Decision Intelligence

ARGO — Decision Intelligence for Airline Operations

A sanitized case study of a decision-intelligence platform for operational planning, recovery workflows, resource allocation, and role-based visibility in airline operations.

Production-inspired / sanitizedMay 30, 2026

ARGO is a decision-intelligence platform for airline operational planning, resource allocation, recovery workflows, and operational visibility.

ReactFastAPIPostgresDockerRulesScoringRole-Based Workflows

Problem

Airline operations require fast decisions under changing constraints: staffing, resources, timing, disruptions, dependencies, and competing priorities. The hard part is not only knowing what happened; it is deciding what to do next with enough context and traceability.

Users

The system was shaped around operations users, terminal teams, resource planners, and business stakeholders who needed visibility into plans, constraints, and recommended actions.

My role

Constraints

Operational data could be messy, delayed, or incomplete.
Users worked under time pressure and needed explainable states.
Different roles needed different levels of visibility and action.
Recommendations could support decisions but should not automatically execute operations.
The system needed to preserve decision traceability.

System built

ARGO brought operational data, rules, scoring, recommendation states, scenario comparison, dashboards, and role-based workflows into a single decision-support surface.

ARGO decision-intelligence architecture

01Operational data
02Validation
03Constraint layer
04Rules and scoring
05Recommendation engine
06Scenario comparison
07User workflow
08Decision trace
09Outcome capture

Architecture snapshot

The architecture separated facts, constraints, recommendation logic, and user workflow. That separation made it easier to explain why a recommendation appeared and where the underlying data came from.

AI/ML approach

ARGO used decision-intelligence patterns rather than opaque automation: rules, constraints, scoring, recommendation states, and scenario comparison. The model of the workflow mattered more than a single predictive model.

The system was designed to become more model-ready over time, but the first priority was useful decision support with clear traceability.

Product decisions

Show recommendation states clearly instead of hiding uncertainty.
Keep humans in control of operational decisions.
Explain constraints and data quality issues near the recommendation.
Preserve audit trails for review and learning.
Design views around real roles, not generic dashboards.

Evaluation & reliability

01 · Grounding

Recommendations were tied back to operational facts, constraints, and visible scoring inputs.

02 · Failure modes

Risks included stale data, missing constraints, edge cases, overconfident recommendations, and role confusion.

03 · Human review

The platform supported decision-making but did not automatically perform operational actions.

04 · Operational risk

The design assumed the system could be incomplete or temporarily unavailable and kept users in the loop.

05 · Adoption

Usefulness depended on actionability, trust, speed, and whether the tool reduced coordination load.

06 · Production readiness

The system needed role-based workflows, audit trails, deployment discipline, and rollback options.

Impact

Planning visibility

Observed

Improved

The platform created clearer operational visibility, faster planning review, and more explicit decision states.

Limitations

What I learned

Decision intelligence works best when it respects how people already make decisions under pressure. The product must reveal constraints, explain recommendations, and make the next action easier.

What I would improve next

Add simulation for disruption and recovery scenarios.
Introduce an optimization engine where constraints are stable enough.
Capture outcomes for recommendation quality review.
Improve monitoring around stale inputs and constraint violations.
Deepen integrations with source systems.