Watchtower · Agentic analytics for business-critical operations

Know what changed,why it matters,what to do next —and whether it worked.

Watchtower, the agentic analytics system from Thrash Analytics, monitors your KPIs and the upstream metrics that drive them — detecting meaningful variance, explaining the drivers, routing approval-gated actions to accountable owners, and grading every result in the Outcome Ledger.

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Watchtower showing ranked insights, KPI variance, drivers, narrative, recommendations, action history, and effectiveness tracking.

20+ yearsDesigning, building, and governing enterprise data, analytics, and AI systems

Former CIOEnterprise data platform and technology leadership

PerficientFormer Director of Artificial Intelligence & Advanced Analytics

Wharton MBAWith hands-on engineering depth — Python, React, RAG, semantic layers

B2B SaaSRevenue-retention watchtower for customer success, RevOps, and growth teams.

ManufacturingVariance watchtowers for plant, supply chain, and operations teams.

HealthcareGoverned RCM intelligence that connects root causes to cash outcomes.

LogisticsControl-tower logic that ranks the work most likely to move throughput or service.

Meet Watchtower

One system that watches the metrics that lead your KPIs

Watchtower connects to the semantic layer you already run — Snowflake, Databricks, dbt — and builds a live map of how upstream behavior drives your headline numbers. When a signal is large enough, timely enough, and valuable enough, it explains why, recommends the next action, and routes it for approval.

Semantic KPI monitoringLeading-indicator detectionDeterministic anomaly detectionRanked operational insightsExplainable drivers and narrativeApproval-gated recommendationsOutcome Ledger gradingAction history and effectiveness tracking

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Watchtower maps the upstream metrics that lead your headline KPI — so the alert fires while there is still time to act, not after the quarter closes.

The Outcome Ledger

Every recommendation graded — including the misses

Dashboards stop at what happened. Watchtower stays accountable for whether the action worked: each recommendation is recorded as a claim with its reason, expected effect, and actual result, then graded honestly so the loop improves over time.

Signal	Action taken	Expected effect	Actual result	Grade
Gross margin −17.1% vs expected, concentrated in region = south	Routed to the regional ops owner; expedited-fulfillment pricing review approved	Recover most of the −$79.9K weekly variance	Margin back inside baseline in two weeks; +$71K recovered	Hit
Support tickets +19% vs expected	Recommended an onboarding-content fix; approved by the support lead	Ticket volume −10% within 30 days	No movement — the real driver was a release defect, not onboarding	Miss — graded honestly
Returns +21.9% vs expected	Held — confidence below the action threshold, so Watchtower kept monitoring	Signal resolves on its own if seasonal	Self-resolved within one cycle; the hold was correct	Correct hold

Sample entries from the Watchtower demo loop. Every recommendation is recorded as a claim — reason, action, expected effect, and actual result — and graded against what really happened.

What agentic analytics means

Detect, explain, prioritize, trigger, and measure

The work is not another dashboard. It is a governed operating loop that turns a signal into accountable action and tracks whether the action worked.

Detect

Monitor semantic KPIs and the upstream behavior that leads them for variance and precursor signals that are large enough, timely enough, and valuable enough to review, with seasonality-aware baselines so normal cycles are not mistaken for problems.

Explain

Surface drivers, evidence, and plain-language narratives so operators can understand why the signal matters.

Prioritize

Rank insights by impact, confidence, urgency, deadline risk, and whether an owner can act.

Trigger

Recommend next-best actions with approvals, policy context, accountable owners, and fallback paths.

Measure

Record every recommendation in the Outcome Ledger — reason, action, expected effect, and actual result, graded honestly — so the loop is accountable for whether the action moved the number and improves over time.

Productized offers

Clear ways to start without a vague AI roadmap

Start with a fixed-fee diagnostic, then move only when the workflow, data readiness, governance needs, and measurable outcome are clear.

Agentic Analytics Diagnostic

Recommendations carry evidence, approvals, policy context, fallback paths, and measurement from the start.

More operational than dashboards

The system anticipates problems with leading indicators, triggers accountable work, and records honest outcome grades in the Outcome Ledger — not only visualizing variance after it happens.

Scorecard

Find the workflow most ready for agentic analytics

The Agentic Analytics Readiness Scorecard helps operators evaluate KPI readiness, explainability, governance, action tracking, and outcome learning.

Start the Scorecard

Agentic Analytics Readiness Scorecard

Score one KPI, queue, or exception pattern in 10-15 minutes. Email is requested only after the scorecard is complete.

Readiness band, knockout check, two gaps, and first fixes.

Start the Scorecard

We do not sell your information.

Founder-led

Enterprise AI judgment with hands-on engineering depth

Will Thrash is an agentic AI engineer and AI/data platform leader with 20+ years designing, building, and governing enterprise software, data warehousing, analytics, and AI systems.

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Will ThrashFounder, Thrash Analytics

Next step

Bring one KPI, queue, or operational bottleneck.

We will identify whether agentic analytics can create a governed path to measurable movement.

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