Semarize
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Use caseData Teams

Conversation data your warehouse can use

Transcript summaries aren't queryable. Semarize returns typed, structured JSON with boolean flags, numeric scores, and extracted values - ready for your data warehouse and BI tools.

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Typed, structured outputsDirect warehouse ingestionBatch and stream processing
SData sciencekit run

Kit

Warehouse Extraction Kit

pain_is_specificscore
budget_amountextracted
stakeholder_countnumeric
next_step_dateextracted
risk_scorescore

Output

{

"pain_is_specific": 64,

"budget_amount": 25000,

"stakeholder_count": 3

}

Data science use cases

From every conversation,
warehouse-ready structured data

01 / Structured output

Typed, queryable fields from every conversation

Transcript summaries aren't queryable. Semarize returns boolean flags, numeric scores, categorical enums, and extracted values - the same schema on every run. Push directly to BigQuery, Snowflake, or Databricks. Trend, aggregate, and model against structured conversation fields without a transformation layer.

budget_amount = 25000stakeholder_count = 3risk_score = 78decision_process_mapped = true
Warehouse extraction patterns

02 / Warehouse ingestion

Schema-stable JSON that maps directly to warehouse columns

Every Semarize API response is deterministic JSON. The same Kit always produces the same output shape. Schema-on-write or schema-on-read - fields map directly to table columns without a transformation step. Stable schema means no pipeline breakage when the next call comes in.

schema_stable = truenull_rate = "2%"rows_per_day = 1,200latency_ms = 340
API and ingestion patterns

03 / Predictive modelling

Build predictive models on structured conversation signals

Conversation data has been the last unmodelled dimension in revenue analytics because it was never structured. Semarize changes that. Correlate pain_is_specific, stakeholder_count, and budget_amount with win rates and cycle times. Build churn predictors. Model on the signals that actually describe what happened.

win_rate_correlation = 0.71churn_predictor = 0.84ramp_predictor = 0.67signal_features = 12
Data science use cases

The problem

Conversation data is
the last unstructured frontier

Data teams have structured everything except conversations. The richest data source in the company is locked in unqueryable transcripts.

Transcripts aren't queryable

You can't run a SQL query against a paragraph. Summaries and narratives don't produce the typed fields BI tools need.

NLP outputs are narrative

Custom NLP pipelines return prose explanations. Converting them to structured fields requires more engineering.

Call tools don't integrate with warehouses

Conversation intelligence tools keep data in their own UI. Exports are CSVs of summaries, not typed fields.

Custom pipelines are expensive

Building and maintaining NLP extraction pipelines requires ML engineers, training data, and ongoing model management.

Why existing tools fail

Existing tools
produce data you can't query

Current conversation tools optimise for human readers, not data systems. Their outputs aren't designed for warehouse ingestion or BI queries.

Conversation intelligence platforms

Produce dashboards and summaries inside their own UI. Bulk export gives you CSVs of prose - not typed fields your warehouse can ingest.

Custom NLP pipelines

Building extraction pipelines from scratch requires ML engineers, training data, and ongoing maintenance. Expensive and fragile.

Transcript storage

Storing raw transcripts in your warehouse gives you full text search at best. You still can't trend, aggregate, or model against structured fields.

The Semarize approach

Semarize returns
warehouse-ready structured data

Every API response is deterministic JSON with typed fields. Push directly to BigQuery, Snowflake, Databricks, or any data store.

Typed, structured outputs

Boolean flags, numeric scores, categorical enums, and extracted values. Every field has a predictable type and schema.

Direct warehouse ingestion

JSON responses map directly to table columns. No transformation layer needed. Schema-on-read or schema-on-write - your choice.

Batch and stream processing

Process historical transcript archives in batch. Stream new conversations as they happen. Same output format either way.

Correlation and modelling

Correlate conversation signals with win rates, cycle times, churn, and NRR. Build predictive models on semantic data.

Bricks & Kits

Example Bricks for
data science

These Bricks evaluate the specific dimensions that matter for bi & data teams. Bundle them into Kits to create reusable evaluation frameworks.

pain_is_specific
score 0–100

Quantifiable pain mentioned, not vague interest

→ 64
budget_amount
extracted

Specific budget figure extracted from conversation

→ 25000
stakeholder_count
numeric

Number of distinct stakeholders mentioned

→ 3
next_step_date
extracted

Specific date for agreed next action

→ "2026-03-12"
risk_score
score 0–100

Composite risk assessment for the deal

→ 78
decision_process_mapped
boolean

Decision process and timeline are understood

→ true

Warehouse Extraction Kit

kit

Extract flat, typed fields for direct warehouse ingestion.

budget_amountextracted
stakeholder_countnumeric
next_step_dateextracted
risk_scorescore
pain_is_specificscore
decision_process_mappedboolean

Output

Structured signals,
not summaries

Every evaluation returns deterministic JSON with typed values, reasons, and evidence spans. Same schema every time.

Warehouse-ready extraction
{
  "run_id": "run_pqr678",
  "status": "succeeded",
  "output": {
    "bricks": {
      "budget_amount": {
        "value": 25000,
        "confidence": 0.94,
        "reason": "Budget figure explicitly stated",
        "evidence": ["...our budget for this is around 25K..."]
      },
      "stakeholder_count": {
        "value": 3,
        "confidence": 0.90,
        "reason": "Three distinct stakeholders mentioned",
        "evidence": ["...Sarah from legal...", "...Mark in procurement...", "...the VP of Eng..."]
      },
      "risk_score": {
        "value": 78,
        "confidence": 0.83,
        "reason": "High risk: budget unclear, competitor active",
        "evidence": ["...still comparing options...", "...budget not finalised..."]
      }
    }
  }
}

Turn conversations into
queryable data.

Get structured, typed fields from every conversation. Feed your warehouse, power your BI, and model on semantic data.

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