About
Jacob builds institutional intelligence infrastructure: the memory and coordination layer that protects, compounds, and activates the knowledge inside a firm. His background in quantitative finance, organizational leadership, and product delivery shaped a practitioner's understanding of how firms actually lose knowledge and what it costs them. Project Symmetry is the proof of concept.
What We Build
Project Symmetry is a private AI infrastructure platform that functions as a digital firm. A coordinated fleet of specialized agents handles research, analysis, strategy, and execution, working together on complex problems across sessions with full institutional memory of everything the firm has ever learned. The platform is designed so that any agent type can be built and deployed for any domain. The architecture is operational; agent fleet expansion and client-facing deployment are on the roadmap.
The platform's core differentiator is a five-stage memory pipeline grounded in cognitive science. Each experience moves through a deliberate encoding and consolidation process before becoming part of the agent's permanent knowledge base.
Engrams are stored in a temporal knowledge graph: a web of typed connections rather than a flat list. When a task begins, the system traverses the graph from the current context outward, surfacing only the knowledge with direct relational bearing on this conversation, this person, this domain. As the archive grows, token cost stays bounded. The firm's knowledge isn't retrieved. It's navigated.
Picture navigating by the stars rather than searching aimlessly throughout the sea. Your destination is inferred by fixed points above and their relationships to each other, to the horizon, and to time itself. And you have the sextant and map. Engrams work the same way. When a task begins, the system doesn't search the entire archive. It reads the sky from wherever it stands.
The platform deploys specialized functions configured entirely around the firm's domain. Each operates within a defined scope, contributes to shared institutional memory, and coordinates through a structured communication protocol. The architecture is extensible by design: any function can be built, deployed, and integrated without rebuilding the underlying intelligence layer.
The system is built on the premise that intelligence without accountability is noise. Every output passes through a structured decision hierarchy: signals are sequenced, indicators are weighted against each other, and conflicting data triggers circuit breakers rather than cascade failures. Quality gates and contrarian reviewers challenge every output before it reaches you. Variant strategies run continuously in shadow evaluation against live decisions, and parameters that outperform are promoted automatically. The result is a platform that improves with every cycle it runs. Every output is auditable. Every parameter is reversible.
The trading system runs Bayesian parameter optimization, hybrid decision architecture, and a two-tier risk governance framework against 25 years of historical market data. Fully autonomous, daily operations.
$500K starting capital. Paper trading through February 2026. Live deployment began March 2026.
Most tools in this space are built to do the work for you. The result is faster output, but a quieter, duller version of yourself.
This is a partner built around how you think, what you dream, and how you strive to be heard. An editor offering just the right contrarian view you never knew you needed. Built to learn your instincts, your tendencies, the places where old habits masquerade as choices. A simple language model is a blank canvas. This is a world you've painted over a lifetime.
A collaborator that helps you be the best version of you.
Methodology
Most AI systems are built by engineers who have spent their careers inside the discipline. Project Symmetry was built by someone who spent his career outside it: through intelligence operations, organizational leadership, quantitative finance, and product delivery, applying the same analytical framework to a new domain.
The cognitive science grounding (Tulving, Tonegawa, Müller and Pilzecker) was validated after the architecture existed, not before. The goal was a system that remembered the way organizations actually need to remember. The research confirmed the design.
The core discipline is applied epistemology: how information is collected, validated, weighted, and stored under conditions where error has consequences. Those principles transfer directly to the problem of what AI agents should know, how confidently they should know it, and how much to trust what they produce.
Contact
J. I. Ashley Consulting is not currently accepting new clients. For partnership inquiries, press, or investment conversations: