AI decision intelligence · Early access
When revenue, conversion or fulfilment moves unexpectedly, Causora investigates across your orders, ads, inventory, payments and support data, then shows the likely causes, the evidence behind each one, and the actions worth taking first.
Checkout revenue
Likely causes
Confidence reflects strength of evidence, not proof of cause.
Orders, ad spend, inventory, payments and support data examined together, not tab by tab.
Each hypothesis lists the specific signals, timestamps and records that support or weaken it.
Causes are ranked by strength of evidence. Correlations are never presented as proven causes.
Estimated impact and recommended next steps, routed to the team that owns the fix.
The problem
When a key metric drops, the answer usually sits across several systems that were never designed to be read together. Teams lose days reconciling exports, and the decision that matters gets made on whichever theory is loudest.
Today · Manual investigation
Storefront dashboard
Sales are down 22%. No reason given.
Ad platform
Spend and clicks look normal.
Inventory spreadsheet
Someone eventually spots a zero.
Help desk
Checkout complaints, read one by one.
Team thread
Three theories, no shared evidence.
SlowRevenue keeps leaking while teams reconcile data.
BiasedThe first plausible story wins, evidence or not.
UnscalableEvery new system adds another place to look.
With Causora · One investigation
Detects the revenue drop against its expected range
Queries orders, ads, inventory and payment logs for the same window
Finds the top SKU hit zero stock while campaigns kept sending traffic to it
Flags a smaller, overlapping rise in checkout errors
Estimates the revenue gap and recommends who should act first
Our principle: Causora separates what the data supports from what it proves. Every finding carries a confidence level and the evidence behind it, so your team makes the call.
Core features
Each part of Causora hands off to the next, so a detected change becomes a reviewed, owned action instead of another chart to interpret.
Learns the expected range of each business metric, accounting for weekday patterns and seasonality, and flags movements that fall outside it.
Why it matters: You hear about a real problem the morning it starts, without alert fatigue from normal swings.
→ Opens an investigation automatically
Pulls the same time window from every connected system and compares hundreds of related signals to find what changed alongside the metric.
Why it matters: Replaces hours of exporting and cross-referencing with one assembled case file.
→ Feeds the hypothesis engine
Models how your metrics relate, from ad spend to sessions to cart to checkout to revenue, including the systems that sit underneath each step.
Why it matters: Investigations follow the real paths a problem can travel, not random correlations.
→ Guides where Causora looks first
Ranks possible explanations by confidence and attaches the records, timestamps and counter-evidence behind each one.
Why it matters: Teams align on what the data supports, and can see what would change the conclusion.
→ Reviewed by your team before action
Estimates the size of the gap against expected performance and suggests corrective steps, each tagged with an owner and effort level.
Why it matters: The biggest leak gets fixed first, by the team that can actually fix it.
→ Sent to the review queue
Watches the metrics and thresholds you choose and notifies the right people by email or team chat when something needs attention.
Why it matters: Recovery is confirmed, not assumed, after a fix ships.
→ Closes the investigation loop
How it works
STEP 01
Link storefront, ad, inventory, payment and support data through read-only connectors or file exports.
Unified event history
STEP 02
Causora learns normal behaviour for each metric and maps how metrics depend on one another.
Expected ranges + dependency map
STEP 03
A metric leaves its expected range. An investigation opens with the affected window and segments.
Anomaly with severity
STEP 04
Related signals are compared along the dependency map. Hypotheses are ranked with evidence and confidence.
Ranked causes + impact estimate
STEP 05
Your team approves, assigns or dismisses recommended actions. Monitoring confirms recovery.
Owned actions + audit trail
Interactive product showcase
This workspace runs in your browser on sample data for a fictional retailer. Open INV-1042, inspect the evidence, approve actions in the review queue, explore the dependency map and adjust alert rules.
Harbor & Pine workspace · Growth
Oct 17, 2026
Open investigations
4
1 critical
Est. revenue at risk
$38.4K
INV-1042 · 4 days
Resolved this month
3
Recovery confirmed
Causora insight
Revenue fell sharply from Oct 14 while ad-driven sessions held steady. The strongest supported explanation is a stock-out on the top-selling SKU combined with paid campaigns that kept sending traffic to its product page. A smaller, overlapping rise in checkout errors also contributed.
Based on 4 sources and 212 compared signals. Correlated factors are labelled; none are presented as proven.
Connected sources
Activity
Causora ranked 4 hypotheses on INV-1042
2h agoMarcus T. assigned INV-1041 to Support
5h agoCausora detected anomaly in Support volume (+48%)
Oct 12Priya N. approved "Open ticket with payment provider"
Oct 15Causora marked INV-1039 resolved after 5 days at baseline
Oct 9All names, figures and records in this demo are fictional sample data.
Who it's for
Growth, merchandising and operations leads at online retailers and DTC brands.
Revenue operations, product and customer success teams.
Network, fulfilment and customer service managers.
Business operations and analytics teams supporting several departments.
Technology
Causora's architecture is designed as a serverless, event-driven pipeline on AWS. Data from connected systems lands in Amazon S3 and Amazon DynamoDB, AWS Lambda functions triggered through Amazon EventBridge run detection and investigation steps, and Amazon Bedrock turns the compared signals into ranked, evidence-linked hypotheses, with Amazon SageMaker AI available for customer-specific forecasting models.
Amazon EventBridge · AWS Lambda
Scheduled and event-driven connectors pull orders, ad spend, inventory, payment and support records from customer systems.
Amazon S3 · Amazon DynamoDB
Raw exports and evidence snapshots are kept in S3; normalised metrics, baselines and investigation state live in DynamoDB.
AWS Lambda · Amazon EventBridge
Lambda functions compute baselines and detect anomalies; EventBridge routes each detected anomaly to an investigation workflow.
Amazon Bedrock · Amazon SageMaker AI
Bedrock models reason over compared signals to draft ranked hypotheses and summaries; SageMaker AI hosts optional forecasting models.
AWS Lambda · Amazon DynamoDB
Lambda-backed APIs serve investigations, the review queue and alert rules to the Causora web workspace.
Business progress · Early access
Causora is in its building stage. We are working with a small group of early-access teams to connect their data, run real investigations and refine how findings are presented and acted on.
Causora founded
2026
Investigation model and product workflow defined
Detection → hypotheses → actions
AWS serverless architecture designed
Bedrock, Lambda, S3, DynamoDB, EventBridge
Early-access program open
Accepting design partners now
First design-partner workspaces
Next milestone
Tell us which metric you most need to understand. We reply from hello@causora.site.
Pricing
Prices are in US dollars. Access is currently by early-access invitation; request access and we will confirm plan details with you.
For a single team getting its first metrics under watch.
$5per month
For teams investigating across several connected systems.
$10per month
For operations groups running continuous monitoring.
$20per month
Need custom connectors or higher data volumes? Contact us to discuss requirements.
Team
Causora is founder-led and building in close contact with its first early-access teams.
Founder
Muhammad-Saad founded Causora in 2026 to give operations teams a faster, more honest way to understand why a business metric changed, not just that it changed.
LinkedIn profile of Muhammad-Saad AbdulsalamCo-Founder
Umar co-founded Causora in 2026 and is building the company and its investigation platform alongside Muhammad-Saad.
Contact
Questions about the product, partnerships, pricing or our AWS architecture go straight to the founding team.