Composite AI brings together large language models (LLM) to translate complex output into understandable language, causal AI (CAI) to determine links through the funnel, machine learning (ML) to detect patterns and categorize data and Natural Language Processing (NLP) to extract/group similarities.

Data activation ingests and stitches together structured and unstructured information from all sources (POS, advertising metrics, social/web analytics, research, studies) creating a complete historical picture that you can extend and interpret into the future. Your Alembic Intelligence Brief presents your high-level and detailed findings.

Observability functions to establish a baseline, taking anomalies and spikes across thousands of data points to create causal chains, which are passed to the causality layer for historical analysis.

Causality conducts a historical analysis, revealing which marketing initiatives best provide growth across your chosen revenue or other metrics, letting you optimize and iterate your campaigns. Thousands of causal chains are analyzed to pinpoint the actions, and campaigns, that offer the most promise.

Prediction modeling offers 30-60-90 days forecasts, using observed conversion metrics like revenue to accurately predict future returns. You can forecast conversion rates of booked revenue. You can project revenue from incoming leads/opportunities. You can make informed marketing investments that will convert to your choice metrics.

Intelligence synthesizes and summarizes all predictive and historical causality information to help you confidently build your marketing plan and budgets. LLMs help you interpret thousands of insights into digestible information over your specified time period from daily and weekly to quarterly and monthly.

Privacy and Security by design are the foundation of Alembic. Resilient. No single point of failure. Fully SOC 2 compliant. 99.5% uptime SLA. Ultimate security where no PII, PDI, PIC data or cookies are collected. Alembic’s complete privacy is a matter of policy and practice, fully GDPR and USDP compliant. Data is ingested as anonymous aggregate statistical causal attribution without individual tracking and is not shared across customers.