Atoti for Buy Side
Real-time total portfolio intelligence. Built for AI agents, not retrofitted with AI features. No batch delays. No spreadsheet risk. Your IP, your LLM, your control.
Atoti for Buy Side unifies performance, risk, sensitivity and liquidity across every asset class and investment type. It gives your AI agents the real-time, deterministic context they need to act on it. Bring your own approved LLM. Keep your data, models and IP entirely under your control.

From Fragmented Data to AI-Native Total Portfolio Intelligence
Built for decision makers on the Buy Side
Portfolio Managers
Risk Managers
Performance Analysts
Quants and Developers
CIOs and COOs
Infrastructure cost
The Buy Side has an analytics problem. And it's getting worse.

No single view across public and private investments.

Batch processing creates dangerous blind spots.

Fund-of-fund look-through takes hours, not seconds.

Spreadsheets introduce risk at every level.

AI tools lack the financial context to be genuinely useful.

Most AI tools are retrofitted, not AI-native.
Atoti was built to solve these problems.
Request a DemoAI Agents Are Only as Good as the Platform They Run On
Real-time risk intelligence
Open and extensible
Domain-specific deterministic expertise
IP protection as a first principle

From Hours to Seconds: Limits Breach Remediation
Intra-day, volatility spikes and a portfolio approaches a sector limit. In most firms today, what happens next plays out across multiple systems and teams over hours or days: a manual limits report flags the breach, a risk analyst pulls market news and internal research, runs scenario analysis offline, then takes action.
With Atoti Intelligence, the same workflow runs in seconds, inside a single source of risk. The Limits agentic workflow auto-detects the approaching breach. The Market Risk agent gathers internal research and current market context.
The Scenario Analysis workflow generates potential tail-risk scenarios on live portfolio data. The agent presents remediation options with a full audit trail, and your portfolio manager decides, with the deterministic numbers and the qualitative context already in front of them.This is what AI-native means in practice: real-time context, domain-specific agents and your own LLM, working together on data that never leaves your environment.
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