RetailReduction in time-to-insight across teams
LogisticsOperational issues detected before escalation87%
SaaSFaster root cause identification on average60%
FinanceDecisions backed by AI-driven analysis92%
HealthcareDecrease in unresolved support tickets51%
RetailReduction in time-to-insight across teams
LogisticsOperational issues detected before escalation87%
SaaSFaster root cause identification on average60%
FinanceDecisions backed by AI-driven analysis92%
HealthcareDecrease in unresolved support tickets51%

Scale Data-Backed Decisions

with Built-In Company Expertise

Cosmio turns data into analysis-ready inputs, captures essential team knowledge,and layers them to unlock unprecedented scale in data analysis.

Complete Data Analysis Lifecycle

Collect, organize, and transform multi-source data to make it analysis-ready.

Data Collection
Add new data source
Structured DataUnstructured DataOrganized DataThinking...
Decision Intelligence

Existing systems and dashboards show where you are and what changed - not why. You need the drivers to decide and act.
Cosmio uncovers the why. End to end.

Preparing Data

Turning raw data into analysis-ready inputs

Data is spread across systems and formats, often siloed and poorly understood - which is why ~70% of time goes to preparation. Cosmio organizes and transforms this data into analysis-ready inputs and builds a unified data catalog to extract business value.

Salesforce

Salesforce

HubSpot

HubSpot

Snowflake

Snowflake

Google BigQuery

Google

BigQuery

Gong

Gong

Oracle

Oracle

Intercom

Intercom

Zendesk

Zendesk

Servicenow

Servicenow

Slack

Slack

Jira

Jira

Notion

Notion

Microsoft Teams

Microsoft

Team

Confluence

Confluence

Monday

Monday

Google Sheets

Google

Sheets

Google Drive

Google

Drive

Outlook

Outlook

Gmail

Gmail

SAP

SAP

Workday

Workday

Capturing Expertise

Turning team knowledge into institutional context

Critical expertise - assumptions, metrics, relationships - lives within teams. Cosmio captures this knowledge, preserves it as institutional context, and activates it during analysis - enabling consistency and unparalleled scale.

AcmeCompany Overview
+5
Invite

Executive Summary

Analysis Process: During the calculation of warehouse utilization metrics, I discovered that 3,613 products (28.1% of total inventory) had missing or incomplete dimension data (length, width, height, weight). After inferring the data for the missing dimensions following utilization rates were calculated.
Warehouse Utilization Summary
Feedback
Warehouse A
Warehouse B
Warehouse C
Warehouse D
Warehouse E
Warehouse F
0%20%40%60%80%100%
Key Findings: Warehouse E - West operating at 90% utilization (highest). Warehouse A - North at 86% utilization (second highest). Both locations approaching capacity limits and may require expansion or redistribution.
Data Quality Insights: Warehouse A - North has the highest data quality (only 5.8% inferred data).
Analysis Process: Cosmio identified multiple cost and productivity variances: labor hours +42% versus estimate, labor efficiency at ~70%, housing costs +35% above estimate, weather-related downtime without offsetting production gains, and critically, forecast variance that was visible but triggered no corrective action.
Analysis Process: Cosmio identified multiple cost and productivity variances: labor hours +42% versus estimate, labor efficiency at ~70%, housing costs +35% above estimate, weather-related downtime without offsetting production gains.

George

Now

@Rachel, should we exclude aisles from the utilization calculation?

Approve
Alice
Artur

Automating Analysis

Turning data and context into confident decisions

With analysis-ready data and embedded company expertise, Cosmio automates analysis end-to-end. From root causes to patterns, correlations, drivers, and forecasts - building confidence in decisions.

root cause analysis
cause candidate
hypothesis
status
actions

External conditions (weather and market-driven housing costs) increased project costs beyond estimate

IF:External factors (weather disruption and market-driven housing/logistics pricing) were the primary drivers of degraded margin versus estimate
THEN:we should observe: Cost overruns temporally aligned with external events. Labor productivity declines concentrated during weather-impacted periods
Main Contributor

Control effectiveness failure allowed budget drift without early corrective action

IF:Internal project controls (cost tracking cadence, variance thresholds) failed to detect and correct margin erosion early enough
THEN:we should observe: Delayed variance flagging relative to cost incurrence. Corrective actions initiated late in project lifecycle
Main Contributor

Decision latency in procurement approvals delayed cost-saving substitutions

IF:Slow decision cycles on procurement changes prevented timely cost mitigation
THEN:we should observe: Procurement approval timelines exceeding SLA. Material cost premiums from delayed purchasing decisions
Contributing
Michel
Anna
Analyze campaign performance
Forecast warehouse utilization
Predict sales pipeline conversion
Assess pricing impact
Track KPI performance
Detect anomalies in data
Identify operational inefficiencies
Analyze inventory turnover
Forecast shipment volumes
Predict carrier capacity needs
Analyze campaign performance
Forecast warehouse utilization
Predict sales pipeline conversion
Assess pricing impact
Track KPI performance
Detect anomalies in data
Identify operational inefficiencies
Analyze inventory turnover
Forecast shipment volumes
Predict carrier capacity needs
Analyze campaign performance
Forecast warehouse utilization
Predict sales pipeline conversion
Assess pricing impact
Track KPI performance
Detect anomalies in data
Identify operational inefficiencies
Analyze inventory turnover
Forecast shipment volumes
Predict carrier capacity needs

“Dashboards only showed delays — never why. We wasted weeks chasing symptoms. Cosmio took one sentence description, connected five systems, and in minutes pinpointed a hidden maintenance-route mismatch no one saw. Late deliveries dropped 78%. Now we trust the answers instantly and move fast.”

Head of OperationsLogistics Industry

“Yield kept dropping on one line. Consultants and internal teams found nothing for months. I told Cosmio the symptom — it dug through seven data sources, found tiny alloy variations triggering scrap under specific conditions. Fixed it in days. Saved $340k in six months. Feels like having a top data scientist on speed dial.”

VP, Supply ChainManufacturing Industry

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Enterprise teams

“Dashboards only showed delays — never why. We wasted weeks chasing symptoms. Cosmio took one sentence description, connected five systems, and in minutes pinpointed a hidden maintenance-route mismatch no one saw. Late deliveries dropped 78%. Now we trust the answers instantly and move fast.”

CTOConsumer Goods Industry

[ F.A.Q ]

Frequently Asked Questions

A quick introduction to what Cosmio is, who it is for, and how to begin.

Your question not answered here?

Contact Us

Cosmio is an end-to-end automated data analysis platform for your business. It handles the full data analysis lifecycle — from integrating and preparing data across your CRMs, ERPs, data warehouses, and external sources, to running multi-step analysis and reporting. Cosmio acts like an automated analyst: detecting patterns, uncovering root causes, forecasting outcomes, and recommending actions automatically and continuously, while helping teams share expertise and scale analytical insights across the organization.

An end-to-end automated data analysis platform covers the entire analytical lifecycle in one connected system: data collection and preparation from multiple sources, automated analysis across all stages (what happened, why it happened, what will happen next), and collaboration features that preserve and share team expertise.

Cosmio is built for the whole organization. Data and analytics teams use it to eliminate repetitive investigation work and focus on deeper, strategic analysis. Operations, finance, and leadership teams use it to get fast, reliable answers to performance questions without waiting on analysts. And because Cosmio supports both technical and non-technical users, it enables governed self-service analytics across the business.

Data teams switch to Cosmio because it eliminates the manual, repetitive work that consumes most of their time — pulling data, reconciling sources, and responding to one-off investigation requests. Instead of spending hours or days diagnosing why a metric changed, Cosmio performs root cause analysis automatically, giving teams back the time to focus on strategic work while ensuring leadership always has answers fast.

Cosmio builds a structured semantic layer and data catalog that organizes raw data into consistent, analysis-ready, business-friendly datasets. On top of that, its collaboration features extract and preserve critical team knowledge — business context, definitions, and expertise — and embed them into the analysis process. This means every insight is grounded in both your actual data and your team's accumulated knowledge, ensuring accuracy and consistency at scale.

Explore Cosmio

Try our sandbox environment