->(d:Artifact) RETURN a.id AS id, a.doc_id AS doc_id, a.source_type AS source_type, a.evidence_span AS evidence_span, a.stance AS stance, a.extraction_method AS extraction_method, a.structured AS structured, a.discovery AS discovery, a.asserted_at AS asserted_at, a.source_field AS source_field, a.residue_class AS residue_class, a.proposition_id AS proposition_id, a.artifact_id AS artifact_id, d.title AS title ORDER BY a.doc_idengine: HydraDB OSSquery_id: canon-0d72ef113872-21754-residue_reverse_traversalparameters: {"proposition_id":225555585526387520}
Reverse traversal: Proposition <-ASSERTS- Assertion -IN_ARTIFACT-> Artifact4 rows · 2.6 ms round trip
MATCH (p:Proposition {id: $proposition_id})<-[:ASSERTS]-(a:Assertion)-[:IN_ARTIFACT]->(d:Artifact) RETURN a.id AS id, a.doc_id AS doc_id, a.source_type AS source_type, a.evidence_span AS evidence_span, a.stance AS stance, a.extraction_method AS extraction_method, a.structured AS structured, a.discovery AS discovery, a.asserted_at AS asserted_at, a.source_field AS source_field, a.residue_class AS residue_class, a.proposition_id AS proposition_id, a.artifact_id AS artifact_id, d.title AS title ORDER BY a.doc_idengine: HydraDB OSSquery_id: canon-0d72ef113872-21755-residue_reverse_traversalparameters: {"proposition_id":217236806715677570}
Reverse traversal: Proposition <-ASSERTS- Assertion -IN_ARTIFACT-> Artifact4 rows · 2.4 ms round trip
MATCH (p:Proposition {id: $proposition_id})<-[:ASSERTS]-(a:Assertion)-[:IN_ARTIFACT]->(d:Artifact) RETURN a.id AS id, a.doc_id AS doc_id, a.source_type AS source_type, a.evidence_span AS evidence_span, a.stance AS stance, a.extraction_method AS extraction_method, a.structured AS structured, a.discovery AS discovery, a.asserted_at AS asserted_at, a.source_field AS source_field, a.residue_class AS residue_class, a.proposition_id AS proposition_id, a.artifact_id AS artifact_id, d.title AS title ORDER BY a.doc_idengine: HydraDB OSSquery_id: canon-0d72ef113872-21756-residue_reverse_traversalparameters: {"proposition_id":217236806715677570}