Ready-to-Deploy, Expertly Tuned Business Service Agents for Energy, Public Sector, and Consumer Sectors

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Our pre-built Generative AI applications deliver significant, measurable value to businesses, enhancing efficiency and innovation. Experience transformative results with streamlined solutions tailored to your needs.

Our out-of-the-box pre-trained industry-specific agents deliver targeted solutions and immediate insights tailored to your sector, enhancing efficiency and driving growth.

Industrial Agents Driven by Business Processes

Our industrial agents for the public sector enhance efficiency and service delivery by automating tasks and supporting data-driven decisions.

Our industrial agents in the energy sector enhance operations by utilizing automation and real-time data analysis, resulting in improved efficiency and sustainability.

Our automated solutions for consumer goods optimize production and inventory, enhancing efficiency and reducing waste. They use real-time analytics to improve supply chain responsiveness.

Retail solutions enhance inventory management and customer experience with data-driven insights. They improve efficiency and adaptability using real-time analytics.

Leverage our Generative AI industrial applications to position your business as a leader in the industry

We build strategic partnerships delivering customized GEN AI solutions that address unique challenges and drive results. Our collaborative approach ensures effective strategies aligned with client goals across diverse industries, building trust through transparency and measurable outcomes.

Multi-Agent Conversational Intelligence: Accelerating Financial Insights Exploration

California’s FI$Cal system needs enhanced capability to transform financial statements into actionable insights. Implementing Generative AI with conversational intelligence would streamline the financial close process while enabling natural language interaction with complex fiscal data. This technology would automate analysis of spending patterns and improve budget forecasting accuracy across state agencies. LLMs could extract critical insights from financial statements that currently require extensive manual review, allowing for more strategic resource allocation.

  • Manual financial statement processing causes delays in the financial close process
  • Data silos between agencies prevent comprehensive financial visibility
  • Insufficient conversational intelligence increases time interpreting financial reports
  • Without AI-powered conversational intelligence, detecting anomalies requires extensive review
  • Lack of conversational intelligence tools hampers financial communication with stakeholders

  • Reduce financial close process time by 40% through automated data extraction and reconciliation
  • Increase anomaly detection accuracy by 65% using LLM pattern recognition across financial statements
  • Decrease manual report generation time by 75% with conversational intelligence interfaces
  • Achieve 30% cost savings in financial operations through agentic workflow automation

  • AI Guardrails for financial data accuracy and compliance assurance
  • Evaluation framework for continuous improvement of model performance
  • Human-in-the-loop feedback system for financial analysis validation
  • Multi-agent orchestration for complex financial workflow automation
  • Retrieval-augmented generation for accurate financial statement processing
  • Knowledge management system with version control for regulatory compliance

Enterprise Q&A Platform for Policy Analysis and Insights

As enterprises increasingly adopt AI for business transactions, there is a growing need for robust systems that ensure security, integrity, and trustworthiness of AI-driven decisions. Without strict governance, uncontrolled AI outputs can lead to compliance risks, data breaches, and eroded stakeholder trust.

  • Orchestrating multiple intelligent agents to work cohesively while maintaining performance.
  • Implementing strong guardrails to prevent AI misuse or generation of harmful outputs.
  • Ensuring the Large Language Model (LLM) can fairly and accurately act as an adjudicator (LLM as Judge).
  • Applying Human-in-the-Loop Fine-Tuning (HIFL) for continuous alignment with enterprise policies.
  • Balancing AI automation with enterprise-level data security and compliance mandates.
  • Gaining stakeholder trust in AI decision-making processes.

  • Provides a highly secure and controlled environment for AI-driven business transactions.
  • Enhances trust by integrating human oversight and clear ethical guardrails.
  • Reduces risk of data breaches and regulatory non-compliance.
  • Streamlines complex decision-making processes with AI adjudication.
  • Scalable and adaptable to evolving enterprise needs and policies.
  • Promotes responsible AI adoption within the enterprise.

  • Multi-Agent Orchestration: Coordinated intelligent agents handling different aspects of the transaction workflow.
  • Guardrails Framework: Predefined policies and restrictions to ensure safe AI outputs.
  • LLM as Judge: Large Language Model empowered to adjudicate, validate, and escalate decisions.
  • Human-in-the-Loop Fine-Tuning (HIFL): Continuous learning and oversight to refine AI behavior.
  • Enterprise-Grade Security: Encryption, role-based access control, and audit logging.
  • Compliance Monitoring: Real-time alerts and reporting to ensure adherence to legal and regulatory standards.
  • Transparent Interface: Clear visibility for stakeholders into AI decision-making processes.

AI-Agent for Application Review and Fraud Prevention

EDD processes a massive volume of applications daily, making it challenging to manually verify the authenticity of information and detect fraudulent or proxy submissions. Delays or errors in this process can lead to misuse of funds and erosion of public trust.

  • Detecting sophisticated fraud patterns and proxies in high volumes of applications.
  • Verifying authenticity of submitted information against trusted data sources.
  • Balancing thorough verification with the need for speedy application processing.
  • Continuously adapting to evolving fraudulent tactics and new data points.
  • Ensuring compliance with privacy regulations while handling sensitive personal data.

  • Automated, real-time application review reduces processing time.
  • Significant reduction in fraudulent claims and misuse of funds.
  • Improved accuracy and consistency in application evaluation.
  • Builds greater public trust through transparent and reliable processes.
  • Frees up human resources to focus on complex or edge cases.

  • AI-Powered Verification Engine: Automatically cross-checks applicant data against multiple trusted databases.
  • Fraud Detection Module: Uses pattern recognition and anomaly detection to flag suspicious applications.
  • Proxy Detection Algorithms: Identifies proxies or third-party submissions attempting to bypass the system.
  • Compliance and Security Framework: Ensures secure handling of sensitive data in line with regulations.
  • Continuous Learning Loop: Enhances detection accuracy by learning from past flagged cases.
  • Dashboard & Reporting: Provides EDD officers with clear, actionable insights and status updates.

Review and Recommendation of Special License Plates

DMV handles vast amounts of public and administrative data, but accessing specific information often requires manual queries or navigating complex systems. Staff and customers need a faster, more intuitive way to retrieve accurate information directly from the database without deep technical knowledge.

  • Translating natural language queries into accurate, executable SQL commands.
  • Ensuring real-time, secure access to sensitive DMV data.
  • Handling diverse query intents, from simple lookups to complex multi-table joins.
  • Maintaining data integrity and preventing misuse of query capabilities.
  • Building a scalable conversational system that supports growing user interactions.

  • Empowers both DMV staff and users to access real-time information quickly.
  • Reduces dependency on technical teams for routine data retrieval tasks.
  • Improves operational efficiency and customer satisfaction.
  • Enhances data transparency while maintaining strict security controls.
  • Scalable solution that can expand to accommodate new services and data needs.

  • Multi-Agent Conversational System: Specialized agents handle natural language understanding, query building, and response formatting.
  • SQL Agent: Converts natural language questions into optimized SQL queries for real-time database access.
  • Natural Language Processing (NLP): Enables intuitive, human-like interactions.
  • Data Security Layer: Ensures only authorized data is retrieved, with role-based access and query validation.
  • Error Handling & Feedback Loop: Provides informative responses and suggestions when queries cannot be processed.
  • Scalable Infrastructure: Supports increasing volume of queries and data growth over time.
  • Dashboard & Analytics: Monitors usage patterns and system performance for continuous improvement.

Global Product Search and Work Order Analysis Using Text, Voice, Image, and Audio

Otis needed an intelligent solution to help users efficiently access complex product and service information from an extensive catalog. Traditional search methods were limited, often requiring manual effort and technical familiarity. Users required a more natural, conversational, and multimodal way to engage with documentation and knowledge resources.

  • Building an intelligent system that accurately understands and retrieves data across audio, image, and text inputs.
  • Structuring and indexing the catalog effectively for high-performance retrieval.
  • Ensuring accurate retrieval from vast knowledge sources (RAG) and avoiding irrelevant or outdated responses.
  • Handling different user intents and contexts across various modalities.
  • Maintaining fast response times and scalability as the knowledge base grows.
  • Enabling seamless interaction regardless of user’s preferred input format (voice, image, or text).

  • Dramatically improves accessibility to complex catalog information.
  • Provides a natural, intuitive user experience across multiple input types.
  • Reduces time spent searching through documents and manuals.
  • Scales effortlessly with growing product lines and document updates.
  • Supports diverse user needs, including visual and audio-based interactions.
  • Increases user engagement and satisfaction with fast, accurate answers.

  • Gen AI-Powered Q&A Engine: Uses advanced language models to understand queries and provide precise answers.
  • Retrieval-Augmented Generation (RAG): Combines real-time retrieval with generative AI for high-accuracy responses.
  • Multimodal Input Handling: Supports text, audio, and image-based queries.
  • Catalog Knowledge Base: Centralized, well-indexed repository of Otis’ product and service information.
  • Semantic Search Layer: Improves search relevance using contextual understanding.
  • Audio & Visual Processing Pipelines: Enables voice queries and image-based document interpretation.

Global Customer Care and Product Support

P&G needed an intelligent, always-available customer care solution to handle a wide range of inquiries from retail customers. Traditional customer service channels were overwhelmed with repetitive queries, leading to delays in response times and inconsistent service experiences.

  • Handling a high volume of customer inquiries across various product lines and retail segments.
  • Understanding diverse customer intents, including complaints, product info, order tracking, and troubleshooting.
  • Maintaining brand consistency and a high standard of customer service through AI responses.
  • Integrating with existing customer databases, order systems, and support knowledge bases.
  • Ensuring multilingual support for global customer reach.
  • Building trust by providing accurate, helpful, and empathetic responses.

  • 24/7 customer support with instant response times.
  • Reduced operational load on human customer service teams.
  • Consistent, high-quality responses aligned with P&G’s brand voice.
  • Improved customer satisfaction and engagement.
  • Scalable to support new products, promotions, and seasonal demand surges.
  • Actionable insights from customer interaction data for continuous improvement.

  • Conversational AI Engine: Understands and responds to customer queries naturally.
  • Retail-Specific Knowledge Base: Product details, troubleshooting guides, FAQs, and promotional info.
  • Integration Layer: Connects with CRM, order management, and logistics systems for real-time updates.
  • Sentiment Analysis Module: Detects customer emotion and prioritizes responses accordingly.
  • Multilingual Support: Enables global customer assistance.
  • Continuous Learning System: Improves over time based on real customer interactions and feedback.

Energy Efficiency: Facility Management

Facility managers at Ngee Ann Polytechnic faced challenges in optimizing energy usage and maintaining infrastructure efficiently across multiple buildings. Traditional methods of managing space utilization, building layouts, asset tracking, and maintenance were time- consuming, reactive, and lacked data-driven insights. They needed an intelligent solution to proactively manage facilities, reduce energy consumption, and extend asset lifespan.

  • Consolidating data from diverse facility systems and energy monitoring tools.
  • Providing actionable insights for space and asset optimization in real-time.
  • Designing AI models to recommend optimal building layouts for efficiency.
  • Predicting maintenance needs for critical equipment (e.g., water pumps) before failures occur.
  • Ensuring AI recommendations align with environmental sustainability goals.
  • Integrating a user-friendly interface for facility managers to query insights easily.

  • Improved energy efficiency across campus facilities, reducing operational costs.
  • Proactive maintenance reduces equipment downtime and extends asset life.
  • Optimized space utilization leads to better planning for future campus growth.
  • Enhanced visibility into asset and inventory management.
  • Scalable solution that can grow with campus expansion and new data sources.
  • Empower facility managers with self-service, natural language insights via Generative AI.

  • Space Optimization Module: Analyzes space usage data to recommend efficient layouts and utilization strategies.
  • Building Layout Recommendation Engine: AI-powered suggestions for layout improvements based on traffic flow and energy efficiency.
  • Asset & Inventory Management System: Tracks and monitors assets, ensuring timely replenishment and maintenance.
  • Predictive Maintenance (Water Pump): Uses sensor data and AI to predict and prevent equipment failures.
  • Natural Language Interface: Allows facility managers to interact with data using conversational queries.
  • Data Integration Layer: Aggregates inputs from energy meters, IoT sensors, and facility databases.
  • Analytics & Reporting Dashboard: Provides actionable insights and trend analysis for ongoing improvements.

Worldwide Customer Engagement and Pricing Promotional Search

Marriott manages extensive data sets containing resort codes, hotel details, and associated charges. Manually analyzing these CSV data files is time-consuming and requires technical expertise. Marriott needed an intelligent assistant to enable natural language interactions and extract insights from this complex data effortlessly.

  • Parsing and interpreting large, multi-dimensional CSV datasets efficiently.
  • Translating natural language queries into executable data analysis code.
  • Ensuring accuracy in interpreting hotel-specific metrics like resort codes, room charges, service fees, etc.
  • Providing meaningful visualizations and summaries from raw data.
  • Maintaining data privacy and secure handling of sensitive financial information.
  • Enabling iterative analysis with conversational follow-ups for deeper insights.

  • Empowers non-technical users to explore data and gain actionable insights easily.
  • Accelerates decision-making with instant, conversational data analysis.
  • Reduces reliance on data analysts for routine queries.
  • Supports complex queries, aggregations, and trend analysis in real-time.
  • Delivers clear visualizations and summaries for executive presentations.
  • Enhances operational efficiency across departments.

  • Conversational AI Assistant: Enables natural language Q&A over CSV data.
  • Code Interpreter (Python-powered): Dynamically writes and executes code to process data and generate insights.
  • Data Ingestion Module: Securely processes large CSV files containing resort codes, hotel data, and charges.
  • Insight Generator: Summarizes key metrics and trends such as occupancy rates, revenue by region, and pricing patterns.
  • Visualization Toolkit: Auto-generates charts and tables to support data storytelling. 
  • Security & Compliance Layer: Ensures sensitive financial data is protected during analysis.
  • Follow-up Query Handling: Allows users to dig deeper into insights through iterative questioning.

Faculty Agent: Content Generation, Assessment, and Evaluations

Educators and faculty members often spend considerable time manually creating quizzes, assessments, and study materials from various sources. This process is not only time-intensive but also limits the ability to frequently update or personalize learning content. There was a need for an AI-powered assistant that could automate question generation and evaluation based on any input material, enhancing teaching efficiency and learning outcomes.

  • Ingesting and understanding unstructured content from multiple formats (TXT, PDF, URLs, etc.).
  • Generating diverse types of questions (MCQs, fill-in-the-blanks, short answers, etc.) with appropriate difficulty levels.
  • Ensuring the accuracy and relevance of generated content to avoid misleading or incorrect questions.
  • Providing evaluation capabilities to review and score answers effectively.
  • Maintaining academic integrity and preventing AI-generated bias in question sets.
  • Designing an intuitive interface for educators with minimal learning curve
    • .

  • Significantly reduces time spent by faculty on content creation and assessment design.
  • Enables rapid generation of quizzes and tests tailored to specific topics and difficulty levels.
  • Enhances learning outcomes through diverse question types and evaluation methods.
  • Scales effortlessly for large curriculam and frequent content updates.
  • Supports multiple content sources, increasing flexibility for educators.
  • Provides automated evaluation for faster feedback to learners.

  • Content Ingestion Module: Supports various input formats like TXT, PDF, web URLs, etc.
  • Question Generation Engine: Creates different types of questions (MCQ, fill-in-the-blanks, short/long answers) from the source material.
  • Difficulty Level Control: Adjusts question complexity based on faculty preferences.
  • Evaluation System: Assesses student answers and provides automated scoring and feedback.
  • Natural Language Processing (NLP): Understands and extracts key concepts from source material.
  • Faculty-Friendly Interface: Easy-to-use platform for educators to generate and manage assessments.

Global Call Center Modernization

Call centers often suffer from impersonal customer service, leading to frustration as customers repeatedly explain their issues or share account details. Without context-aware tools, agents and bots alike struggle to deliver fast, relevant, and personalized support, resulting in longer resolution times and lower customer satisfaction.

  • Real-time integration of fragmented customer data (profile, past orders, call history).
  • Maintaining conversation continuity and context across multiple interactions.
  • Ensuring AI understands varied intents and provides appropriate, tailored responses.
  • Balancing personalized service with strict data privacy and regulatory compliance.
  • Building natural, human-like conversational flows during live calls.
  • Seamlessly escalating to human agents with full context transfer.

  • Highly personalized customer interactions, improving customer loyalty and satisfaction.
  • Shorter call durations and faster issue resolution.
  • Reduced workload for human agents by handling routine inquiries effectively.
  • Better insights into customer preferences and behavior for continuous improvement.
  • Scalable and adaptable system for growing customer bases.
  • Strengthens customer trust through consistent and context-aware service.

  • AI-Powered Personalization Engine: Utilizes user profiles and history to customize responses.
  • Real-Time Data Access: Pulls in live data such as order status, previous tickets, and conversation history.
  • Conversational Memory: Remembers prior interactions to maintain a seamless experience.
  • Sentiment Analysis & Intent Detection: Adjusts tone and approach based on customer mood and query type.
  • Secure Data Handling: Adheres to best practices for privacy, encryption, and compliance.
  • Smart Escalation Workflow: Transfers complex cases to human agents with complete context.
  • Analytics & Reporting: Provides actionable insights from customer interactions for business improvement.

Guidelines for Establishing Advanced
Guardrail Systems in Enterprises

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