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OLTAIX™: AI-Powered Foresight for Asset Owners™
Customized Intelligence for Every Asset Owner
The foundation of OLTAIX is a sovereign intelligence plane — a client-controlled operating layer that unifies data across custodians, managers, and providers, orchestrates specialized AI agents, and ensures that every output is evidence-backed, auditable, and aligned with fiduciary governance:
- Core Framework: Asset Owner (AO) Meta-Planner Agent, secure MCP Registry, unified RAG Corpus, and a Unified Data Lakehouse with governance, risk, compliance, and audit.
- Sovereign Control: Owned and operated by the asset owner, never by custodians, managers, or third parties.
- Intelligence Plane: The layer where data from all partners converges, agents orchestrate workflows, and decision intelligence is generated — always evidence-backed, auditable, and board-ready.
Agentic AI — The Workhorses
Each partner zone Asset Servicers (AS), Asset Managers (AM), External Data Providers (DP) operates its own Agentic AI cluster (AG)
- Definition: An agentic AI cluster is a team of specialized AI agents (planner, executor, critic) that carry out tasks such as reconciliations, compliance checks, or exposure analysis.
- Role in OLTAIX™:
- Servicer AGs: detect exceptions, reconcile breaks, monitor SLAs.
- Manager AGs: track exposures, mandate compliance, performance commentary.
- Provider AGs: process benchmarks, ratings, index events, ESG signals.
- Servicer AGs: detect exceptions, reconcile breaks, monitor SLAs.
- Why it matters: This design ensures autonomy—each partner controls its own AI agents—but all results are auditable and feed back into the Asset Owner’s Intelligence Plane.
MCP (Model Context Protocol) — The Gatekeeper
Every AG uses MCP registries to connect AI agents to partner systems
- Definition: MCP is a secure protocol for calling external tools, APIs, and data systems with governance controls.
- Role in OLTAIX™:
- Custody MCPs at Servicers for positions, pricing, exceptions databases.
- OMS/EMS MCPs at Managers for holdings, trades, exposure feeds.
- Data MCPs at Providers for indices, ratings, macroeconomic feeds.
- Custody MCPs at Servicers for positions, pricing, exceptions databases.
- Why it matters: MCP guarantees that all AI interactions respect trust boundaries, with least-privilege, token-scoped access and audit logs. No “black box” calls.
RAG (Retrieval-Augmented Generation) — The Truth Engine
Each partner and the Asset Owner itself has its own RAG index
- Definition: RAG grounds AI outputs in evidence by retrieving from a curated, local corpus before generating an answer.
- Role in OLTAIX:
- Servicer RAGs: procedures, pricing files, reconciliation history.
- Manager RAGs: mandate documents, factor notes, guidelines.
- Provider RAGs: index methodologies, ratings bulletins, data licenses.
- AO RAG: the authoritative, deduplicated corpus spanning all partners plus board policies, contracts, and minutes.
- Servicer RAGs: procedures, pricing files, reconciliation history.
- Why it matters: RAG enforces cite-or-fail: every output must trace back to retrieved evidence. This underpins fiduciary confidence and regulatory compliance.
LLMs (Large Language Models) — The Reasoning Brain
At the core, LLMs provide reasoning and explanation
- Definition: LLMs are generative models trained to understand and produce human-like text.
- Role in OLTAIX™:
- Summarize reconciliations into board-ready narratives.
- Explain exposure deltas or liquidity gaps in plain language.
- Translate agent outputs into actionable foresight for trustees.
- Summarize reconciliations into board-ready narratives.
- Why it matters: LLMs bridge the gap between raw data and human decision-making—giving boards explainable, contextual foresight instead of raw numbers.