Real Results, Real Impact

Government departments receive high-volume official economic and employment documents, often in PDF format. Extracting meaningful insights from these reports is slow, error-prone, and dependent on manual interpretation.

Although structured analytical data exists inside these documents, non-technical users struggle to analyze it quickly. Lack of real-time, data-driven insights results in delayed policy decisions, ineffective resource allocation, and missed economic development opportunities.

InteleChat 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.

• Time to Insight: Transitioned from days or weeks of manual data compilation to instant, query-based intelligence.
• Decision Accuracy: Improved decision-making through AI-driven insights backed by real datasets instead of assumptions.
• Accessibility: Enabled non-technical government officials to independently interact with and interpret analytical data.
• Data Utilization: Maximized the value of existing government documents by converting them into actionable intelligence using AI.

Insurance analysts spend significant time manually reviewing claim data to identify fraud risks and predict financial impacts. Duplicate claims, unclear suspicious patterns, and delayed insights lead to losses and reactive decision-making. Teams struggle to access relevant insights quickly, impacting fraud prevention and financial planning.

Using InteleChat’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.

• Faster Fraud Detection: High-risk and duplicate claims identified instantly instead of through prolonged manual review
• Reduced Financial Leakage: Early detection of high-risk cases helps avoid fraudulent payouts and improves claim settlement accuracy
• Improved Risk Assessment: Suspicious claim patterns highlighted proactively, enabling preventive action rather than post-loss recovery

During high-value arbitration proceedings, legal teams must analyze multiple judicial precedents, interpret jurisdictional positions, and assess the evolution of legal doctrine. This research is often manual, time-consuming, and vulnerable to interpretation gaps. Even a single missed reference or incorrect inference can shift the outcome of a cross-border legal challenge — causing reputational, financial, and strategic risk.

InteleChat 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.

• Risk-Free Legal Intelligence: On-prem deployment ensured total confidentiality and secure handling of high-stakes arbitration documents.
• Accelerated Case Analysis: Manual research efforts dropped from hours to seconds with instant, AI-assisted precedent extraction.
• Extended Knowledge Retrieval: When information isn’t available internally, the system intelligently searches trusted online sources to provide complete and context-aware responses.

Business development and proposal teams spend hours manually browsing GeM and other tender portals to identify relevant RFPs, review scope of work, and evaluate feasibility. Due to high document volume and manual interpretation, opportunities are missed, summaries are inconsistent, and decision cycles slow down. Non-technical users must read entire multi-page tenders to understand scope, skills required, and eligibility — delaying opportunity qualification and response planning.

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.

• Opportunity Discovery Speed: AI retrieves only relevant RFPs, eliminating time spent manually searching portals.
• Faster Decision Cycles: Instant scope of work summaries and skill extraction enable quick bid/no-bid decisions.
• Higher Win Probability: Early visibility of relevant RFPs gives teams more time for proposal creation and submission.

Loan underwriting required analysts to manually interpret financial statements, extract ratios, and verify them against banking and bureau data. This repetitive and time-consuming workflow caused delays in approval, inconsistent risk assessment, and a slower customer turnaround experience — especially for high-volume applications.

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.

• Faster Approval Cycles: Automated financial analysis and scoring reduced decision time from hours to just minutes.
• Stronger Compliance Control: Credit decisions routed through SAS Decisioning ensured policy adherence and complete audit traceability.

Fraud investigators often work with highly complex financial networks containing thousands of entities and links. Manually exploring these networks to uncover relationships or anomalies or hidden patterns is slow and technically demanding. Traditional systems require investigators to know specific tools and workflows, creating dependency on technical teams and delaying case outcomes.

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.

• Smarter Tool Orchestration: MCP automates tool selection and execution, reducing reliance on technical expertise.
• Faster Investigation Cycles: Query-to-insight turnaround time dropped from hours to minutes.
• Higher Accuracy: Context-aware responses improve precision in identifying hidden patterns.

Engineering teams lose significant time writing repetitive code, debugging errors, and managing automation workflows manually. Slow development cycles delay releases, increase operational cost, and limit innovation. Teams spend more time fixing issues than building new features — reducing velocity and overall product impact.

InteleChat 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.

• Faster Delivery: Development time reduced from hours to minutes with instant code generation.
• Better Engineering Efficiency: Engineers focus on complex logic while AI automates repetitive coding tasks.
• Error Reduction: Debug and validation agents significantly lower production defects.