LLM analysis

The analysis stage turns a normalised transcript into a structured business summary: title, prose summary, bullet-point summary, key discussion points, action items, sentiment (with per-party + progression), tone, and a long tail of typed lists (decisions, risks, opportunities, important dates, …).

Provider. OpenAI by default. Any OpenAI-compatible endpoint works. Configured via ScryonProperties.Llm (model, base URL, API key, temperature, timeout).

What the LLM receives

AnalysisPrompt builds a two-part chat completion:

1. A system prompt that defines the output schema and the rules (no inventing names/dates, cite sourceSegmentIds, avoid generic phrasing, etc.).
2. A user prompt containing:
   • A non-PII call metadata block (callId, recordedAt, durationSeconds, optional title).
   • The compact transcript view — language, durationSeconds, speakers[], and segments with stable seg_NNNN ids, speakerId, role, times, and text.

The transcript is post-resolution — by the time it reaches the LLM, speakers are named where evidence allowed, and ids are stable.

Speaker-neutral narrative. Speaker ids/labels are used only to resolve action-item ownership. The summary, bullets, discussion points, and sections are written speaker-neutral — what was discussed/decided/agreed, not who said it (e.g. "A revised quote was requested" rather than "Speaker 1 asked for a revised quote"). Real names appear only when the transcript states them and naming the person matters (e.g. someone to follow up with). "Speaker 1 / Speaker 2" phrasing never appears in human-readable text.

No repetition. executiveSummary, executiveSummaryBullets, keyDiscussionPoints, and sections must not restate the same fact. Each fact lives in its single best home: prose = 2–4 sentence headline; bullets = the one detailed view; discussion points = only when chronological flow adds something the bullets don't (may be empty); sections = only when thematic grouping adds structure beyond the bullets.

Schema versions

Version	What's in it
v1	Prose executiveSummary, sections[], flat Sentiment { overall, reason }. No tone.
v2 (current)	Adds executiveSummaryBullets[] (bullet companion to the prose summary), keyDiscussionPoints[] (chronological flow), enriched Sentiment (numeric score, per-party split, progression[], emotionalSignals[]), and a new top-level Tone. All additions are nullable — v1 artifacts still deserialise unchanged.

ScryonAnalysis.CURRENT_SCHEMA_VERSION is 2. The endpoint serves whatever was stored on disk: old calls keep their v1 shape until re-analyzed.

What we get back

{
  schemaVersion: 2,
  callType: ...,
  suggestedTitle: ...,
  oneLineSummary: ...,
  executiveSummary: "<prose paragraph>",
  executiveSummaryBullets: [
    { text, category, importance, sourceSegmentIds }       // 8–15 detailed, speaker-neutral bullets
  ],
  conversationOutcome: ...,
  sections: [ Section ],                                   // dynamic, thematic
  keyDiscussionPoints: [
    { text, topic, phase, speakerId, ..., sourceSegmentIds } // chronological flow
  ],
  actionItems: [ ActionItem ],
  followUps, importantDates, decisions, commitments,
  openQuestions, risks, opportunities,
  peopleMentioned, numbersAndAmounts,
  sentiment: {
    overall, score, reason,
    userSentiment    : { overall, score, notes },
    contactSentiment : { overall, score, notes },
    progression: [ { phase, overall, note, sourceSegmentIds } ],
    emotionalSignals: [ string ]
  },
  tone: {
    overall, descriptors, formality, energy, pace, notes,
    byParty: { userTone, contactTone }                     // PartyTone = { overall, descriptors, notes }
  },
  tags: [ string ],
  qualityWarnings: [ string ]
}

The full field reference lives in API · Analysis. What follows is the design of the new pieces.

Bullets: executiveSummaryBullets

executiveSummaryBullets is the primary detailed summary the app renders (Fathom-style). The prose executiveSummary is only a brief 2–4 sentence headline. Rules baked into the prompt:

• 8 to 15 bullets for a typical 3–15 minute call (scale up for longer/denser calls; minimum 4 if substantive).
• Each bullet is a specific, self-contained, speaker-neutral fact — include names, amounts, dates, and outcomes when the transcript provides them (describe the fact, not who voiced it). Do not omit material details.
• Categories: context | outcome | next_steps | concern | agreement | decision | blocker | observation | topic.
• Never duplicate the prose executiveSummary (a 2–4 sentence headline) or any other field — each fact appears once.
• Cite sourceSegmentIds wherever possible.

Key discussion points

sections group items by theme. keyDiscussionPoints lays them out in narrative order — what actually happened, in sequence. This is the view a client renders when the user wants a timeline of the call without re-reading the transcript.

• Optional — included only when chronological order/flow adds information the bullets don't already carry (see Speaker-neutral narrative above). May be empty.
• When present, 5 to 15 points for a typical 3–15 minute call. Low-signal small talk is dropped, as is anything that merely paraphrases a bullet.
• Each point names a free-form topic and a phase (opening | middle | closing | followup).
• Written speaker-neutral: speakerId, speakerLabel, speakerDisplayName, speakerRole are left null. (They remain in the schema for backward compatibility but are no longer populated.)
• sourceSegmentIds are always cited.

Enriched sentiment

v1 sentiment was { overall, reason }. v2 expands it because that wasn't enough signal for clients to render anything richer than a single emoji.

• score ∈ [-1.0, 1.0] — numeric polarity. null only when truly unclear.
• userSentiment / contactSentiment — per-party reads with their own overall + score + notes. They commonly disagree. A frustrated customer talking to a calm support agent has contactSentiment.overall=negative and userSentiment.overall=neutral.
• progression — 2–4 timeline points (opening | middle | closing | followup). Captures shifts: "started tense, ended warm" is the classic example.
• emotionalSignals — 0–6 short adjective tags grounded in the transcript (frustrated, appreciative, relieved, anxious, confident). Never invented — the LLM is told to leave the array empty when the text doesn't support a tag.

Tone

Tone is distinct from sentiment. Sentiment is polarity (positive/negative). Tone is register — how things were said (formal, urgent, friendly, terse, …).

A perfectly cordial conversation can be sentiment.overall=positive and tone.overall=transactional. A heated argument can be sentiment.overall=negative and tone.overall=confrontational. They aren't redundant.

Field	Meaning
overall	Headline tone label from a documented vocabulary.
descriptors	1–4 short adjectives capturing nuance.
formality	formal | semi-formal | informal
energy	low | medium | high
pace	slow | normal | fast
notes	1–2 sentence rationale.
byParty.userTone / contactTone	Per-party tone reads (overall, descriptors, notes).

Allowed headline labels include professional, friendly, tense, urgent, supportive, transactional, escalated, empathetic, terse, playful, confrontational, informational, casual, formal-business, conversational.

Action item extraction

After analysis succeeds, CallPersistenceService.applyAnalysisSideEffects walks actionItems[] and inserts a row per item into action_items. Owner fields are resolved through ActionItemOwnerMapper and double-checked against transcript.speakers[]:

LLM output	Used directly?	Cross-checked?
ownerSpeakerId	Preferred	Verified to exist in speakers[].
ownerSpeakerLabel	Used if id missing	Matched (case-insensitive) against labels and display names.
ownerDisplayName	Used if neither above	Populated from the resolved speaker.
ownerRole	Used directly	Filled in from the resolved speaker if absent.

The mapper never invents an owner — if the LLM is vague and no transcript speaker matches, the row carries null owner fields. The raw ownerSpeaker string is preserved for forensic debugging.

Intent classification (v2 additive)

Each action item now carries a provider-neutral intent and optional intentMetadata. This is the contract between the LLM and the client for launcher chips:

intent	Meaning
meeting	Schedule a video / phone meeting
email	Send / draft an email
call	Phone the contact back
message	Send a chat / SMS
reminder	Self-reminder
task	Track in a task system
none	No launchable intent

The backend classifies; the client launches. The server never constructs deep links or knows which apps the user has installed. When the transcript was explicit ("let's hop on Zoom"), the LLM may set intentMetadata.providerHint — a reordering hint for the client's chip list, not a binding choice.

intent and intentMetadata are persisted to Postgres (action_items.intent, action_items.intent_metadata_json) and surfaced on both GET /api/actions and GET /api/calls/{id}/analysis. Older rows have null intent — no chips rendered.

See API · Action items for the full metadata field reference and client integration notes.

executiveSummaryBullets, keyDiscussionPoints, sentiment, and tone are not denormalised to Postgres. They live in the analysis JSON artifact only.

Failure handling

• Hard fail on LLM 4xx (e.g. content policy block, invalid key). Call moves to FAILED.
• Retry once on network / 5xx.
• Soft fail on action-item extraction — transcript and analysis still ship.

Idempotency

Re-running analysis (e.g. after a prompt change) is safe:

• ANALYSIS_JSON is overwritten in place. The artifact stays the raw LLM bytes, not Jackson's re-serialization, so the original wire format is preserved verbatim.
• Action items are deleted-then-reinserted for the call — no duplicate rows on reprocess.

Telemetry

• scryon.analysis.duration{provider="openai"} — timer.
• scryon.analysis.cost.tokens{type="prompt|completion"} — counter (when the provider returns usage info).
• scryon.action_items.extracted — counter (per call).
• event=PIPELINE stage=ANALYZED status=COMPLETED durationMs=... tokens=...

Privacy

• The LLM provider receives the transcript text. Choose a provider that meets your data residency requirements.
• The prompt does not include phone numbers, emails, the user's externalUserId, contact name, or any field beyond what's required for the analysis.
• Set LLM_TEMPERATURE=0.2 (default) to keep outputs deterministic-ish; raise carefully.
• Sentiment and tone scores must be grounded in the transcript. The prompt explicitly tells the model to prefer unclear and lower magnitudes when in doubt.

Code map

Concern	File
Provider interface	AnalysisClient
OpenAI implementation	OpenAiAnalysisClient
Prompt builder + schema enforcement	AnalysisPrompt
Output DTO (records)	ScryonAnalysis (in com.scryon.analysis.dto)
Pipeline orchestrator	CallProcessingService.finishPipelineAfterTranscript
Postgres side effects + action items	CallPersistenceService.applyAnalysisSideEffects
Owner reconciliation	ActionItemOwnerMapper
GET /analysis adapter	CallAnalysisResolver (legacy aliases)
Endpoint	CallController#analysis

Tests to look at

Test	What it covers
ScryonAnalysisV2SchemaTest	Round-trip of all v2 fields + v1 backward compat.
CallAnalysisResolverTest	Legacy alias generation + v2 pass-through.
AnalysisPipelineActionItemsTest	End-to-end pipeline with a mocked LLM.
CallArtifactEndpointTest	GET /analysis legacy aliases from stored artifact.

Related

• Sharing & summary digest — how clients turn this analysis into a shareable WhatsApp / email summary.