This document describes concrete scenarios where Zixir is a good fit.

• Batch feature computation — Ingest events, compute features (e.g. with engine.list_sum, engine.dot_product), write to storage. One process, no DAG YAML or separate scheduler.
• Model scoring — Load weights, run engine.dot_product(weights, features) per request or batch; optionally call Python for post-processing. Elixir handles concurrency and restarts.
• Data validation and drift — Run validation and drift checks as steps in a pipeline; use built-in circuit breakers and retries when steps fail.

Why Zixir: One language and runtime; Zig for fast math; Python only where you need libraries.

• Multi-step agent workflows — Plan → call tools (APIs, DBs) → reason → act. Each step can be a Zixir workflow step with checkpoints and retries; state in memory or disk cache (no Redis).
• Orchestrating LLM + code + data — Elixir coordinates calls; Zig runs fast numeric or parsing steps; Python runs numpy/scipy or LLM SDKs when needed.
• CI/CD or ops automation — Scripts that run experiments, run models, or validate data; use Zixir’s resource limits and timeouts to avoid runaway jobs.

Why Zixir: Built for high-success agentic workloads; supervision and circuit breakers; optional observability and checkpointing.

• Recommendation / ranking — Per-request score with engine.dot_product(weights, features) (and similar) in Zig; optional Python for heavier ML libs; Elixir for concurrency and failure handling.
• Metrics and analytics aggregation — Stream of events → aggregate (sum, product, counts) in Zig; cache results (ETS/disk); dashboards or alerts read from cache.
• Pricing or risk calculations — Deterministic numeric formulas in Zixir (Zig for hot path); workflow steps and logging for auditability.

Why Zixir: Predictable performance from Zig NIFs; no GC on the hot path; Elixir for reliability.

• ETL / data prep — Load → transform (Zixir + engine.* + Python for pandas/numpy) → validate → write; run as one-off or on a schedule via mix or scripts.
• A/B and experimentation — Use Zixir’s experiment framework (stats, confidence intervals, winner promotion) in pipelines that prepare and analyze experiment data.
• Report generation — Pull data → compute aggregates (Zig) → optional charts (Python) → emit report; orchestration and retries in Zixir.

Why Zixir: Single stack for orchestration, math, and Python libs; less glue than “Python script + cron + Redis.”

• On-device or on-prem scoring — Compile Zixir to a native binary (mix zixir compile); run scoring or small pipelines without a heavy runtime; Python only if the environment has it.
• Raspberry Pi / gateways — Lightweight binaries; Elixir’s supervision for long-running or periodic jobs.

Why Zixir: Small binaries (Zig), no JVM; optional Python when needed.

For setup and capabilities, see README.md and PROJECT_ANALYSIS.md. For “why Zixir vs others,” see WHY_ZIXIR_AND_COMPARISON.md.