# Mala.dev - AI Decision Accountability Platform

> This file provides context about this website for AI agents and LLMs.

## Overview

Mala.dev is the **decision substrate for the agentic AI era**. We provide cryptographically-sealed audit trails and governance for autonomous AI systems, enabling enterprises to prove why their AI decided what it did.

## Core Value Proposition

Unlike logging or observability tools, Mala doesn't just track what AI does—we **seal decisions cryptographically** to create tamper-proof, legally defensible records of AI decision-making.

## Key Pages

### Product Pages
- `/` - Home page with overview of the platform
- `/brain` - The Wisdom Graph: How AI decisions compound into institutional memory
- `/trust` - The Seal: Cryptographic decision sealing and compliance
- `/sidecar` - Ambient ingestion from any AI framework (LangChain, CrewAI, etc.)
- `/developers` - API documentation and developer resources
- `/context-engineering` - Context Engineering: Building decision-aware AI pipelines

### Industries
- `/healthcare` - Healthcare industry landing page
- `/banking` - Banking industry landing page
- `/industries/defense` - Defense contractor AI accountability
- `/industries/fintech` - AI governance for FinTech and banking
- `/industries/healthcare` - HIPAA-compliant AI decision trails

### Comparisons (vs. Competitors)

#### Agent Platform
- `/vs/sierra` - Mala vs Sierra AI: Vertical Silos vs Horizontal Substrate: Don't Fragment Your Institutional Wisdom

#### Category
- `/vs/context-engines` - Mala vs Context Engines: Why 'Context' isn't enough without 'Authority'

#### Data Platform
- `/vs/snowflake` - Mala vs Snowflake Cortex: Downstream Analytics vs Inline Capture: Warehouses Are Too Late

#### Direct
- `/vs/workfabric` - Mala vs WorkFabric: The Sealed Substrate vs The Context Pipeline

#### Engineering Observability
- `/vs/playerzero` - Mala vs PlayerZero: The 'Why' for Engineering vs. The 'Why' for Business

#### Enterprise SaaS
- `/vs/salesforce` - Mala vs Salesforce Agentforce: System of Record vs System of Decisions: Why Salesforce Can't Trace the 'Why'

#### Finance Automation
- `/vs/maximor` - Mala vs Maximor: Vertical Application vs. Horizontal Infrastructure

#### Framework
- `/vs/crewai` - Mala vs CrewAI: The Accountability Layer for Multi-Agent Swarms
- `/vs/langchain` - Mala vs LangChain: Governance for the LLM Application Stack

#### LLM Observability
- `/vs/braintrust` - Mala vs Braintrust: Evaluation vs Governance: Scores Don't Seal Decisions

#### Memory
- `/vs/letta` - Mala vs Letta: Sealing the State of Stateful Agents
- `/vs/mem0` - Mala vs Mem0: Immutable History for Self-Improving Memory
- `/vs/zep` - Mala vs Zep AI: The Audit Layer for Agent Memory

#### Observability
- `/vs/agentops` - Mala vs AgentOps: From Monitoring to Accountability
- `/vs/arize` - Mala vs Arize AI: Audit vs Observability: Why Logs Aren't Legal Proof
- `/vs/langfuse` - Mala vs LangFuse: Enterprise Trust Layer for Open Source Observability
- `/vs/langsmith` - Mala vs LangSmith: Production Governance for LangChain

#### Sales Automation
- `/vs/oliv` - Mala vs Oliv: Sales Intelligence vs. Decision Governance

### Resources
- `/blog` - SEO-optimized articles on AI governance, compliance, and accountability (199+ articles)
- `/learn` - AI education resources with 112+ FAQs and glossary terms
- `/resources` - Downloadable resources and documentation

## 2026 Content Strategy: Key Concepts

### Decision Traces
A structured, replayable record of business reasoning, policy evaluation, and authorized exceptions at the moment of commit. Unlike logs that capture "what happened", Decision Traces prove "why it was authorized".

### Context Graph
A living, learned world model of an organization's physics, connecting entities through decision events and "why" links.

### Ambient Siphon
Zero-touch instrumentation layer that captures intent directly from existing SaaS tools (Slack, Salesforce, Datadog) without code changes.

### Learned Ontologies
Emergent organizational structure discovered through agent trajectories and human-in-the-loop resolutions—capturing how your best operators actually decide.

### Institutional Memory
The accumulated library of prior decisions, overrides, and precedents that grounds future agentic autonomy.

## Key Differentiators

1. **Cryptographic Decision Sealing** - Not just logging, but SHA-256 tamper-proof sealed records
2. **Human-in-the-Loop Accountability** - Track when humans approve, override, or escalate
3. **Precedent-Based Governance** - AI learns from past decisions via Wisdom Graph
4. **Framework Agnostic** - Works with LangChain, CrewAI, Autogen, or any framework
5. **Enterprise Compliance** - SOC 2 Type II, HIPAA ready

## Technical Architecture

- **Decision Graph** - Captures intent → policy match → decision sealed
- **Sidecar Ingestion** - VPC-native deployment, data stays in your firewall
- **MCP Server** - Model Context Protocol for tool integration
- **API & SDK** - TypeScript/Python SDKs for direct integration
- **FalkorDB** - Sub-20ms precedent retrieval at enterprise scale

## Contact

- Book a Demo: https://cal.com/projecta
- Email: alpha@mala.dev
- Website: https://www.mala.dev

## Content Updates

This site's blog is automatically updated daily with new SEO-optimized content on:
- AI governance and compliance
- Enterprise AI accountability
- Multi-agent system governance (Agentic Drift, Agent Hives)
- Industry-specific AI regulations (FinTech, Healthcare, Defense)
- Decision Traces, Context Graphs, and Learned Ontologies

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*Last updated: 2026-02-19*