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.
Kit
Warehouse Extraction Kit
Output
{
"pain_is_specific": 64,
"risk_score": 78,
"stakeholders": ["VP Eng", "CFO", "Legal"]
}
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, categories, and extracted lists - 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.
Sales Call
Transcript
Semarize Kit
Evaluation
Warehouse Table
BigQuery / Snowflake
| field | type | value |
|---|---|---|
| risk_score | score | 78 |
| pain_is_specific | score | 64 |
| decision_process_mapped | boolean | true |
| competitor_mentioned | category | Rival Co |
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 output
JSON keys map to warehouse columns
api response
warehouse.deal_signals
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, risk_score, and stakeholder counts with win rates and cycle times. Build churn predictors. Model on the signals that actually describe what happened.
Signal correlation
pain_is_specific vs win rate
feature importance
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 free-text 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 free text - 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.
Quantifiable pain mentioned, not vague interest
Composite risk assessment for the deal
Distinct stakeholders named on the call
Competitor named in the conversation
Decision process and timeline are understood
A concrete next step was agreed
Warehouse Extraction Kit
kitExtract flat, typed fields for direct warehouse ingestion.
Output
Structured signals,
not summaries
Every evaluation returns deterministic JSON with typed values, reasons, and evidence spans. Same schema every time.
{
"run_id": "run_pqr678",
"status": "succeeded",
"output": {
"bricks": {
"stakeholders": {
"value": ["VP Eng", "CFO", "Legal"],
"confidence": 0.90,
"reason": "Three distinct stakeholders named",
"evidence": ["...Sarah from legal...", "...Mark in procurement...", "...the VP of Eng..."]
},
"risk_score": {
"value": 78,
"confidence": 0.83,
"reason": "High risk: budget unconfirmed, competitor active",
"evidence": ["...still comparing options...", "...budget not finalised..."]
},
"decision_process_mapped": {
"value": true,
"confidence": 0.88,
"reason": "Steps, owners, and timing were covered",
"evidence": ["...legal signs off, then procurement, close by quarter end..."]
}
}
}
}Turn conversations into
queryable data.
Get structured, typed fields from every conversation. Feed your warehouse, power your BI, and model on semantic data.