This guide walks you through how to design a use case for your unique business needs and leverage SeekrFlow's explainability features to maintain trustworthiness and human oversight during each step of the process.

SeekrFlow's core capabilities

First, let's talk about what makes a comprehensive AI enablement platform like SeekrFlow:

Data conversion, creation, and augmentation
Model integration and orchestration
Development tools for both technical and non-technical users
Deployment infrastructure with monitoring
Explainability mechanisms
Human-in-the-loop workflows
Security and compliance alignment

Each of these core capabilities is available throughout the development process, whether you're building a relatively straightforward virtual assistant, or a more complex agentic system. Read on for some use case inspiration across several business verticals, complete with potential document sources and collection strategies, and examples of expected business impact:

Industry-specific use cases

Terminology:

Standard fine-tuning workflow: Where a generalist model is fine-tuned to follow specific directions and complete tasks based on user instructions (e.g., a virtual assistant that answers questions about policies).
Standard retrieval workflow: A workflow that combines a generative AI model with an information retrieval system that dynamically fetches relevant external data, integrates it into the model's context, and generates precise, grounded responses. This might also take the form of a virtual assistant, but with more context-aware responses tailored to the user's specific query than the fine-tuned version.
Agentic workflow: These systems operate with greater autonomy to pursue human-set goals across multiple steps: making decisions, using tools, and taking independent actions while maintaining the ability to adapt to new information and changing conditions (e.g., an assistant that can access external, up-to-date data sources, or decide to escalate a customer ticket).
HITL: Human-in-the-loop describes an approach that allows you to review and modify model outputs to ensure accuracy and alignment with your unique guidelines, while providing feedback that improves system performance.

Government/Federal applications

	Standard fine-tuning or context-grounded workflow	Agentic workflow
Example Use Case	Automated document summarization for policy briefings with explainable citations	Fraud detection systems that flag suspicious patterns for human review
Domain-Specific Elements	Regulatory language, legal frameworks, and agency-specific terminology.	Agency-specific fraud patterns, jurisdiction-based enforcement criteria
Document Sources	Federal regulations, agency handbooks, court decisions, policy manuals, precedent documents, and interagency communications	Historical transaction data, fraud case files, customer profiles with normal behavior patterns, regulatory requirements, investigation procedures, alert handling protocols, performance metrics
Expected Business Impact	Enhanced policy analysis with 40% faster brief generation and 25-30% reduction in citizen services	Estimated 30-40% decrease in manual review time with potential to identify 15-25% more fraudulent activities
HITL Component	Managers or officials approve AI-generated content before publication and can provide feedback to improve future outputs	Analysts investigate flagged patterns and determine their risk level, then provide feedback to improve future outputs

Manufacturing and supply chain

	Standard fine-tuning or context-grounded workflow	Agentic workflow
Example Use Case	Predictive maintenance reports based on equipment sensor data	Autonomous inventory management that orders supplies when thresholds are reached
Domain-Specific Elements	Production processes, equipment tolerances, supply chain dependencies	Lead time variations, supplier reliability metrics, seasonal demand patterns
Document Sources	Equipment manuals, maintenance logs, quality control standards, vendor specifications	Inventory master data, supplier agreements and fulfillment records, threshold configuration documentation, cost management frameworks, performance metric standards (e.g., service level targets, stockout tolerance thresholds)
Expected Business Impact	15-25% decrease in overall maintenance expenses with 20-30% improvement in maintenance labor utilization	10-20% decrease in inventory holding costs with 30-50% reduction in stockout events
HITL Component	Production managers can review and adjust AI recommendations for critical equipment	Production managers can review and adjust AI purchases of critical equipment

Media and publishing

	Standard fine-tuning or context-grounded workflow	Agentic workflow
Example Use Case	Content personalization across platforms with brand voice adaptation	Automated content moderation with escalation paths for borderline cases
Domain-Specific Elements	Content rights management, publishing workflows, audience engagement metrics	Platform-specific community guidelines, cultural context sensitivity
Document Sources	Style guides, published content archives, audience research, competitor analysis	Moderation precedents, policy documents, context-dependent examples
Expected Business Impact	20-30% reduction in content production costs, with 15-25% higher conversion rates	50-70% decrease in policy violation exposure time with 40-60% reduction in human moderation workload
HITL Component	Editors review AI-generated headlines and summaries before publication	Community moderators can review and adjust flagged content violations

Hospitality and travel logistics

	Standard fine-tuning or context-grounded workflow	Agentic workflow
Example Use Case	Personalized itinerary creation with explanation of recommendations	Dynamic pricing systems that adjust based on demand patterns
Domain-Specific Elements	Seasonal travel patterns, regulatory requirements, loyalty program structures	Competitive rate intelligence, demand forecasting patterns, event impact analysis
Document Sources	Guest history, booking terminology, destination descriptions, service amenities	Historical pricing performance, competitor positioning, market response patterns
Expected Business Impact	10-15% increase in average booking value with 25-30% increase in NPS	8-12% increase in profit margin with 15-20% reduction in unbooked inventory
HITL Component	Customer service representatives can override AI-suggested solutions	Pricing systems can be reviewed and adjusted for unusual demand patterns, or price changes outside a certain range

Financial services

	Standard fine-tuning or context-grounded workflow	Agentic workflow
Example Use Case	Risk assessment reports with highlighted factors and confidence levels	Anomaly detection for transactions that autonomously escalates suspicious activity
Domain-Specific Elements	Market mechanics, financial products, regulatory frameworks	Transaction pattern baselines, risk scoring models, regulatory thresholds
Document Sources	Regulatory filings, market reports, financial statements, compliance documents, reporting templates	Historical transaction data, regulatory compliance documents, escalation protocols, risk assessment frameworks
Expected Business Impact	40% faster report generation with 25-30% reduction in risk exposure	40-60% improvement in fraud detection rates with 60-80% faster processing of legitimate transactions
HITL Component	Financial analysts review model outputs for major investment decisions	Analysts review and adjust flagged activity and provide feedback on accuracy to improve future outputs

Customer support

	Standard fine-tuning or context-grounded workflow	Agentic workflow
Example Use Case	Knowledge base article generation with source attribution	Support ticket routing and prioritization with automatic responses for common issues
Domain-Specific Elements	Product terminology, service procedures, product capabilities	Support escalation paths, troubleshooting procedures, technical specifications, regulatory or compliance-related urgency factors, solution documentation structure, customer segmentation criteria
Document Sources	Product documentation, knowledge base articles, resolved ticket history, FAQs	SLA documentation, prioritization frameworks, knowledge base articles, support team structure, historical ticket database, response templates, reporting templates, performance metrics (success, customer satisfaction, accuracy, QA review processes)
Expected Business Impact	30-40% reduction in support ticket filing, with 15-20% increase in customer retention	30-50% improvement in first-contact resolution with 15-25% increase in CSAT scores
HITL Component	Customer support can review articles and sources for accuracy and appropriateness and adjust accordingly	Support managers review alerts for potentially problematic interactions and adjust as needed