How does this solve 'Management Blindness' in large organizations?

Decision-makers often rely on filtered reports from local managers. Our systems give AI agents direct, unbiased access to raw corporate data—emails, contracts, and internal records—providing an unvarnished view of what is actually happening in the organization.

Can this really help dismantle departmental information silos?

Information feudalism occurs when local brokers hoard data to maintain power. By building a unified, auditable knowledge graph that bypasses these gatekeepers, we democratize information and ensure that knowledge becomes a verifiable company asset rather than a personal one.

Why does my AI agent lose context between sessions?

Most agents rely on flat chat history or simple vector search (RAG). Without a persistent cognitive layer that separates facts from beliefs and tracks temporal updates, the agent treats every new session as a 'blank slate' or retrieves irrelevant, outdated snippets from the vector store.

How to add persistent memory to an LLM agent properly?

True persistence isn't just about storing text. It's about building a 'Memory OS' that uses a semantic knowledge graph (like Neo4j) alongside fast vector recall (FAISS). This allows the agent to traverse entities, understand relationships, and maintain state across weeks or months of interaction.

Why is RAG not working for my multi-hop questions?

Vector search is 'point-to-point'. If a question requires connecting Fact A from document 1 to Fact B from document 2, flat RAG often misses the link. A Knowledge Graph-based approach (GraphRAG) maps these associations explicitly, allowing for complex reasoning that vector search alone cannot achieve.

How do I ensure an audit trail for agent hallucinations?

We implement SGR (Source-Grounded Retrieval) Provenance. Every claim the agent makes is tied back to a specific character-level offset in your source data. If the agent makes a mistake, you can instantly see exactly which piece of data (or lack thereof) led to that conclusion.

Should I use Zep, Mem0, or a custom memory pipeline?

Tools like Zep and Mem0 are excellent building blocks, but enterprise needs often require a custom infrastructure for specific domain schemas, bi-temporal fact tracking, and autonomous 'Gardener' agents. We design and deploy the right sovereign stack for your specific requirements.

Auditable Intelligence Layer

Auditable Intelligence for the Data-Driven Enterprise.

RAG is not enough. We build reflective memory layers that eradicate management blindness and bypass information silos.

Discuss a Pilot Explore Infrastructure

Supported LLM Providers & Core Stack

OpenAIGeminiClaudeKimiDeepseekNeo4jFAISSMCP

The Reality Check

RAG is Not Enough for Business

It's not about the model. It's about the infrastructure that makes your data transparent, verifiable, and actionable.

Eradicate Management Blindness

Stop relying on filtered reports. Give your agents direct access to the unvarnished truth hidden in raw corporate data.

Defeat Information Feudalism

Break down departmental silos and bypass internal gatekeepers. Knowledge belongs to the company, not the broker.

Proprietary Content Synthesis

Generate analytical reports and contracts deeply grounded in your private corpus with 100% auditable citations.

AI-Native Readiness

Standard data swamps aren't enough for autonomy. Build the structured, temporal memory foundation for tomorrow's agents.

Engineering Services

What We Build

We don't just sell software. We deploy auditable infrastructure that transforms raw data into verifiable organizational knowledge.

Reflective Knowledge Pilot

A 4–8 week deployment on your corpus. Deliverables: Fact Layer with character-level provenance, Concept Graph, and Synthesis Report.

4-8 WeeksFixed Scope

Memory Layer for Agents

Engineering cognitive memory into your production stack. Solve cross-session continuity, temporal drift, and audit trail requirements.

IntegrationProduction

Research Knowledge Factory

Deploy multi-agent pipelines for deep synthesis. Identify methodology risks and semantic ancestry in large-scale scientific or legal corpora.

AnalyticsMulti-Agent

Architecture & Strategy Audit

Technical review for teams scaling beyond RAG. We provide ADR-driven roadmaps to transition from search to auditable intelligence.

ConsultingTechnical

The Pipeline

Universal Knowledge Pipeline v3

A four-stage autonomous process that transforms raw data into a verifiable, reflective knowledge graph.

Extract

Normalizing disparate sources—PDFs, APIs, and databases—into a unified stream with character-level provenance.

Enrich

Multi-agent analysis to extract atomic facts, identify entities, and map temporal validity for every claim.

Consolidate

Reflective Memory Loop: aligning new information with existing knowledge to resolve contradictions and merge concepts.

Index

Dual-layer indexing: PostgreSQL for high-speed recall and Neo4j for complex associative reasoning and audit logs.

Our Framework

The Technology Behind the Solution

We use our proprietary MemoryOS architecture to build your sovereign cognitive infrastructure.

MCP Interface

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Cognitive Router

FACTUALFast Memory (FAISS)

REFLECTIVEConsolidation Loop

HYBRIDGraph Synthesis (Neo4j)

Universal Pipeline v2+

Preparation → Extraction → Enrichment → Consolidation → Graph Commit

Autonomous Agents (Gardener System)

Phase 0: Batch Triage → Phase 1: Surgical Agent (merge | split | refine | create_relation)

Why it's different

Bi-temporal Fact Tracking

SGR Symbol-Level Provenance

Agentic Memory Gardening

Cognitive MCP Reasoners

Turnkey Infrastructure

Case Studies & ROI

Proven on Enterprise Data

Our architecture is battle-tested on large-scale scientific corpora and complex organizational archives.

438+Papers ProcessedOpenSCG corpus

6,397Semantic SegmentsGrounded claims

26,000+Concept NodesPersonal knowledge graph

400%Throughput ImprovementIntelligent token batching

Featured Case Study

OpenSCG: Autonomous Scientific Knowledge Synthesis

How we automated the extraction and consolidation of thousands of scientific claims into an evolving concept graph.

Long-Tail Analysis

Why AI Agents Fail in Production

Addressing the critical memory gaps that prevent LLM agents from reaching enterprise-grade reliability.

The market for AI agents is exploding, but the value is moving from generic models to specific, persistent intelligence.

Works With Your Stack

Memory OS is designed to be infrastructure-agnostic and connects to any LLM or agent framework.

ClaudeChatGPTLangGraphNeo4jFAISSPostgreSQLDockerAzureGeminiMCP ProtocolClaudeChatGPTLangGraphNeo4jFAISSPostgreSQLDockerAzureGeminiMCP Protocol

MCP ProtocolGeminiAzureDockerPostgreSQLFAISSNeo4jLangGraphChatGPTClaudeMCP ProtocolGeminiAzureDockerPostgreSQLFAISSNeo4jLangGraphChatGPTClaude

Ready to bypass information silos?

Launch a **Reflective Knowledge Pilot** to transform your corpus into a verifiable, auditable asset. No more management blindness.

Discuss a Pilot

Or email us at info@blatt.ltd