Deployments

Production deployment guidance for Fund Analyst Intelligence: environments, releases, observability, safe reruns, and operational controls.

Deployments

Fund Analyst Intelligence is a scheduled production workflow.
Deployment must therefore support reliability, traceability, and controlled change.
The goal is stable monthly operations with safe failure behaviour.

This page outlines a production-minded deployment approach.
It focuses on environments, release discipline, observability, and runbooks.

Deployment objectives

A production deployment should ensure:

• separation between development, staging, and production
  
• controlled releases with rollback capability
  
• safe execution of monthly cycles and reruns
  
• strong observability and actionable failure modes
  
• secure handling of artefacts, reports, and evidence packs
  
• clear operational ownership and incident response

Environments

Development

Used for feature work and local testing.
May use synthetic or redacted data.
Should not hold sensitive production artefacts.

Staging

Used for end-to-end validation before production release.
Should mimic production configuration.
Should run at least one full cycle on a representative dataset.

Production

Runs scheduled monthly cycles and real workflows.
Must enforce strict access controls and retention policies.
Must preserve audit trail integrity.

Environment separation is a governance requirement.
It prevents accidental leakage and untracked changes.

Release discipline

Versioning

Releases should be versioned and traceable.
API contracts, templates, rulesets, and policies should be versioned explicitly.

Change control

Production changes should be deliberate.

Minimum expectations:

• changelog entry per release
  
• staging verification evidence
  
• backward compatibility checks for APIs and exports
  
• template diffs reviewed for reporting outputs
  
• policy changes recorded with rationale

Rollback plan

Every release should have a rollback route.
Rollbacks must preserve cycle record integrity.
No rollback should corrupt the audit trail.

Scheduling and runtime

Fund Analyst Intelligence runs scheduled cycles.
The scheduler should support:

• monthly cadence per portfolio
  
• controlled ad-hoc cycles for urgent updates
  
• reruns with explicit version identifiers
  
• prevention of double-runs for the same period

A cycle should never be ambiguous.
Cycle ids and run states must be explicit.

Artefact and report storage

Artefact storage

Artefacts are sensitive and must be controlled.

Operational expectations:

• immutable storage for ingested artefacts
  
• version metadata and hashing
  
• controlled access and scoped permissions
  
• retention and deletion policies as configured

Report storage

Reports are production artefacts.

Operational expectations:

• stable storage locations
  
• publication gates tied to approval states
  
• retention policies aligned with governance
  
• export logs for downstream distribution

Evidence packs must remain accessible as long as the reports remain defensible.

Observability

Production success depends on visibility.
Observability should include logs, metrics, and traces.

Structured logging

Logs should include:

• cycle_id, fund_id, and portfolio_id
  
• stage name (ingest, extract, validate, compare, report)
  
• error classification
  
• user id for approval actions
  
• request ids for API calls

Metrics

Track:

• cycle durations and throughput
  
• exception counts by category and severity
  
• evidence coverage rates
  
• failed job counts and retry rates
  
• time-to-close for follow-ups
  
• alert volume and acknowledgment latency

Alerts for operators

Alert operators on:

• failed or stalled cycles
  
• missing required artefacts
  
• repeated extraction or validation failures
  
• abnormal exception volume spikes
  
• publication attempts without approval

Observability is part of governance.
It also reduces operational cost.

Safe failure and reruns

Fail explicitly

If a stage fails, the system should:

• stop publication
  
• preserve cycle state as failed or incomplete
  
• keep the input inventory and intermediate artefacts
  
• surface actionable error codes and diagnostics

Idempotency

Ingestion and processing should be idempotent.
A rerun should not duplicate artefacts or create ambiguous state.

Controlled reruns

Reruns should be explicit events.

Rerun requirements:

• new run id linked to the original cycle
  
• preserved history of prior failed attempts
  
• clear indication of what changed (inputs, policy version, code version)
  
• no overwriting of approved snapshots

This protects auditability and supports incident response.

Backups and retention

Production operations should define:

• backup frequency for structured data and metadata
  
• retention policy for artefacts, logs, and reports
  
• archival procedures for older cycle records
  
• deletion policy aligned with compliance requirements

Retention is not only storage.
It is part of defensibility.

Runbooks (minimum set)

A production deployment should include runbooks for:

• failed cycle recovery
  
• missing or stale input handling
  
• conflicting source resolution workflows
  
• report regeneration and reproducibility checks
  
• access control incidents
  
• policy change rollout and rollback

Runbooks reduce downtime.
They also reduce institutional dependency on specific individuals.

Operational roles

Define ownership clearly:

• platform owner: accountable for production operations
  
• operator: runs cycles and triages exceptions
  
• reviewer: resolves and approves outputs
  
• admin: manages policies and access
  
• incident owner: handles failures and escalations

The tool supports these roles.
The organisation must assign them.

Definition of done

Deployment is production-grade when:

• environments are separated and configured consistently
  
• releases are versioned, tested, and rollbackable
  
• scheduled cycles run reliably with clear state transitions
  
• artefacts and reports are stored immutably with controlled access
  
• observability provides actionable metrics and alerts
  
• reruns are controlled and auditable
  
• runbooks exist for common incidents

This is the operational foundation for Fund Analyst Intelligence at scale.