Last updated 09-01-2026
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Siftree
Siftree pulls tickets, transcripts, reviews, social posts, PDFs, and call notes into one marketing analytics workspace for unstructured text, video, image, and audio. It runs ingest, discover, structure, and synthesize pipelines that tag entities, sentiment, and themes automatically. Teams query the resulting tables without waiting on a data science backlog. The homepage pitches it as a single source of truth for company-wide customer and market signals.
Most retrieval stacks send a few vector fragments to a model and hide the rest of the corpus. Siftree structures data on ingestion and answers with lookup queries that count and rank matches across the full dataset. Its product page contrasts opaque 1,536-dimension embeddings with quantified, exhaustive retrieval tied to source evidence. That trade-off favors auditability over quick semantic guesses.
Marketing analysts use Siftree for social listening and churn narrative tracking across TikTok, Reddit, and review sites. Customer success and support teams cluster tickets and dig into call transcripts with evidence trails back to exact sentences. Product managers track sizing complaints, risk signals, and emerging themes without manually tagging every document.
Maps 1,204 sizing complaint documents across 7 source types with linked evidence sentences
Pipeline example processes 1,204,331 documents through ingest, discover, structure, and synthesize steps
DTC workflow ingests 40,000 weekly TikTok comments while manual review covers about 200
Marketplace lists TikTok, Reddit, YouTube, G2, SEC EDGAR, and 10+ other public web sources
Connects Zendesk, Gong, Salesforce, and custom REST or GraphQL APIs without engineering
MCP-native agents run inside Claude and ChatGPT or embed in your own product
Consolidates fragmented unstructured sources into one queryable platform instead of juggling separate tools.
Every insight links back to the exact source document and sentence for auditable analysis.
MCP support lets teams run pipelines from Claude, ChatGPT, or embedded agents without writing code.
No public pricing page; teams must contact sales to get started.
Built for enterprise-scale unstructured data, which may be more than small teams need.
Marketplace and connector breadth still depends on which sources your workflow requires.
What types of data does Siftree support?
Siftree ingests unstructured and semi-structured data including text, video, image, audio, PDFs, transcripts, support tickets, and social posts. Its homepage lists customer conversations, sales calls, documents, contracts, reviews, and social content as supported source types.
How is Siftree different from traditional BI tools?
Siftree is built for unstructured data with an evolving schema, automated labeling, and 1:1 traceable evidence trails. Its comparison table positions it against AI chats and BI tools on capabilities like unstructured data support, zero-code access, and automated data labeling.
Does Siftree work with Claude and ChatGPT?
Yes. Siftree is MCP-native and shows direct integrations with Claude and ChatGPT on its homepage. Agents can kick off ingestion and analysis pipelines from those chat tools or embed Siftree into a custom product.
What public data sources are in the Siftree marketplace?
Siftree's data marketplace includes TikTok, Reddit, YouTube, Instagram, Facebook, LinkedIn, Discord, Threads, Twitch, Bluesky, Substack, G2, the Apple App Store, SEC EDGAR, US Congress data, and custom API connections.
Does Siftree publish pricing online?
Siftree does not list plan prices on its website. Access is offered through Get Started and contact flows on siftree.com, which indicates a sales-led custom pricing model rather than self-serve checkout.
What is Inductive Intelligence on Siftree?
Inductive Intelligence is Siftree's approach where the platform learns what is inside your data and builds structure around it without predefined categories. The how-it-works page describes ingest, fact extraction, pattern discovery, and structured views as the core steps.
How does Siftree retrieval differ from vector search?
Siftree structures data during ingestion and runs lookup queries that count matches across the full corpus. Its product page contrasts this with opaque vector embeddings that return only a few fragments, leaving most documents invisible to the model.