Inductive Intelligence

Siftree learns what's inside your data, then builds a structure around it. You don't need to define the categories — Siftree automatically discovers them.

TikTokRedditPDFsAudioSentiment Shift+12%8,310 docsChurn Risk+24%5,058 docsEmerging TopicNEW3,854 docsLegal ThreatsSTABLE3,372 docsEmbed1,204,331 docs indexedDiscover847 topics activeStructure98.2% confidenceExtract14,200 entities tagged
PIPELINE ACTIVE | 1,204,331 docs processed
01 INGEST

Smart Ingestion

Siftree ingests transcripts, tickets, PDFs, audio, and video, then reads each source for meaning. Different formats become one shared semantic space, so related ideas can be compared directly without forcing data into rigid schemas first.

DOCPDF / ReportAUDAudio RecordingVIDVideo CallTXTSupport TicketAll formats mapped to a shared meaning space
Support TicketI spoke with John Davis at Acme Corp last Tuesday.Trying to cancel my subscription has been frustrating.Billing pages looped and support chat timed out.EXTRACTED ENTITIESPERSONORGTIMEINTENT: CHURNSENTIMENT: NEGATIVE
02 DISCOVER

Fact Extraction

Once ingested, Siftree labels what is explicitly present: people, organizations, intents, timestamps, and sentiment. Each detected signal becomes structured, queryable ground truth tied back to source evidence.

03 STRUCTURE

Pattern Discovery

With facts extracted, broader themes organize themselves. Recurring issues and opportunities cluster into clear groups, so your data defines its own structure before any dashboard or taxonomy is imposed.

Sizing Issues2,341 docsOnboarding Friction1,882 docsPrice Objections2,104 docsSupport Delay1,409 docs
Churn Risk8,412 docsPrice Objections4,930 docsOnboarding Friction3,204 docsTicket Escalation1,740 docsSizing Issues5,180 docsRefund Intent2,611 docsSentiment Neg6,277 docsDelayed Support2,109 docsSELECT concept, doc_count FROM ontologyWHERE signal = 'Churn Risk' AND delta > 0-- 8,412 rows · auditable to source
04 SYNTHESIZE

Structured Ontology

All signals converge into a queryable, auditable system. Concepts and relationships are counted, ranked, and tied to source evidence. Not a narrative summary — a live database your teams and agents can operate on.

Put this process to work on social data

See how your AI agents can query, compare, and act on millions of social posts with auditable evidence.

The result?

You can slice, dice, and break down unstructured data like never before. We take care of all the messy work for you, and organize your data into a hierarchical taxonomy that's ready to use right away. And because your data is embedded, you're able to comb through it with neural search, sifting through your data like a knife through butter.