Get Started — Overview
Three years after the AI Big Bang, early galaxies are forming in the Cloud AI universe. This report summarizes benchmarks, roadmaps, "dark matter", and five strategic predictions for founders and builders.
Core Concepts
Defined terms
Supernova, Shooting Star, Model Context Protocol (MCP), Persistent Memory, System of Action, Eval Pipeline.
- Supernova — explosive ARR growth; low margins
- Shooting Star — stable growth; strong PMF
- MCP — Model Context Protocol (MCP)
- Persistent Memory — cross-session personalization
Notable software & services
Standards & protocols
Model Context Protocol (MCP); data lineage; eval harnesses; vector DB standards for memory.
Technology & Systems
AI Infrastructure
Model layer (OpenAI, Anthropic, Gemini), open-source models (Llama, Mixtral), compute, and observability.
- Foundation models & specialized open stacks
- Evaluation pipelines and data lineage
Developer Platforms
Prompts-as-programs, persistent memory, and MCP-enabled agent orchestration.
Acronyms expanded on first use: Model Context Protocol (MCP); Large Language Model (LLM); Annual Recurring Revenue (ARR); Full-Time Equivalent (FTE); Retrieval-Augmented Generation (RAG); Massive Multi-Task Language Understanding (MMLU).
Challenges
Retention & Value
Rapid demos and low switching costs can create fragile retention despite fast growth.
Evaluation
Public benchmarks are poor proxies; private evals and lineage are required for enterprise trust.
Memory & Context
Persistent cross-session memory remains brittle and costly; privacy concerns matter.
Solutions — How OPAL addresses the challenges
OPAL & Memory / Eval services
OPAL provides enterprise-grade semantic integration, knowledge graph connectivity, and tooling for managing eval flow and lineage. (Primary solution linked below.)
Solution comparison
| Capability | OPAL | Memory-as-a-Service |
|---|---|---|
| Primary focus | Knowledge graph & enterprise integration | Hosted vector stores & retrieval |
| Use-case | Semantic integration, eval pipelines | Low-latency memory & RAG |
| Deployment | On-prem / cloud with Virtuoso | Cloud-managed APIs |
Standards & Protocols
Model Context Protocol (MCP)
A spec for agent tool access, memory, and permissioning which simplifies integrations among agents and services.
Evals & Data Lineage
Private eval harnesses and lineage metadata for compliance and reproducible performance measurement.
Implementation Strategy
- Define the wedge: pick a language-heavy workflow with clear 10x ROI.
- Design memory: scope persistent vs session memory; apply consent & encryption.
- Build evals: instrument private, reproducible metrics for hallucination and business impact.
- Integrate MCP: connect tools, agents, and permissioning using the Model Context Protocol (MCP).
- Operationalize: run continuous drift detection, prune memory, and expand into a system of action.
HowTos
FAQ
Entity Directory
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