The method should be inspectable
The Methods Library explains the research and engineering patterns that inform Eigenvalue Systems projects. A citation is not proof that a system was built. It is the technical foundation for understanding why a method works, where it can fail, and how it should be controlled.
Retrieval and grounded generation
Retrieval-augmented generation fetches relevant material from a controlled source before a language model answers, so responses are grounded in your documents rather than in the model's general training.
Limitations: Retrieval quality, source quality, permissions, and answer evaluation remain separate problems. Transformers made these systems possible; they did not make outputs automatically factual.
Multimodal document intelligence
Modern document models read text, page layout, and visual structure together, which is how invoices, statements, and forms carry meaning that plain text extraction loses.
Limitations: OCR errors propagate into downstream logic. OCR-free approaches are alternatives with their own failure modes, not universal replacements. Production systems still need validation rules, confidence handling, and human review.
Forecasting and model validation
Forecasting methods range from driver-based accounting logic to statistical and neural approaches; the choice depends on the data, the horizon, and whether meaningful drivers exist.
Limitations: A forecast is a decision tool with an error range, not an accounting fact. Neural methods such as N-BEATS and DeepAR can excel on rich series and fail on short, structural, driver-dominated business data.
Anomaly detection
Anomaly detection surfaces records that deviate from learned patterns, which makes it a triage tool for reconciliation, transaction review, and data quality.
Limitations: An anomaly is a statistical statement, not an accusation. Detection surfaces unusual records; it does not independently prove fraud, error, or misconduct.
Model explainability
Explanation methods attribute a model's prediction to its input features, which is a requirement for using models anywhere a decision must be defended.
Limitations: SHAP-style attributions describe feature contributions within the model. They do not prove causation, and they can mislead when features are correlated.
Controlled agents
Agent-style systems interleave reasoning with tool use. Making them safe for real operations is a permissions, validation, and audit problem, not a prompting problem.
Limitations: Risk frameworks are references, not certifications; Eigenvalue does not claim NIST compliance or OWASP certification. Prompt injection, excessive agency, and data exposure remain live risks that architecture, not vigilance, must contain.
Production ML engineering
A model is a small component of a production system; data dependencies, monitoring, tests, and ownership determine whether the system keeps working after the demo.
Limitations: A notebook or demo is not a production ML system, and Eigenvalue does not describe one as such. Offline accuracy says little about behavior under real, changing data.
Bring me the bottleneck
Describe the process, system, or recurring failure that should not still require this much human effort. You will receive a response from Jason, the person who will scope the work.