AI Workbench
The governed toolkit built to take investment IP from idea to prototype to production – connected directly to live portfolio data, each client’s own entity models, and enterprise production infrastructure.
The prototype-to-production gap in investment management
AI has made the prototype phase faster than it has ever been. A quant or portfolio manager can build a genuinely powerful investment workflow in days. Leadership gets excited. And then it stalls.
Moving from ‘this works on my laptop’ to ‘this runs reliably across our platform, for multiple users, with auditable inputs and consistent outputs’ is not a small step. In investment management it is a risk with fiduciary obligations, regulatory scrutiny and client money on-the-line.
Generic AI development tools present challenges in that they are not connected to your portfolio data, your entity data model, or your production infrastructure. Every prototype you build in them must be traditionally rebuilt by your internal technology teams when it’s time to push live.
Jacobi’s powerful AI workbench
The Jacobi AI Workbench is a governed prototyping and development toolkit built for both coders and non-coders – taking proprietary investment IP seamlessly from idea to prototype to production. Connected directly to live portfolio data, your own entity data model, and production Infrastructure, the path to an institutional workflow is built into the environment itself, not bolted on afterwards.
Designed to empower the entire investment team, non-coders can assemble workflows using pre-built Component Libraries, while quant and technology teams gain Python-native access via the Jacobi SDK, custom Rules, Skills, Code Components, and a pre-configured MCP server.
Model-agnostic by design, it supports OpenAI, Anthropic, Mistral, or proprietary in-house models – providing the deterministic, auditable substrate that makes AI reasoning reliable in real investment workflows.
Features & Capabilities
Key features of the AI Workbench
AI Coding Resources
MCP server, Rules, Skills, and Code Components purpose-built for investment workflow development. Start from tested patterns, not blank files.
Component Library
Pre-built, reusable building blocks for portfolio analytics, risk calculations, attribution, and reporting. No rebuilding standard investment functions from scratch.
Model-Agnostic
Bring your own LLM or use Jacobi’s defaults. OpenAI, Anthropic, Mistral, the Workbench provides the investment-domain structure that makes any model useful.
Jacobi SDK
Python-native access to the full Jacobi data layer. Your investment team works in familiar tools, connected directly to live portfolio data and the entity model.
Governed Environment
Sandboxed environment with access controls, code screening, and audit logging. Build freely with zero risk to production data or live workflows.
One-Step Deployment
When a prototype is worth shipping, promote it to production directly from the Workbench. No rebuild. No re-mapping the data model. No starting over.
Case Study
Building and deploying a proprietary optimization model at scale
A leading global investment consultant had a proprietary optimization model that was outgrowing the tools it ran on. Simulation speeds were slow, iteration was difficult, and the model depended on a small number of individuals to operate it. This optimization model was key to their process and value generated for their clients.
Using the Jacobi AI Workbench, the client rebuilt (with support from Jacobi’s Forward Deployed Engineers) and extended the model in a governed development environment connected directly to live portfolio data. Iteration cycles shortened, simulation speeds improved, and the model was deployed into production infrastructure – accessible across the investment team, not locked to key individuals.
Our Clients Include
