Modern architecture for investment research, in practice.
Reckoning Machines builds secure, auditable, reproducible AI-augmented research and decision systems, designed for environments where errors are expensive.
Our work is informed by experience on both sides: portfolio management and systems engineering. That shapes responsible exploration, experimentation, and scalable workflows that preserve clarity, auditability, and judgment.
People, leveraged by technology
Architecture designed to scale experienced judgment: machines handle enumeration, analysis, and reporting, while investors retain responsibility for decisioning rules, evaluation, and feedback.
Explainable agentic and machine-learning systems
Machine learning and agentic components used to explore, evaluate, and synthesize analysis within defined guardrails.
Enhanced decision processes
Investment reasoning made explicit as structured workflows—assumptions, dependencies, and decision points are encoded so they can be executed, audited, backtested, and improved over time.
Business impact
- Scaled research processes without loss of rigor
- Reproducible decision logic across teams
- De-risked LLM usage through governed execution
- Observable research workflows for compliance and review
Core principles
- Deterministic execution: Research is treated as a sequence of governed steps. Given the same inputs, the same execution path and outputs can be reconstructed, inspected, and compared.
- Control plane vs. data plane: Execution logic, dependency gating, and provenance live in a control plane, while data access and slicing are handled independently.
- Explicit decision structure: Assumptions, prerequisites, and decision points are made concrete rather than implicit.
- Failure as signal: Missing data, invalid assumptions, or broken dependencies halt execution early and visibly.
- Systems that learn over time: Research systems are designed for iteration and review, enabling post-hoc analysis, comparison across runs, and continuous improvement.
Representative work
The following examples are illustrative of the kinds of systems and architectural problems this work draws from. They are not packaged products or transferable platforms.
- Machine-learning–driven portfolio research: systematic research workflows combining traditional financial signals with modern modeling techniques, designed to remain interpretable and reviewable.
- LLM-assisted portfolio explainability: research layers that augment investment analysis with structured, natural-language explanations intended for PM review.
- Distributed spreadsheet computation: architectures for orchestrating and observing large numbers of spreadsheet-based models as part of governed research processes.
- Model routing and orchestration: systems that coordinate multiple analytical and language models under explicit execution and evaluation constraints.
- Deterministic research execution graphs: directed, dependency-aware research workflows designed for replay, auditability, and controlled experimentation.
- PM-oriented research interfaces: interfaces and review tools designed to fit how portfolio managers actually work.
How we work
Engagements are exploratory and architecture-focused. The emphasis is on understanding how research is
actually conducted, where hidden state accumulates, and how execution discipline can be improved without
disrupting existing workflows.
This work reflects experience designing and operating internal research systems inside investment
organizations. It is not a packaged platform or hosted service, and implementations are
organization-specific.
Contact
If you are responsible for research infrastructure, portfolio systems, or decision governance, email reckoningmachines@gmail.com.