search_actuarial_embeds

shallow

ai.serff/ca-rate-filings · Verify this server

Pure vector search over per-filing actuarial-memorandum embeddings (`extract_embeds` where `kind='actuarial_memo'`). Each hit is a filing whose memo is semantically closest to your query, with the matching excerpt and lite filing metadata. **Cost**: one query-embedding call + one indexed Postgres lookup. Bounded, cheap, fast. No LLM planning, no LLM composition. **This is the right tool any time the question is *actuarial-shape*.** Reach for it — not `search_summary_embeds` and not `search_filing_embeds` — when the user is asking about: - Rate adequacy: headline rate change, indicated vs selected, off-balance, capping. - Loss trends: severity trend, frequency trend, pure-premium trend, projected ultimates, LDFs, IBNR development. - Credibility / experience: experience period, weight assigned to own experience vs class-plan / bureau, credibility tables. - Expense / profit provisions: permissible loss ratio, target combined ratio, profit & contingency loading, expense ratio, investment-income offset. - Reason codes / drivers: reinsurance cost, weather/cat load, severity-driven rate need, mix shift, frequency reductions from telematics. - Anything where the answer would be a *number from the actuarial memo* rather than a description of what the filing does. The memo is where actuaries put the numerics; the extraction summary is where the pipeline puts the prose. If the question reaches for numbers, hit this surface first. **Wrong surface for**: - *Content* questions ("filings discussing wildfire scoring", "telematics programmes", "parametric triggers") — those discuss what the filing is *about*, not actuarial numerics. Use `search_summary_embeds` (broader coverage). - Concrete-filter questions ("Filings from carrier NAIC 12345 in 2024") — use `search_filings`. - Filings with no actuarial memo. Memos are typically attached to Rate filings; Form, Rule, and Withdrawal filings often have none. Coverage is narrower than `search_summary_embeds` for that reason — most of the 2026 corpus is covered, prior years are backfilling. **How to combine**: - "Personal auto filings in California whose indicated rate exceeds selected by 5+ points" → `search_filings` (state=CA, product_type="Personal Auto", filing_type="Rate") to scope a candidate set, then this tool over the candidates' memos. - "Carriers citing severity-driven rate need in 2025" → this tool first; `get_filing_summary` on the top hits to read in full. Returns top-K hits, each with `{serff, similarity, excerpt, meta}`. Default `topK=10`, max 50. Excerpt is the first 800 chars of the matching memo.

100.0/100

1 trials · measured 8 days ago

search_actuarial_embeds scores 100.0/100 on Vouch's measured behaviour index, from 1 real invocation trials against ai.serff/ca-rate-filings, measured 25 Aug 2026 under methodology v0.2.0. Every measured component scored 100.

Component breakdown

ComponentWeightValue
Reliability35%not applicable
Schema integrity25%100.0
Failure behaviour15%not applicable
Latency15%not applicable
Concurrency10%not applicable

Tool details

Transport
remote
Credential class
self-provisionable
Category
Finance & compliance
Input schema
not declared
Output schema
not declared
Side-effect classification
unclassified

Score history

DayScoreTierMethodology
2026-08-25100.0shallowv0.2.0

Probe evidence

ProbeOutcomes
schema_integritypass: 1

Raw request/response logs are not archived yet — the outcome counts above are drawn directly from every recorded trial.

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Vouch score: search_actuarial_embeds
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search_actuarial_embeds — Vouch