kwale Memory

Creative intelligence, structured and searchable

A multimodal knowledge base that ingests documents, images, videos, and audio. Search across every format with a single query. The context layer for all kwale products.

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The Problem

Brand knowledge is scattered everywhere

Brand guidelines live in PDFs nobody reads. Past campaign performance sits in spreadsheets nobody opens. Creative assets are scattered across Google Drive, Dropbox, and hard drives. Institutional knowledge lives in people's heads and leaves when they do.

Every time a team starts a new campaign, they reinvent the wheel. They cannot easily find what worked before, why it worked, or which assets matched which guidelines. The result is inconsistent creative output and wasted effort recreating knowledge that already exists.

kwale Memory unifies all creative knowledge into a single, semantically searchable intelligence layer that every kwale product can draw from automatically.

6 formats
Documents, images, videos, audio, URLs, and presentations -- all ingested, embedded, and cross-searchable from a single query interface.
How It Works

From scattered assets to structured intelligence

01

Ingest everything

Upload documents, images, videos, audio, and presentations -- or connect Google Drive, Dropbox, and URLs for automatic ingestion. Memory processes every format through specialized ingestion pipelines.

02

Multimodal embeddings

Every piece of content is embedded using modality-specific encoders (SigLIP 2 for images, Whisper for audio, dense text embeddings). All modalities share a unified semantic space for cross-modal search.

03

Hybrid search

Dense vector search + BM25 keyword search + reciprocal rank fusion (RRF). Find videos matching a brand guideline document. Search images by describing a scene in natural language.

04

Knowledge graph

Memory maps relationships between assets automatically. See which briefs produced which content, which guidelines apply to which campaigns, and which creative approaches drove the highest scores.

05

Context injection

Every kwale product can draw from Memory automatically. Studio uses your brand KB for generation. Score uses past performance data for benchmarking. The knowledge compounds over time.

Key Capabilities

Everything for creative knowledge management

Cross-Modal Retrieval

Search across all content types with a single natural language query. Find videos matching a brand guideline, images matching a scene description, or documents referencing a specific metric.

Multimodal embedding space

Parent-Child Chunking

Intelligent document segmentation that preserves context. Child chunks carry their parent context, so search results always include the surrounding narrative and source attribution.

Hierarchical context preservation

Contextual Enrichment

Every chunk is automatically enriched with metadata: source, creation date, related assets, neural scores (for video content), and AI-generated summaries for quick scanning.

Automatic metadata generation

Knowledge Graph

Relationships between assets are mapped automatically. Briefs link to content they produced. Guidelines link to assets that follow them. Performance data links to creative decisions.

Entity relationship mapping

6 Ingestion Pipelines

Specialized pipelines for documents (PDF, DOCX, TXT), images (SigLIP 2 + VLM description), video (Marengo + transcript), audio (Whisper), URLs (Firecrawl), and presentations (PPTX).

Format-specific processing

Feedback-Driven Learning

Memory learns from usage patterns. Frequently accessed assets rise in search rankings. Neural score data from kwale Score enriches video assets automatically over time.

Usage-adaptive relevance ranking
The Architecture

Built for multimodal intelligence at scale

Memory is an infrastructure product, not a neural scoring product. It provides the context layer that makes every other kwale product smarter. The architecture is designed for high-throughput ingestion, low-latency retrieval, and growing knowledge over time.

Dense vector search (SigLIP 2 / text embeddings)
BM25 keyword search for exact matching
Reciprocal Rank Fusion (RRF) for hybrid results
Knowledge graph for entity relationships
Parent-child chunking with context preservation
Feedback loop from search usage patterns
Related Research
Radford, A., et al. (2021). Learning transferable visual models from natural language supervision. ICML.
Robertson, S., et al. (2009). The probabilistic relevance framework: BM25 and beyond. Foundations and Trends in IR.
Lewis, P., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. NeurIPS.
View full research references →
Pricing

Choose your plan

Bundled

Included

With any Pro product

  • Per product tier limits
  • 5 GB storage
  • 500 queries/month
  • 5 videos/month ingestion
  • Basic search
  • Manual upload only
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Standalone

$19/mo

10 KBs, 50 GB

  • 10 knowledge bases
  • 1,000 documents
  • 20 videos/month ingestion
  • 50 GB storage
  • 5,000 queries/month
  • Google Drive sync
  • Auto-discovery (5 sessions/mo)
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Enterprise

Custom

Unlimited everything

  • Unlimited KBs & documents
  • Unlimited video ingestion
  • 500 GB storage
  • Unlimited queries
  • Confluence + Notion sync
  • Unlimited auto-discovery
  • API access
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Ready to organize your creative intelligence?

Join the waitlist for kwale Memory and unify all your creative knowledge in one searchable layer.

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Built on peer-reviewed neuroscience. Patent pending.