Agent to Agent (A2A) vs. Model Context Protocol (MCP)
Agent‑to‑Agent (A2A) vs. Model Context Protocol (MCP) — what’s the difference, and how to use them in asset & wealth management. Summary MCP standardises how an AI agent connects to tools and data (filesystems, databases, SaaS) through a client‑server protocol, solving the “N×M” integration problem and enabling secure, governed access to context. Think vertical integration:…
Read MoreData Concepts for Agentic AI Process Automation & Workflows
The infin8 simple guide to Data Concepts for Agentic AI Process Automation & Workflows Let’s start with some definitions: Ontology A shared definition of the core business concepts and how they relate. Gives AI clear concepts and relationships to reason about. Entity A real-world object or concept that data represents. Helps AI distinguish persona, products,…
Read MoreFrom Pilots to Platforms with Gen-AI for investment research, client communications and investment performance reporting commentary.
Summary; Pilots to Platforms with Gen AI and Agentic AI In asset and wealth management, AI has lingered too long in proofs‑of‑concept—interesting demos that rarely touched investment research, quarterly investment snapshots, factsheets, RFP responses, or client reporting. That phase is ending. With governance‑first platforms, multi‑agent orchestration, and human‑in‑the‑loop controls, ‘agentic AI’ is crossing the chasm…
Read MoreLTM vs LLM: The Essential Guide for Financial Services Leaders
Here’s a clear explanation, grounded for financials services practitioners, of what an LTM (Large Tabular Model) is and how it differs from the development path of LLMs for tables and Gen‑AI so far. What is an LTM? An LTM (Large Tabular Model) is a type of AI foundation model designed specifically for structured data,…
Read MoreWhy Context Augmented Generation (CAG) Is Becoming Essential for Financial Services AI
Financial‑services firms are moving fast to embed AI into high‑value workflows — from investment research to compliance, reporting and client engagement. But as models become more powerful, the real differentiator isn’t just capability. It’s context. Context‑Augmented Generation (CAG) is emerging as a foundational architecture for financial‑services AI. It enhances the model’s output by integrating domain‑specific…
Read MoreAgentic AI in Financial Services: From Pilots to Platforms
Executive takeaways for asset & wealth management teams; Investment marketing communication, research and client communication Agentic AI is moving from pilots to platforms. Microsoft, Google, Anthropic and Cohere have introduced enterprise‑grade orchestration, governance and multi‑agent capabilities designed for regulated environments. These are now viable for client reporting, performance attribution, and commentary workflows—provided you pair…
Read MoreAgentic Automation in Financial Services: Why Orchestration Is the Real Challenge
Agentic AI—systems where autonomous agents handle tasks—has generated huge excitement. But the biggest obstacle isn’t what most people expect. It’s not about reasoning power, speed, or access to tools. The real bottleneck is orchestration: how we coordinate multiple agents to work together effectively. What’s Wrong with Current Approaches? Most teams use simple strategies like: Serial…
Read MoreShadow Agentic AI: Rethinking Transformation in Investment Management
Transformation and Change Management The investment management industry has long been under pressure to evolve. Fee compression, rising operational costs, and increasing client expectations have driven firms to seek efficiencies through automation. This trend is not new—banking, financial services, and insurance (BFSI) have historically been early adopters of technological innovation. Yet, as the industry enters…
Read More3 Powerful Use Cases of Gen AI in Asset Management: Portfolio Strategy, Attribution & Marketing
A summary of 3 powerful use cases of Gen AI in Asset Management: Portfolio Strategy, Attribution & Marketing. Gen AI role in portfolio construction and research within financial services asset management. We focus on how our infin8 capabilities can be practically embedded into your workflow. Portfolio Strategy Optimization Using Unstructured Data Signals In today’s investment…
Read MoreHarnessing Enterprise Data Management and AI in Financial Services
Enterprise Data Management and AI in the financial services industry is undergoing a profound transformation driven by data proliferation, regulatory complexity, and the demand for real-time insights. Innovative platforms enable financial institutions to adopt a “one-to-many” approach to artificial intelligence (AI). This strategy involves deploying centralized AI and data infrastructure that can serve multiple business…
Read More