Applied AI and Operational Systems
We build the path once
Eigenvalue Systems designs automation, quantitative models, data infrastructure, document intelligence, and internal applications for operations that are too important to remain manual.
Built for finance, operations, manufacturing, property, professional services, and teams working between systems that were never designed to cooperate.
Every company has a second operating system: the work carried through exports, inboxes, spreadsheets, reconciliations, and decisions that never became software. Eigenvalue Systems turns that hidden system into something reliable.
Six disciplines. One standard
Beyond one-off scripts: monitored workflows with retries, exception queues, permissions, and a record of every action.
Operational automation
Workflows that monitor, decide, act, retry, escalate, and document themselves
- Inbox and folder monitoring
- Approval and exception queues
- Scheduled and event-driven jobs
Data systems and integration
Pipelines, mappings, databases, and control totals that keep systems aligned
- ETL between platforms
- Migration validation
- Reporting layers and Power BI models
Quantitative and financial models
Forecasting, scenario analysis, and decision models with visible assumptions
- Driver-based forecasting
- Scenario comparison
- Cash and balance-sheet models
Applied AI and document intelligence
Retrieval, extraction, classification, and controlled model use with review built in
- Document extraction with review queues
- Source-grounded retrieval
- Anomaly surfacing
Internal applications
Purpose-built interfaces and operating tools for work generic software handles poorly
- Review portals
- Operational control rooms
- Client onboarding flows
Finance and control systems
Reconciliation, close, receivables, reporting, evidence, and auditability
- Reconciliation with evidence
- AR and collections workflows
- Close and reporting controls
Systems already carrying weight
Statuses stated plainly. No metric appears unless it is confirmed.
Quantitative Forecasting and Scenario Engine
A driver-based financial model connecting account-level assumptions, operating drivers, entity behavior, and balance-sheet mechanics into one self-balancing forecast environment.
Financial Operations Platform
A full financial operating system spanning source intake, classification, rules, exception review, reconciliation, close controls, reporting, and a traceable record of how each result was produced.
Accounts Receivable and Collections Automation
A controlled receivables workflow that monitors open balances, organizes outreach, records responses, escalates exceptions, and writes status back to the source system.
Document Intelligence and Controlled Extraction
A pipeline that reads invoices, statements, forms, scans, and email attachments, converts them into structured data, validates the result, and routes uncertain cases to review.
Controlled AI Assistant and Engineering Foundry
A consent-aware assistant architecture connecting communication, memory, tools, and model orchestration, with explicit human takeover, permission boundaries, and staged execution.
Built from both sides of the process
Eigenvalue Systems is led by Jason Chaidez, Founder & Principal Engineer, whose work sits at the intersection of finance, operations, automation, and software engineering. He builds systems after understanding how the work is reconciled, approved, audited, and used, not merely how it appears in a requirements document.
Jason currently builds automations, internal tools, and financial and quantitative models inside aerospace. Earlier work across accounting, property, nonprofits, insurance, real estate, and financial operations exposed the same pattern repeatedly: important processes held together by manual effort and institutional memory.
Eigenvalue Systems exists to turn those processes into durable operating infrastructure.
Founder-led. The person who scopes the work is accountable for the build.
- Finance & accounting operations
- Forecasting, scenarios & model validation
- Workflow automation & internal tools
- Multi-system data & reconciliation
- Document intelligence & controlled AI
- Internal systems in aerospace
The method should be inspectable
AI and quantitative systems should not be black boxes attached to a process. The Methods Library explains the research, engineering patterns, controls, and limitations behind the systems Eigenvalue builds.
US-based. Built to work across borders
Remote engagements are available in selected international markets, beginning with Hong Kong, Japan, and the United Kingdom. Market pages state the delivery, language, currency, and compliance boundaries before the sales conversation begins.
No surprises, by design
A written scope, delivery plan, and pricing structure before the build begins. For uncertain integrations or messy data, a paid discovery phase sizes the problem before either side commits.
Map the operation
Understand the actual work, data, exceptions, approvals, and failure points.
Define the control boundary
Decide what can be automated, what needs approval, and what evidence must remain.
Build and verify
Implement, test calculations and failure paths, and compare outputs to source truth.
Deploy and transfer
Documentation, monitoring, support, and handover. The agreed source, documentation, and operating rights are yours. You own it.
Bring me the bottleneck
Describe the process, model, 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.