What makes us different?
Our technology is proven and highly differentiated.
Over 3 years of intense R&D resulted our "The Meaning Layer" which blends structured and unstructured data into a system running causal analysis to find meaningful relationships / true anomalies in the data -- finding the truly meaningful signal in the noise for any given business question. The Agent capabilities move you through the process fast, helping the user to identify and develop the story behind the data along with operational insights.
The solution is build using data science principles with accessibility for anyone who has experience working with data. Used correctly context engineering (the successor to prompt engineering) unlocks a huge range of advanced analytical and reporting skills to execute projects in a couple hours that before would have taken weeks.
For our customers the result is an ability to answer the questions "why?" and "so what?" more quickly and easily. When an executive asks, why?, you have the answer at your fingertips with the hard numbers to back it up. With causal intelligence you are armed with answers to the most difficult questions.
The causal gap
The distance between knowing what happened and understanding why is the most expensive gap in enterprise analytics.
Causal Intelligence provides a new level of analysis that businesses need. By joining your unstructured language data — calls, chats, tickets, reviews, surveys, transcripts — with your structured business metrics, it produces statistically meaningful answers grounded in a complete picture.
This was not possible at scale, until now.
The first three tiers are blind to language, requiring structured inputs: tables, columns, numeric fields. The most important inputs to your business — what customers actually say, ask, and complain about — are invisible .
How it works: The Meaning Layer
You've likely struggled to get the insights you want from analytics tools in the past. Not because they didn't have AI, but because they lacked the full picture.
Over 3 years of intense research and development went into creating a data pipeline that addresses the 90% of enterprise data that is unstructured and transforms it into something usable.
Unstructured data isn't something that AI solves. AI as a commodity — a chatbot or an agent — pointed at conversational data will hit the same limits as your dashboards.
The meaning layer is a piece of infrastructure that your Agent to produce superior insights compared with other solutions making similar claims. It serves as the mechanism required for stitching structured and unstructured data into a complete picture of the enterprise.
Shows what happened. Stops at the numbers.
Leaves the why as a guess.
Explains why it happened. Grounds every claim in row-level evidence.
Drillable to the source conversation.
Your Agent is the interface
You access Causal Intelligence through your Agent, an AI partner built to access the meaning layer. Your Agent supports four executive workflows.
Ask anything in plain English, make a chart, define a business objective, or generate a leadership-ready report. In each workflow, your Agent connects structured business data with unstructured conversation data, then returns answers grounded in source evidence.
Ask questions like:
- “What’s driving churn in mid-market last quarter?”
- “Which conversation themes predict expansion 90 days out?”
Your Agent writes the SQL, queries across structured and unstructured data, generates the chart, and returns an evidence-backed answer. Every chart is drillable to the source conversations behind it.
Three applications, one platform
Causal Intelligence ships as three high-impact applications, each activating different combinations of your data sources:
Causal Intelligence for every customer interaction, human or AI.
Causal Intelligence for what to build next, backed by customer evidence.
Causal Intelligence for why your revenue moves.