Every Prism studio uses the same workspace, data layer, AI agents, monitoring, experimentation, security, and deployment infrastructure. One shell for your entire analytics stack.
Build production ML without building an ML platform.
Know what demand, revenue, and operations are likely to look like next.
Automatically turn raw business data into useful signals.
Detect risk before it becomes a business problem.
Find the price that balances demand, revenue, and margin.
Know what to stock, when to reorder, and where risk is building.
Turn predictions into better decisions.
Ask your data. Run your analytics. Take action.
Sentiment, topics, entities, and trends over millions of conversations, surveys, and documents — zero always-on infrastructure.
Index thousands of PDFs, DOCX, XLSX, and images; ask natural-language questions with source-cited answers via Bedrock KB.
Meridian + Robyn in one pipeline with budget optimization, what-if simulation, and an RL-driven budget adapter.
Visually orchestrate multi-step AI pipelines with branches, waits, notifications, and retries — versioned and replayable.
Trust-scored datasets, freshness checks, drift detection, and quality alerts — one monitoring surface for the whole platform.
Tenant-scoped Cognito auth, RBAC entitlements, multi-tenant isolation, KMS encryption, and ABAC across every studio.
Prism goes further: “here is what you should do next.”
Every studio is powered by reusable Prism engines instead of isolated ML pipelines. Train once — apply everywhere.
AutoML, forecasting, anomaly detection, model evaluation, and deployment.
Automated feature generation, entity aggregation, and temporal and geographic intelligence.
Goals, constraints, simulation, allocation, and policy optimization.
Tool-using agents that understand data, invoke studios, explain results, and orchestrate workflows.
Data quality, drift, model health, lineage, and decision monitoring.
Prism is one connected system — not a collection of disconnected apps.
Counts, health verdicts, open alerts, and quick actions across every asset.
One index across datasets, policies, experiments, workflows, feature sets, and alerts.
Every meaningful event — training, promotion, drift, retrain, alert — recorded and replayable.
The platform explains what changed in plain language — never fabricated.
Why any dataset, policy, or workflow has its trust and health — with evidence.
App-store of prebuilt solutions that instantiate a wired, versioned stack in one click.
Reproducible ledger across classification, forecasting, clustering, policies, MMM, LLM, and features.
Versioned input schemas bind training to exact features; reproduce any run byte-identically.
Read Snowflake, Redshift, Postgres, and S3 via Athena catalogs without copying data in.
Tenant-scoped Cognito auth, RBAC, KMS encryption, and granular audit across the platform.
Increase margin. Reduce stockouts. Improve customer retention.
Reduce downtime, inventory cost, and supplier risk.
Stop fraud. Protect revenue. Stay compliant.
Improve outcomes. Cut no-shows. Right-size supply.
Spend smarter. Boost ROAS. Track brand health.
Reduce churn. Diagnose issues. Predict demand.
From students and researchers to SMB analytics teams and enterprises, Prism lets users explore real datasets, build models, forecast outcomes, optimize decisions, and use AI agents — without building an entire ML infrastructure stack.
Every tier deploys into your AWS account. Compute and AI usage are metered separately — you only pay for the capabilities you actually use.
For students, researchers, individuals, and small teams.
For SMBs building repeatable analytics and decision workflows.
For organizations running multiple production studios.
For large, regulated, or multi-account deployments.
Compute and AI usage are metered separately, based on actual consumption.
Prism helps you act on them. Deploy an AI-native analytics and decision platform in your AWS environment and start building.