Content Observability · AI / ML

Sharper monitoring and smarter workflows — forecast, not guessed.

Reveille AI/ML turns your operational data into intelligent, decision-ready context. It continuously analyzes signals across content, automation, and document processes — detecting anomalies, predicting risk, and delivering trusted operational truth to your AI agents through Reveille MCP. It is the predictive layer of Content Observability: faster diagnosis, smarter automation, and AI-driven decisions grounded in what is really happening.

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Monitor. Analyze. Connect.Dashboards▾CONTENT MANAGEMENTOverviewMonitoringDocument HealthPerformanceStorageSystem Dashboards▸Reports▸Administration▸Logs▸User SettingsReveilleAboutFeedbackHelpContent ManagementMonitoring13/15 healthy · 2 healingVisuals ▾⟳HEALTHY13 /15 servicesWARNINGS2 database, performanceTHROUGHPUT12.6k docs/hr · last hrAVG LATENCY89 ms · p50DatabaseApplicationServerClientSearchCaptureSystem ProcessingConnectivityPlatformWeb ClientPerformanceUsageSecurityUser ActivityContent ManagementHealthyHealingCore service
The AI operations gap

AI can only act on what it can see. Most of your operations are invisible to it.

Models and agents reason brilliantly over the data they are handed — and stay blind to everything they are not. The real state of your ECM, IDP, and automation workflows is exactly the operational context they are missing, and exactly what static thresholds are too brittle to surface.

01

Static thresholds age badly

A limit set for last quarter’s volume fires constantly on this quarter’s. Brittle thresholds bury the real issues under a steady stream of false alarms.

02

Failures stay invisible until they’re expensive

A drifting OCR confidence score or a slowly deepening queue rarely trips a fixed alert. By the time it does, the claim is late — and the model has already acted on bad input.

03

AI is missing operational truth

Copilots and agents reason over content, not over the health of the systems producing it. Reveille MCP supplies the missing operational context, normalized and real-time.

Dynamic thresholding

Accurate thresholds that move with your workload

Static limits break the moment your volume shifts. Reveille AI forecasts what normal looks like and adapts the threshold to it — in real time.

  • Adaptively set evaluation thresholds to keep monitoring accurate and valid as workloads change
  • Minimize false positives with forecast bands that move with normal fluctuation, not against it
  • Account for periodic patterns — daily, weekly, or monthly — to tell true anomalies from expected variation
  • Cut alert noise so teams stay focused on real service-level issues instead of statistical wobble
MonitorsAI / MLMetricsAlertsDashboardsDynamic ThresholdingCapture throughput · forecast vs. actualAnomaly flaggedForecast bandActualAnomaly4.2%Forecast error (MAPE)↓ 71%False positivesSeasonalDaily / weekly / monthly
Predictive analytics

Forecast failures before they reach your users

Reveille AI learns from historical metric data to predict where a process is heading — and flags the deviation early.

  • Open-source Prophet and Neural Prophet models (both MIT-licensed), applied with an unsupervised learning approach
  • Upper and lower forecasted values calculated per metric-and-attribute combination from your own history
  • Forecast accuracy tracked with MAPE, plus a subsystem to configure, measure, and review every threshold
  • Runs behind your firewall on a single or dedicated server, or in AWS, Azure, or GCP — models are never modified or locked in
MonitorsAI / MLMetricsAlertsDashboardsPredictive ForecastWorkflow queue depth · 14-day projectionforecast →historyProphettrained · MIT-licensedNeural Prophettrained · MIT-licensed3.8%Forecast accuracy (MAPE)
Conversational observability

Ask your AI agent how your content operations are really doing

With Reveille MCP, your AI assistant answers questions about live operations in plain language — grounded in what is actually happening across ECM, IDP, and automation.

  • Ask in natural language — “How is the IDP capture pipeline doing right now?” — and get an answer grounded in live metrics, events, and SLAs
  • Feed real-time, normalized observability data from ECM, IDP, and automation directly into AI agents using Reveille MCP
  • Combine metrics, events, dependencies, and service-level signals to dramatically reduce MTTD and MTTR
  • Move copilots and agents from passive analysis to proactive decisions grounded in real operational state
ClaudeReveille MCP connectedHow is our IDP capture pipeline doing right now?ClaudeUsed Reveille MCP · live contextCapture looks healthy — 1,240 docs/hr, inside the forecast band.One thing to watch: OCR confidence on the ACME batch dropped to 82%about 12 minutes ago — just below the dynamic-threshold lower bound.Workflow queue depth is normal, and there are no SLA breaches in the last 24h.Want me to open a ticket for the ACME batch and watch it?Reply to Claude…
Why Reveille

It only works if it always works.

AI is only as good as the operational data beneath it. Reveille assures the content layer your automation and your models depend on — so AI workflows don’t fail quietly underneath the model. Platform SLA is not workflow SLA; Reveille measures the difference.

Part of a broader platform

One observability layer across every platform you run

Reveille Monitoring spans every major Enterprise Content Management (ECM), Intelligent Document Processing (IDP), and automation platform — so every signal feeds the same console as the rest of your stack.

Hyland OnBase · Alfresco OpenText Documentum · Capture IBM FileNet · Datacap · CMOD ABBYY FlexiCapture · Vantage Tungsten TotalAgility · Capture UiPath RPA assurance Microsoft SharePoint · M365 Box Content cloud
The platform

Explore the Reveille Observability Platform.

AI/ML is one capability of a single platform. Eight work together to keep every ECM, IDP, and automation workflow observed, assured, and self-healed.

Monitoring

1,000+ prebuilt tests for ECM, IDP, and automation service-level problem areas.

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Metrics

See exactly how your automation is actually performing.

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Self-Healing

Automated remediation actions for uninterrupted processes.

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You are here

AI / ML

Dynamic thresholds and forecasting for accurate monitoring and smarter workflows.

Alerts

The earliest-warning system for document and content processes.

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Dashboards

Visualize and quickly diagnose platform health.

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Infrastructure

A complete view of your automation ecosystem and every system it depends on.

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Integration

Seamless connection to the enterprise tools your team already uses.

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Questions

Reveille AI/ML, answered

What is Reveille AI/ML?
Reveille AI/ML is the predictive layer of Reveille’s Content Observability platform. It applies machine learning to your operational data to set dynamic thresholds, forecast risk, and detect anomalies across ECM, IDP, and automation workflows — and it delivers that operational context to AI agents through Reveille MCP, so monitoring stays accurate and automation stays grounded in what is really happening.
What machine learning models does Reveille AI use?
Reveille employs the open-source Prophet and Neural Prophet models (both MIT-licensed) with an unsupervised learning approach. The models are not modified by Reveille, and forecast accuracy is tracked with MAPE (Mean Absolute Percentage Error).
What is dynamic thresholding?
Dynamic thresholding uses historical metric data to forecast upper and lower bounds for each metric-and-attribute combination, replacing static thresholds that break when workloads shift. It accounts for periodic patterns — daily, weekly, and monthly — to distinguish true anomalies from expected variation, cutting false positives and alert noise.
Does Reveille AI/ML run in the cloud?
Yes. The machine learning process can run behind your firewall on a single Reveille server for testing, on a separate machine learning server for production volume, or in AWS, Azure, or GCP — any environment capable of running the Python-based Prophet or Neural Prophet models. Reveille AI is a separately licensed feature of the Reveille platform.
What is Reveille MCP?
Reveille MCP delivers trusted, normalized operational context to AI agents using the Model Context Protocol. It lets an AI assistant answer natural-language questions about live operations — such as how the IDP capture pipeline is performing right now — grounded in real metrics, events, dependencies, and service-level signals from ECM, IDP, and automation. The result: faster diagnosis, lower MTTD and MTTR, and agents that move from passive analysis to proactive decisions.
Does dynamic thresholding require code?
No. Reveille provides a comprehensive subsystem to configure, maintain, measure, and review dynamic threshold forecasting — no custom scripting required.
How is Reveille AI/ML different from generic anomaly detection?
Generic anomaly detection watches infrastructure and code metrics. Reveille AI/ML is application-aware: it forecasts the content-layer behavior — capture throughput, queue depth, repository and workflow performance — that ECM, IDP, and automation actually run on, and that infrastructure tools cannot see.
Does Reveille AI/ML replace my monitoring?
No. It makes monitoring sharper. Reveille still monitors, alerts, self-heals, and reports on your content operations; AI/ML adds the predictive thresholds and forecasting that reduce noise and surface issues earlier.
Get started

The content layer is where your business runs. Reveille makes sure it holds.

See how Reveille keeps the invoices, claims, audits, and customer documents your content operations run on always-on — with the evidence to prove it.

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