PRODUCT CONCEPT / THE SOVEREIGN WEAVER
AANANSI
The sovereign web
for every AI.
Every memory becomes a thread you own. Every model connects to the web without owning it.
Follow the weaver
The sovereign web
01 THE GUARDED WEB
Every thread knows
who may touch it.
Role-based access, dynamic context masking, and complete audit visibility filter sensitive knowledge before any model receives it.
- Local-first storage
- Staff access control
- Zero-trust context
02 THE MODEL WEAVE
Change the intelligence.
Keep every thread.
Connect different cloud or offline models to one independent knowledge layer without rebuilding your organization’s accumulated context.
- Provider agility
- Portable embeddings
- Offline model routing
03 THE SIGNAL WEAVER
Cut the dead weight.
Keep the living signal.
Semantic retrieval and graph compaction isolate the knowledge that matters, with a design target of reducing prompt payloads by up to 60%.
60%CONTEXT REDUCTION TARGET
WHY NOW THE LOCK-IN PROBLEM
AI providers should compete for your work.
Your memory should belong to you.
Today, corporate knowledge becomes trapped inside model-specific workflows. Moving providers means losing context, rebuilding embeddings, or paying to resend history again and again.
ANANSI is conceived as independent cognitive middleware: a local knowledge graph that holds memory once, governs it at the source, and supplies only the necessary context to the model chosen for each task.
ARCHITECTURE THREE SOVEREIGN PILLARS
Built around control,
not dependence.
01 / GOVERN
ZERO-TRUST DATA GOVERNANCE
Context filtered at the source.
- Role-Based AccessMap visibility to teams, roles, and organizational hierarchy.
- Dynamic MaskingRemove financial, HR, or restricted data before inference.
- Audit TrailsSee who queried which knowledge and when.
02 / MOVE
LOCAL-FIRST COGNITIVE CORE
One memory. Any intelligence.
- Model AgilityKeep vectors, graphs, and history on local or private infrastructure.
- Hot-SwappingRoute each task to the model that fits without memory loss.
- Offline PathsKeep sensitive workloads away from public model endpoints.
03 / REDUCE
CONTEXT-PRUNING ENGINE
Dense knowledge. Smaller payloads.
- Semantic RetrievalPull only the sentences and data points relevant to the task.
- Graph CompactionTurn repeated history into connected, reusable knowledge nodes.
- Cost ControlTarget up to 60% less prompt context and lower API usage.
USE CASES ONE CORE / THREE SCALES
Private intelligence
where it matters.
ENTERPRISE
A secure internal knowledge layer
Accelerate onboarding and institutional search while separating general staff context from executive, HR, and financial knowledge.
REGULATED & BOUTIQUE FIRMS
AI utility without public data exposure
Give legal, financial, medical, and specialist teams local-first retrieval paths designed for sensitive client information.
POWER USERS
A lifelong private memory graph
Connect journals, books, project notes, and personal archives to the AI of your choice without surrendering the source library.
CONTROLLED RELEASE THE FIRST THREAD
ANANSI is being built now.
Enter before public release.
Join for development updates, product previews, private testing invitations, and opportunities to influence the first controlled release.