Real Results, Real Impact

IntelliAgent transforms static government reports into dynamic decision intelligence. Structured data is extracted from official PDF documents and integrated with an analytical engine. Officials could now query economic and employment data in plain natural language, and the agent would instantly generate actionable insights, comparative analytics, forecasting outputs, and visual charts—directly from official government documents.

Using IntelliAgent's AI-powered natural language analytics agent, claim analysts can query structured data directly (e.g., duplicate claims with high suspicious scores), identify extreme high-risk cases, forecast future claim amounts, and generate real-time visualizations. The platform enables proactive fraud detection, data-driven decision support, and accurate financial forecasting—without depending on IT or data scientists.

IntelliAgent enabled lawyers to retrieve precedent-based insights instantly by querying past judgments in natural language — eliminating manual document review. The on-premise agent surfaced case references, summaries, and legal interpretations on demand, and expanded research by searching trusted online sources when additional rulings were required.

WP_RFP Analyzer automates tender discovery and scope extraction by fetching RFPs directly from portals like GeM, filtering them using AI-based topic relevance, and generating instant scope summaries with required skills and bid details. Users can identify relevant opportunities, review scope quickly, and download only the necessary documents — all without manual portal browsing or PDF reading.

A fully automated loan approval workflow was built to extract financial details from applicant documents, enrich them with bureau and internal data, and evaluate eligibility through SAS Intelligent Decisioning. Underwriters could query the AI assistant in natural language to instantly retrieve financial ratios, risk signals, and credit outcomes — with the system automatically classifying applications as Approved, Rejected, or Routed for Manual Review based on decision rules. This eliminated manual document reading and spreadsheet-based evaluation.

The SAS VI Network combines SAS Visual Investigator with a Large Language Model (LLM) agent and MCP (Model Context Protocol) framework. Through MCP, the AI agent intelligently selects and orchestrates the right SAS analytical tools — such as entity link analysis, transaction pattern detection, or network scoring — based on the user’s query. Investigators simply ask questions in natural language (e.g., “Show circular transactions linked to Taxpayer X”), and the system automatically identifies relevant tools, executes the analysis, and visualizes the results. This seamless AI–MCP synergy eliminates manual configuration, enabling effortless, context-aware fraud exploration.

A single upload auto-maps all source tables and validates in real time. The engine resolves all multiple formulas through their full dependency graph — every figure audit-linked to its source. Reconciliation runs in the background, stress scenarios run overnight, and the final submission is packaged per regulator before the board meeting starts.

IntelliAgent delivers an autonomous AI coding agent capable of generating, debugging the production-ready code from natural language instructions. Developers simply describe what they need, and the system produces validated code outputs — while also allowing manual edits wherever required.

WPIntelliAgent agents run inside SAS RAM and reach Viya services through MCP tool servers — so they don't sit beside the platform, they work within it, inheriting SAS identity, entitlements, and lifecycle management.

Ask for CASLIBs and they list. Ask to filter a dataset and the result renders below. Ask for a dashboard with bar, pie, and line charts and a presentation-ready visual builds in seconds. A decision-rules engine and guardrails constrain what may run; every tool call, parameter, and result lands in an execution trace and audit log. The agent decides what to run — Viya produces the answer.

An AI agent inside SAS RAM reads any document, from any domain, the way an expert would — reasoning through the language and extracting every rule on its own: thresholds, conditions, approvals, and underlying logic. Those rules flow straight into SAS Intelligent Decisioning as live, executable decision flows, with no manual coding or re-keying. Data is then scored against them in a single pass, and a second AI agent triages every result — rating risk and recommending the next step — while SAS Visual Analytics turns it into audit-ready dashboards.