AGENTIC AI DELIVERING PREDICTIVE INTELLIGENCE

The Forward-Looking Intelligence Platform Built on Databricks.

Through aggregated data sets, proprietary models, and continuous predictions, Clarecast gives organizations the forward-looking intelligence to make better decisions — about people, customers, markets, prospects, and the world in which they operate.

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Radar Sweep

THE PROMISE

PLATFORM AT A GLANCE

18M+

COMPANIES SCORED CONTINUOUSLY

300M+

EMPLOYMENT PROFILES

80%+

HEADCOUNT FORECAST ACCURACY

19

FOUNDATION MODELS BENCHMARKED

WHAT CLARECAST DOES

Intelligence that arrives in time to matter.

Clarecast aggregates the data signals that explain how companies are changing, runs proprietary models against those signals, and produces predictions that reach 12 months ahead. The platform's agents take those predictions and turn them into specific, defensible actions: which company to contact, why this week, which person to address, and what to say when you reach them. Every signal traces back to its source, every model output is reproducible, and every agent decision is recorded for audit.

Three example use-cases for how customers put the platform to work.

01 CUSTOMER INTELLIGENCE

Clarecast analyzes a customer's existing book as a strategic portfolio: 12-month headcount forecasts on every account, concentration risk detection across industries and geographies, and proactive alerts for churn risk and expansion opportunity.

02 Future-focused ICP Development

Clarecast builds a living, forward-looking ICP from real customer outcomes, scored across growth predictions, hiring patterns, and geography. Each match comes with the intelligence packet a rep needs: contacts, personalized emails, and call preparation.

03 MARKET EXPANSION

Beyond the known book and the known ICP, Clarecast surfaces net-new segments through forward-looking TAM analysis, identifies where companies are investing through workforce and talent signals, and produces ready-to-use contacts and personalized outreach for every target.

BUILT ON DATABRICKS

Clarecast operates at the scale and velocity where conventional data stacks break. Tens of millions of companies and hundreds of millions of employment profiles must be continuously ingested, enriched, modeled, and served. Predictions must remain governed and auditable from raw source to customer-facing application. Agents need to act on the same data the analytics teams trust, with no extraction layer in between.

Databricks is the foundation that makes this possible. Clarecast was built on Databricks from day one as an active participant in the Built On program. Nine Databricks capabilities carry the architecture.

→ Unity Catalog. Every dataset Clarecast produces lives under a single governance layer. Following a medallion architecture from raw ingestion through to the final serving layer, lineage, access, and audit are continuous — which is what makes the predictions defensible and the agents accountable.

→ Apache Spark & Photon. Continuous ingestion, enrichment, and forecasting across tens of millions of companies, hundreds of millions of people, and hundreds of billions of data records runs on Apache Spark, accelerated by the Databricks-native Photon engine.

→ Databricks Asset Bundles. The Clarecast platform is operated as code. Every job and pipeline that runs in production is source-controlled, reviewable, and reproducible, which is what allows the team to ship changes continuously without compromising stability.

→ SQL Warehouse, Genie & Real-Time Job Runs. Agents that need to reason over Clarecast data — including those operated by our customers — address it through the Databricks SQL Warehouse, real-time job runs, and Genie spaces. Both reach the same governed surface without additional integration.

→ Genie Code. Genie code revolutionizes how solutions engineers deliver custom data work, reducing days to hours and hours to minutes. A single engineer can now support what previously required ten, allowing Clarecast to meet customers with meaningfully different data needs without adding headcount in lockstep.

→ Lakehouse (Analytical) + Lakebase (Transactional). Clarecast splits its data into the right shape for the right workload. The Lakehouse handles the heavy analytical lift: ingestion, modeling, and forecasting. Lakebase serves the customer-facing application with the low-latency reads a production product requires. Both systems are designed around a shared identity model and governance posture — the architecture that lets us ship a fast, governed, AI-native application without standing up a parallel data stack.

→ Databricks ML. The proprietary models that produce Clarecast's predictions are built and operated on Databricks-native ML tooling, which is what allows the platform to retrain, evaluate, and ship new model versions on the same infrastructure that runs everything else.

→ Foundation Model Serving. LLM-driven enrichment, classification, and agent reasoning all run on Databricks-served foundation models inside the same governance boundary as the rest of the platform. Customer data never leaves the lakehouse, and Clarecast can swap or compose models without rewriting the surrounding stack.

→ AI/BI Dashboards. Clarecast uses Databricks AI/BI Dashboards at both ends of the stack. Internally, they monitor pipeline health, data quality, and model performance. In the production application, the same dashboard layer is embedded directly in the customer-facing UI — a live view powered by the same platform that produces the predictions.

Forward-looking intelligence meets the data architecture platform of the future.

WHY THIS MATTERS FOR DATABRICKS CUSTOMERS

Your data is already in the Lakehouse. Clarecast runs inside it.

Most intelligence platforms require an extraction layer, a custom ingestion pipeline, and a separate governance model before a single prediction reaches a downstream system. Clarecast eliminates that overhead because its production environment is the same Databricks workspace pattern your data team already operates. Forecasts, signals, and agent outputs land directly in Unity Catalog as delta tables, joinable to your first-party data through standard company identifiers on day one. Agent-facing systems address the same data through the Databricks SQL Warehouse, real-time job runs, and Genie spaces and deliver conversational analytics over the result set without additional integration.

Clarecast is built to meet your platform where it lives. With Delta Sharing, this means no parallel data stack, no separate identity model, no competing security surface — joining Clarecast predictions to first-party data becomes a notebook operation, and every signal an agent acted on is traceable to its source under the same controls that govern the rest of the estate.

CLOSING

Bring us a use case. Our agents put our data, models, and predictions to work.

Every engagement starts the same way, with a problem to solve. Once we understand the mission, we assess the data we need from you, the data we already have ready, and the context and outputs the work requires. We configure the platform, train your team, and our agents deliver the intelligence and the actions that solve for the outcome.

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