{"apiVersion":"1","solution":{"slug":"scheduled-ai-investigations","title":"Scheduled AI investigations and KPI monitoring","summary":"Keep a defined business question under recurring review. Compare products that repeat AI analysis on a schedule and return findings with context, then check how your team can inspect and follow up each result.","reviewedAt":"2026-10-10","canonicalUrl":"https://igamingdiscovery.com/solutions/scheduled-ai-investigations","jsonUrl":"https://igamingdiscovery.com/api/discovery/v1/solutions/scheduled-ai-investigations","operator":"AND","capabilityIds":["scheduled-ai-investigations"],"relatedTypes":["business-intelligence","conversational-analytics","player-analytics"],"answer":"A scheduled AI investigation repeats a user-defined analytical question or investigation at an agreed frequency and returns findings with context. It goes beyond sending an existing dashboard, checking a numeric threshold or refreshing a risk score. The user defines the recurring work; a system choosing its own investigative topics belongs to a separate autonomous-analysis scope.","qualification":["The user can define the analytical question or investigation to be repeated, including a meaningful business scope.","The named product explicitly supports running that AI analysis on a recurring schedule and returning findings with context.","A platform qualifies when the relevant module is explicitly included. Separate conversational products do not inherit its recurring-investigation capability."],"caveats":["A scheduled PDF, fixed dashboard subscription, threshold alert or continuously refreshed predictive score is insufficient evidence on its own.","A run schedule is not a source-freshness guarantee. Incomplete feeds or changing definitions can affect the meaning of a recurring result.","Receiving a finding does not prove its cause, delivery to every intended recipient or completion of an operational response. These require separate checks."],"sections":[{"id":"recurring-question-scope","title":"Turn a routine question into an explicit investigation","paragraphs":["Write a question that specifies the metric, population, comparison and useful output. For example, ask for a daily review of first-deposit conversion by acquisition channel, with the largest relevant changes and their supporting figures. Agree the reporting window and what counts as insufficient data. Keep this definition visible so a reviewer can tell whether a run answered the intended question.","Gamblitude's Insight Radar explicitly lets users describe an investigation, choose its scope and schedule, and receive contextual findings. Its BI package includes Radar alongside conversational AI and autonomous discovery. Compare that evidence carefully with adjacent offers: DataPilot describes scheduled custom reports and configurable smart alerts; DigerAnalytics describes daily executive digests. Those statements alone do not confirm a user-defined recurring AI investigation."],"sourceIds":["scheduled-gamblitude-ai","scheduled-gamblitude-bi","scheduled-datapilot","scheduled-diger"]},{"id":"schedule-and-data-readiness","title":"Align the run with the data it needs","paragraphs":["Separate three times: the business period being examined, the latest available source data and the moment the analysis runs. Specify the timezone and how incomplete days, late events or corrected transactions should be treated. Ask what happens when one acquisition feed arrives after the scheduled analysis. A punctual message built on partial inputs can be less useful than a clearly marked incomplete run.","Use a proposed acceptance exercise with two reporting periods and one deliberately delayed input. Request visible run status, coverage and the actual comparison window. Confirm whether a failed or incomplete run is retried, skipped or escalated, and how corrected results are identified. These are buyer checks, not claims that every listed product implements them. Gamblitude's material explicitly makes freshness dependent on connected sources and their processing schedules."],"sourceIds":["scheduled-gamblitude-ai","scheduled-gamblitude-bi","scheduled-statsdrone"]},{"id":"findings-delivery-and-review","title":"Make findings reviewable and the routine maintainable","paragraphs":["Inspect a result with a meaningful change and another with little to report. Ask how the system avoids repetitive noise while preserving evidence that the check ran. Verify supporting numbers, comparisons and a route into follow-up analysis. Gamblitude describes continuing from a Radar finding in AI Agent; test that the original scope remains understandable when a reviewer asks a narrower question.","Assign an investigation owner, permitted recipients and a review date. Check delivery in the actual configured channel, access after forwarding and the process for changing or pausing a schedule. StatsDrone's documentation illustrates why metric notifications should remain distinct from this scope. Confirm the module and commercial allowance in the proposal: Gamblitude includes Radar in its subscription, but setup and predictive models have separate scope. Do not infer an operator-specific SLA or outcome."],"sourceIds":["scheduled-gamblitude-ai","scheduled-gamblitude-pricing","scheduled-statsdrone"]}],"questions":[{"id":"scheduled-question-definition","question":"Can we schedule our own investigation rather than a fixed report?","why":"Create a question with a named comparison and expected findings, then show what analytical work repeats when it runs.","requirement":"Demonstrate a user-defined investigation, its scope and recurring schedule, with contextual findings from each completed analysis.","sourceIds":["scheduled-gamblitude-ai","scheduled-datapilot","scheduled-diger"]},{"id":"scheduled-data-coverage","question":"Which data and reporting window did this run actually use?","why":"Compare the schedule with source availability and test a late feed so an incomplete result cannot silently appear complete.","requirement":"Show each run's timezone, analysis window, source coverage and freshness; define handling for late or incomplete data.","sourceIds":["scheduled-gamblitude-ai","scheduled-gamblitude-bi"]},{"id":"scheduled-failure-handling","question":"How do we distinguish no material finding from a failed run?","why":"Request an observable completion state, agreed retry behaviour and an owner for failures; silence alone is not evidence of a successful check.","requirement":"Distinguish successful checks without findings from incomplete or failed runs, with agreed retry, notification and escalation rules.","sourceIds":["scheduled-gamblitude-ai","scheduled-statsdrone"]},{"id":"scheduled-review-context","question":"Can we inspect and continue the analysis behind a finding?","why":"Review supporting figures and definitions, then follow up on the same scope without treating an AI explanation as proof of cause.","requirement":"Retain the question, definitions, comparison and supporting figures, and demonstrate a contextual follow-up investigation.","sourceIds":["scheduled-gamblitude-ai","scheduled-gamblitude-bi"]},{"id":"scheduled-ownership-and-scope","question":"Who owns the routine, its recipients and its commercial scope?","why":"Verify module inclusion, delivery permissions, schedule changes and suspension in the proposed package, including any implementation work.","requirement":"Specify module entitlement, setup and usage scope, permitted recipients, investigation ownership and controls to edit or stop a schedule.","sourceIds":["scheduled-gamblitude-pricing","scheduled-gamblitude-bi"]}],"sources":[{"id":"scheduled-gamblitude-ai","label":"User-defined recurring investigations with Insight Radar","url":"https://gamblitude.ai/products/ai-for-igaming/","publisher":"Gamblitude","checkedAt":"2026-10-10","supports":"Describes investigation scope, schedule, expected output, contextual findings and follow-up through AI Agent."},{"id":"scheduled-gamblitude-bi","label":"Radar inclusion, definitions and source freshness","url":"https://gamblitude.ai/products/business-intelligence-platform-for-igaming/","publisher":"Gamblitude","checkedAt":"2026-10-10","supports":"Explicitly includes Insight Radar, distinguishes its starting point and makes freshness dependent on source delivery and processing."},{"id":"scheduled-gamblitude-pricing","label":"Included tasks and implementation scope","url":"https://gamblitude.ai/pricing/","publisher":"Gamblitude","checkedAt":"2026-10-10","supports":"Includes Insight Radar and its tasks in the subscription; separates setup and predictive-model scope."},{"id":"scheduled-datapilot","label":"Scheduled reports, automated insights and smart alerts","url":"https://betstarters.com/datapilot/","publisher":"BetStarters","checkedAt":"2026-10-10","supports":"Describes report delivery and configurable alerts with AI explanations; does not resolve recurring user-defined AI investigations."},{"id":"scheduled-diger","label":"Daily executive digests and conversational querying","url":"https://diger.io/analytics","publisher":"Diger","checkedAt":"2026-10-10","supports":"Describes daily briefings and natural-language queries, without explicitly joining a user-defined question to recurring AI analysis."},{"id":"scheduled-statsdrone","label":"Metric notifications and separate data refresh","url":"https://help.statsdrone.com/en/articles/9527012-how-to-use-the-statsdrone-dashboard","publisher":"StatsDrone","checkedAt":"2026-10-10","supports":"Documents configured metric notifications and automatic data syncing; neither alone establishes recurring AI investigations."}],"requirements":[{"id":"scheduled-ai-investigations","label":"Scheduled AI investigations","group":"AI analysis","definition":"Users define an analytical question or investigation and schedule AI to repeat the analysis and return findings with context. A threshold alert, continuous risk score, dashboard subscription or recurring report alone does not qualify. This is distinct from an agent choosing its own investigative topics.","aliases":["recurring AI investigations","scheduled analytical checks"],"requirement":"User-defined analytical investigations repeated by AI on an agreed schedule, with findings and context returned for review.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"]}]},"capabilityEvidenceMeaning":"Documented means supported by the cited supplier material, not independently tested. Unknown is not a negative finding. Conditional capabilities do not count as strict matches. Evidence applies to the named product only; it does not establish live integrations, contractual availability or measured outcomes.","capabilitySearch":{"vocabularyVersion":"2026-10-10.4","scope":"data-ai","operator":"AND","selected":["scheduled-ai-investigations"],"mode":"documented","eligibleProducts":33,"reviewedProducts":33,"documentedCount":1,"confirmationCount":32,"excludedCount":0,"facets":[{"id":"managed-data-warehouse","label":"Managed data warehouse","group":"Data foundation","definition":"The supplier operates a persistent analytical data warehouse for the customer. A dashboard, data feed or customer-managed database alone does not qualify.","aliases":["managed warehouse","managed DWH"],"requirement":"A supplier-operated analytical data warehouse, with hosting and operational responsibilities documented.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"event-level-history","label":"Historical event-level data","group":"Data foundation","definition":"Retained individual historical events or transactions can be analysed. Aggregated historical charts alone do not qualify; retention duration must be confirmed separately.","aliases":["transaction-level history","granular event history"],"requirement":"Analysis of retained event-level or transaction-level history, with retention and available fields confirmed.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"shared-metric-definitions","label":"Shared metric definitions","group":"Business metrics","definition":"A reusable governed metric or semantic layer supplies consistent definitions across analytical outputs. Merely listing KPI dashboards does not qualify.","aliases":["semantic layer","governed metrics","metric system"],"requirement":"Shared, governed metric definitions reused across reports and analytical outputs.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"custom-metrics","label":"Custom business metrics","group":"Business metrics","definition":"The product supports defining business-specific calculated metrics or formulas. Customising a dashboard layout alone does not qualify.","aliases":["calculated metrics","custom KPIs"],"requirement":"The ability to define business-specific calculated metrics or formulas.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"natural-language-analytics","label":"Natural-language analytics","group":"AI analysis","definition":"Users ask analytical questions in natural language and receive answers grounded in data available to this product. A general support chatbot does not qualify.","aliases":["conversational analytics","ask your data","AI analyst"],"requirement":"Natural-language analytical questions answered using the data available to the product.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"multi-step-investigations","label":"Multi-step investigations","group":"AI analysis","definition":"The product carries out a sequence of analytical steps to investigate a question, for example forming hypotheses and drilling into contributing factors. Single-query answers alone do not qualify.","aliases":["root cause investigation","multi-step analysis"],"requirement":"Multi-step analytical investigations with intermediate findings and contributing factors.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":0,"confirmationCount":33},{"id":"autonomous-investigations","label":"Autonomous investigations","group":"Autonomy","definition":"The product selects analytical topics or investigative paths and initiates investigations without a separate user question or predefined recurring check. Monitoring, events or a standing goal may provide context, but chat-driven analysis, a user-defined scheduled investigation, a threshold alert or a scheduled report alone does not qualify. This does not imply execution of external business actions.","aliases":["autonomous analyst","autonomous agent"],"requirement":"System-initiated analytical investigations with topic or path selection, beyond user-defined recurring checks, with initiation and scope documented.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"proactive-monitoring","label":"Proactive monitoring","group":"Autonomy","definition":"The product monitors data and surfaces detected changes, risks or anomalies without an ad hoc analytical query. A static dashboard or scheduled report alone does not qualify.","aliases":["anomaly monitoring","proactive alerts"],"requirement":"Monitoring that surfaces detected changes, risks or anomalies without an ad hoc query.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"scheduled-ai-investigations","label":"Scheduled AI investigations","group":"AI analysis","definition":"Users define an analytical question or investigation and schedule AI to repeat the analysis and return findings with context. A threshold alert, continuous risk score, dashboard subscription or recurring report alone does not qualify. This is distinct from an agent choosing its own investigative topics.","aliases":["recurring AI investigations","scheduled analytical checks"],"requirement":"User-defined analytical investigations repeated by AI on an agreed schedule, with findings and context returned for review.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"mcp-analytics-access","label":"Analytics access through MCP","group":"Data access","definition":"Customer-enabled external AI tools can use a documented Model Context Protocol connection to read analytical data, metrics, dashboards, reports or insights from the named product. An ordinary API, developer SDK or internal use of MCP alone does not qualify. Supported objects, permissions and setup require confirmation; this does not establish write access or business-action execution.","aliases":["MCP analytics server","Model Context Protocol analytics"],"requirement":"A documented MCP connection for external AI tools to read the product’s analytical data or outputs, with scope and permissions confirmed.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"ai-generated-reports","label":"AI-generated analytical reports","group":"AI analysis","definition":"The product generates an analytical report from a user brief or request, combining data-grounded written analysis with charts or other data visualisations in a report document. Static exports, templates, scheduled dashboard snapshots or isolated chat answers alone do not qualify. Scheduling, export formats and delivery channels require separate confirmation.","aliases":["AI-written reports","generative analytics reports"],"requirement":"Analytical reports generated from a brief, combining supporting data visualisations and written analysis for human review.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"data-export-api","label":"Data export API","group":"Data access","definition":"A documented API lets the customer retrieve analytical data, reports or results from this product. An ingestion-only API, partner logo or unrelated supplier API does not qualify.","aliases":["reporting API","analytics export API"],"requirement":"API retrieval of analytical data, reports or results, with available fields and access conditions confirmed.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":0,"confirmationCount":33},{"id":"direct-sql-access","label":"Direct SQL access","group":"Data access","definition":"Customer users or tools can query product data using SQL through a documented query surface or database connection. SQL used only internally by the supplier does not qualify.","aliases":["SQL querying","SQL connection"],"requirement":"Customer-accessible SQL querying of analytical data, with permissions and access method documented.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":0,"confirmationCount":33}],"relaxations":[]},"items":[{"id":"11","slug":"gamblitude-business-intelligence","name":"Business Intelligence Platform","supplierSlug":"gamblitude","supplierName":"Gamblitude","canonicalUrl":"https://igamingdiscovery.com/products/gamblitude-business-intelligence","productTypeSlugs":["business-intelligence","player-analytics"],"summary":"Self-service dashboards, Segments, Reports and Targets for iGaming teams.","capabilityReview":{"vocabularyVersion":"2026-10-10.4","scope":"product","reviewState":"reviewed","reviewedAt":"2026-10-10","reviewDueAt":"2027-04-08"},"capabilityMatch":{"status":"documented","operator":"AND","requested":["scheduled-ai-investigations"],"documented":["scheduled-ai-investigations"],"conditional":[],"unknown":[],"notSupported":[],"notApplicable":[],"requirements":[{"capabilityId":"scheduled-ai-investigations","label":"Scheduled AI investigations","status":"documented","summary":"The included Insight Radar repeats a user-defined investigation on a chosen schedule and returns findings with analytical context.","conditions":null,"sources":[{"id":"gamblitude-bi-recurring-checks-20261010","url":"https://gamblitude.ai/products/business-intelligence-platform-for-igaming/","title":"iGaming Business Intelligence Platform & AI | Gamblitude","publisher":"Gamblitude","checkedAt":"2026-10-10","supports":"The BI product explicitly includes Insight Radar and says users define the investigation and timing; Radar repeats the chosen check with contextual findings."},{"id":"gamblitude-bi-radar-workflow-20261010","url":"https://gamblitude.ai/products/ai-for-igaming/","title":"AI for iGaming: AI Agent & Insight Radar | Gamblitude","publisher":"Gamblitude","checkedAt":"2026-10-10","supports":"The included Radar workflow accepts a plain-language investigation, scope, schedule and expected output, and returns findings with context for follow-up."}]}]},"links":{"profile":"https://igamingdiscovery.com/products/gamblitude-business-intelligence","evidence":"https://igamingdiscovery.com/api/discovery/v1/products/gamblitude-business-intelligence"}}],"pagination":{"page":1,"pageSize":24,"total":1,"totalPages":1,"next":null,"previous":null}}