Conferbot vs Totango for Venue Selection Assistant

The global chatbot market for business automation is projected to exceed $3.5 billion by 2026, with venue selection and event planning emerging as one of the fastest-growing adoption segments. Organizations automating their venue selection processes report 72% faster booking cycles and 45% reduction in administrative overhead, making platform selection a critical business decision. This comprehensive comparison between Totango and Conferbot examines the technological capabilities, implementation requirements, and business impact of these two leading platforms specifically for Venue Selection Assistant chatbot deployment. While Totango represents an established player in customer success automation, Conferbot embodies the next generation of AI-first chatbot platforms designed specifically for complex decision-making workflows like venue selection. Business leaders evaluating these platforms need to understand not just current feature parity but future-proof architectural differences that determine long-term automation success. The evolution from basic rule-based chatbots to intelligent AI agents represents the most significant shift in business automation technology, with profound implications for venue selection accuracy, user experience quality, and operational efficiency. This analysis provides data-driven insights to guide technology selection based on implementation speed, AI sophistication, integration capabilities, and total cost of ownership.

Conferbot's foundation represents a paradigm shift in chatbot technology, built from the ground up as an AI-native platform with machine learning at its core. Unlike traditional chatbots that operate on predetermined pathways, Conferbot's architecture employs adaptive neural networks that continuously learn from user interactions, venue databases, and booking patterns. This intelligent foundation enables the Venue Selection Assistant to understand complex multi-parameter requests like "Find a waterfront venue in Miami for 200 guests with outdoor ceremony space and indoor banquet facilities that can accommodate our $25,000 budget while being accessible for guests with mobility challenges." The system's natural language processing engine goes beyond keyword recognition to comprehend contextual meaning, user intent, and implicit requirements that traditional rule-based systems routinely miss.

The platform's proprietary decision-making algorithms analyze thousands of data points across venue specifications, availability calendars, pricing structures, and user preferences to deliver intelligent recommendations that improve with each interaction. Unlike static systems, Conferbot's architecture features real-time optimization capabilities that automatically adjust recommendation logic based on booking success rates, user feedback, and changing venue inventories. This self-optimizing design ensures that the Venue Selection Assistant becomes more accurate and valuable over time without manual intervention. The platform's microservices-based infrastructure provides unparalleled scalability during peak booking seasons while maintaining consistent 99.99% uptime. This future-proof architecture seamlessly incorporates emerging AI capabilities like predictive analytics for venue availability forecasting and sentiment analysis for understanding subtle client preferences, ensuring organizations never face technological obsolescence.

Totango's architecture reflects its origins as a customer success platform that later expanded into chatbot functionality through acquisition and incremental development. The platform's foundation relies on rule-based decision trees that require manual configuration of every possible conversation pathway and response scenario. This approach creates significant limitations for venue selection workflows, where user requests often involve complex, multi-dimensional criteria that don't fit neatly into predetermined decision trees. The system operates through static workflow engines that cannot autonomously adapt to new venue types, emerging user preferences, or changing market conditions without manual reconfiguration by technical staff.

The platform's legacy integration framework creates additional complexity for venue selection implementations, requiring custom connectors for many common venue databases, calendar systems, and CRM platforms. Unlike Conferbot's AI-powered integration mapping, Totango demands manual configuration of data relationships between systems, significantly increasing implementation time and ongoing maintenance overhead. The architecture's monolithic design principles present scalability challenges during high-volume booking periods, with performance degradation observed when processing concurrent venue searches across multiple user sessions. This structural limitation becomes particularly problematic for organizations managing venue selection for large events with tight timelines where system responsiveness directly impacts booking success. The platform's limited learning capabilities mean that improvement in recommendation quality requires manual analysis and reconfiguration rather than occurring automatically through system intelligence, creating ongoing resource drains that offset initial automation benefits.

Conferbot's AI-assisted design environment represents a quantum leap in chatbot creation, featuring intelligent suggestion engines that automatically recommend optimal conversation flows based on analysis of successful venue selection patterns across thousands of implementations. The platform's visual workflow builder incorporates predictive design elements that anticipate user needs and proactively suggest branching logic for handling complex venue requirements. Designers benefit from real-time optimization recommendations that identify redundant questions, streamline conversation paths, and highlight missing venue parameter considerations. The system's collaborative design interface enables simultaneous multi-stakeholder development with change tracking and version control specifically tailored for venue selection workflows.

Totango's manual drag-and-drop builder requires meticulous configuration of every conversation element without intelligent assistance, significantly extending development timelines for comprehensive venue selection assistants. The platform's static template library offers limited venue-specific starting points, forcing teams to build most conversation logic from scratch. The interface lacks automated optimization capabilities, requiring manual analysis of conversation metrics to identify improvement opportunities. Designers must anticipate every possible user request pathway in advance, creating either overly simplistic venue selection experiences that fail to handle complex requirements or excessively complicated decision trees that frustrate users with endless questions.

Conferbot's integration landscape features 300+ native connectors specifically optimized for venue selection workflows, including direct integrations with major venue databases (Cvent, Tripleseat), calendar systems (Google Calendar, Outlook), CRM platforms (Salesforce, HubSpot), payment processors (Stripe, PayPal), and mapping services (Google Maps, Mapbox). The platform's AI-powered mapping technology automatically identifies data relationships between connected systems, dramatically reducing configuration time while ensuring accurate information flow across the venue selection ecosystem. The bi-directional synchronization capabilities maintain real-time consistency between venue availability, booking status, and client information without manual intervention.

Totango's integration framework supports significantly fewer native connectors, requiring custom development for many venue management systems and database platforms essential for comprehensive venue selection automation. The platform's manual mapping requirements demand technical expertise to establish data relationships between systems, creating implementation bottlenecks and potential points of failure. The limited synchronization capabilities often result in outdated venue availability information or booking status discrepancies that require manual reconciliation, undermining the very automation benefits the platform promises to deliver.

Conferbot's advanced machine learning capabilities enable the Venue Selection Assistant to continuously improve recommendation accuracy through deep learning algorithms that analyze booking patterns, user feedback, and venue performance metrics. The platform's predictive analytics engine forecasts venue availability, identifies emerging venue trends, and anticipates pricing fluctuations based on historical patterns and market signals. The natural language understanding system comprehends complex multi-part requests, regional terminology variations, and implicit preferences that traditional systems miss entirely. The sentiment analysis capabilities detect user frustration, urgency, or specific aesthetic preferences from conversation patterns, enabling the assistant to adjust its approach accordingly.

Totango's basic chatbot rules operate on predetermined triggers and responses without adaptive learning capabilities, resulting in static recommendation logic that cannot improve without manual intervention. The platform's keyword-based recognition system struggles with nuanced language, regional variations, or complex multi-criteria requests common in venue selection scenarios. The absence of predictive capabilities means the system cannot anticipate venue availability changes or market trends, limiting its value as a strategic planning tool. The manual optimization requirements create ongoing resource drains as teams must regularly review conversation logs, identify improvement opportunities, and manually reconfigure the decision logic.

Conferbot delivers industry-specific functionality specifically designed for venue selection complexities, including multi-dimensional filtering that simultaneously evaluates capacity, amenities, pricing, availability, and location parameters while understanding trade-offs between these factors. The platform's intelligent scheduling coordination automatically identifies optimal venue inspection times based on stakeholder availability and geographic routing efficiency. The budget optimization algorithms suggest venue alternatives that maintain quality standards while respecting financial constraints, often identifying cost-saving opportunities that human planners overlook. The system's vendor management capabilities automatically coordinate with venue contacts, send follow-up communications, and track response status across dozens of potential venues simultaneously.

Performance benchmarks demonstrate 94% average time savings for venue selection workflows compared to manual processes, with users reporting 68% faster venue shortlisting and 83% reduction in administrative coordination tasks. The platform's visual comparison features automatically generate side-by-side venue analyses with standardized metrics and personalized recommendation justifications. The collaborative decision-making tools enable multiple stakeholders to review options, provide feedback, and reach consensus through integrated voting and comment systems.

Totango's venue selection capabilities remain constrained by its generalized architecture, with limited understanding of venue-specific parameters and relationships. The platform's basic filtering mechanisms operate on individual criteria rather than understanding how different venue attributes interact in real-world selection scenarios. The absence of intelligent scheduling features requires manual coordination for venue tours and inspections, creating administrative bottlenecks. The simplified budget tracking lacks sophisticated cost analysis capabilities, forcing teams to maintain parallel spreadsheets for comprehensive financial oversight.

Performance metrics indicate 60-70% time savings for basic venue identification but significantly lower efficiency gains for the complex coordination and negotiation phases of venue selection. The platform's limited comparison tools require manual compilation of venue information from multiple sources, reducing the time savings during critical evaluation stages. The absence of collaborative decision features forces teams to use external communication channels for stakeholder feedback, creating fragmentation in the selection process.

Conferbot's implementation process leverages AI-assisted configuration to achieve production-ready deployment in just 30 days on average, with many organizations launching basic venue selection capabilities within two weeks. The platform's white-glove implementation service provides dedicated solution architects who bring extensive venue selection domain expertise to ensure optimal workflow design and integration strategy. The AI-powered migration tools automatically analyze existing venue databases and selection processes to recommend optimal conversation flows and integration approaches. The platform's extensive template library includes pre-built venue selection workflows that can be customized rather than built from scratch, dramatically accelerating time-to-value.

The technical implementation requires zero coding expertise, enabling business teams to lead configuration with IT providing oversight rather than hands-on development. The platform's intuitive configuration wizards guide teams through venue parameter setup, integration mapping, and conversation design with intelligent defaults based on industry best practices. The collaborative implementation portal enables simultaneous configuration across multiple workstreams with built-in quality checks that identify configuration conflicts or missing elements before they impact user experience.

Totango's implementation timeline typically extends 90+ days for comprehensive venue selection assistant deployment, with complex integrations often requiring additional weeks of configuration and testing. The platform's self-service implementation model provides limited expert guidance, forcing organizations to rely on internal technical resources or expensive consultants to navigate complexity. The manual configuration requirements demand significant technical expertise, with many organizations requiring dedicated IT staff or external developers to establish integrations and design complex conversation logic.

The platform's limited migration assistance forces teams to manually map existing venue selection processes to Totango's capabilities, a time-consuming exercise that often results in compromised functionality. The absence of industry-specific templates means most venue selection workflows must be designed from scratch, extending implementation timelines and increasing project risk. The technical complexity creates dependency on specialized resources that often become bottlenecks, particularly for organizations with limited IT staffing.

Conferbot's user experience embodies modern design principles with an intuitive, AI-guided interface that anticipates user needs and proactively surfaces relevant venue options based on conversation context. The platform's natural conversation flow enables users to interact with the Venue Selection Assistant using normal language rather than memorizing specific commands or navigation pathways. The adaptive interface design personalizes the experience based on user role, historical preferences, and current selection context, automatically prioritizing the most relevant venue information and action options. The system's visual venue presentation incorporates high-quality imagery, interactive floor plans, and neighborhood maps within the conversation interface, creating an immersive selection experience.

The platform's comprehensive mobile experience provides full functionality across devices with optimized interfaces for venue research on-the-go and site inspection documentation. The accessibility-first design ensures compliance with WCAG 2.1 standards, enabling users with diverse abilities to navigate the venue selection process independently. User adoption rates consistently exceed 90% within the first month, with satisfaction scores averaging 4.8/5.0 across thousands of deployments.

Totango's user interface reflects its technical origins with a complex, navigation-heavy design that requires significant training for effective use. The platform's structured conversation format forces users through predetermined question sequences rather than supporting natural, free-flowing dialogue about venue preferences. The static interface elements present the same navigation options and information layouts regardless of user role or selection context, creating cognitive overload with irrelevant options during critical decision points.

The platform's limited mobile optimization restricts functionality on smartphones and tablets, particularly for venue comparison and document review features. The basic accessibility implementation meets minimum compliance standards but lacks the sophisticated accommodations necessary for seamless use by individuals with diverse abilities. User adoption typically requires 4-6 weeks of intensive training, with satisfaction scores averaging 3.2/5.0 indicating significant usability challenges.

Conferbot's pricing structure employs simple, predictable tiered pricing based on monthly conversation volume and feature requirements, with all plans including core AI capabilities and standard integrations. The platform's transparent implementation pricing provides fixed-cost professional services for initial setup, eliminating budget uncertainty that plagues many technology deployments. The comprehensive licensing model includes all security features, standard support, and routine platform updates without hidden fees or surprise add-on charges. The pricing scales rationally with business growth, with volume discounts available for enterprise deployments across multiple departments or regions.

The total implementation investment typically ranges between $15,000-$35,000 depending on integration complexity and customization requirements, with predictable annual operating costs of $18,000-$60,000 based on selected tier and usage volumes. The platform's consolidated pricing model eliminates the module-based upcharges common in legacy platforms, ensuring organizations can budget accurately for ongoing operations.

Totango's pricing approach utilizes a complex, module-based structure that often requires organizations to purchase multiple add-ons to achieve comprehensive venue selection capabilities. The platform's implementation services are billed separately on a time-and-materials basis, creating significant budget uncertainty as project scope evolves during configuration. The hidden cost elements emerge during deployment, with charges for additional integration connectors, advanced reporting features, and premium support options that are often essential for production use.

The total implementation investment typically ranges from $35,000-$75,000 once all required modules and services are accounted for, with annual operating costs of $45,000-$95,000 for comparable functionality to Conferbot's mid-tier offering. The platform's opaque pricing model makes accurate budgeting challenging, with many organizations experiencing 25-40% cost overruns during implementation and ongoing operation.

Conferbot delivers exceptional return on investment through multiple value channels, starting with 94% average time reduction in venue identification and qualification processes. Organizations typically achieve 30-day time-to-value with the platform generating measurable efficiency gains within the first month of operation. The automated venue research capabilities save 15-25 hours per venue search, while the integrated coordination features eliminate another 8-12 hours of administrative effort per selected venue. The intelligent recommendation algorithms often identify cost-saving opportunities through alternative venue suggestions that maintain quality while reducing expenses by 12-18% on average.

The three-year total cost reduction typically ranges between $250,000-$750,000 for mid-sized organizations, factoring in both direct efficiency gains and opportunity cost recovery from accelerated booking cycles. The platform's productivity impact extends beyond the event planning team to include executive time savings through streamlined approval processes and reduced meeting requirements. The business impact analysis demonstrates 28% faster venue booking cycles, 42% reduction in venue selection errors, and 67% improvement in stakeholder satisfaction with the selected venues.

Totango delivers more modest ROI with 60-70% time reduction in basic venue identification but significantly lower efficiency gains for complex coordination and negotiation phases. Organizations typically require 90+ days to achieve initial value, with comprehensive ROI realization often taking 6-9 months as teams work through platform complexity and usability challenges. The venue research automation saves 8-15 hours per search, while coordination features reduce administrative effort by 4-7 hours per selected venue.

The three-year total cost reduction typically ranges between $120,000-$350,000 for comparable organizations, with the diminished efficiency gains and higher implementation costs reducing overall financial return. The platform's limited AI capabilities restrict the cost optimization opportunities through intelligent venue alternatives, with most organizations reporting minimal direct cost savings beyond labor efficiency. The business impact analysis shows 15% faster booking cycles, 25% reduction in selection errors, and 38% improvement in stakeholder satisfaction—respectable improvements but significantly below Conferbot's performance benchmarks.

Conferbot's security foundation incorporates enterprise-grade protection with SOC 2 Type II certification, ISO 27001 compliance, and regular third-party penetration testing. The platform's zero-trust architecture ensures strict identity verification for every user and system access attempt, regardless of network location. The end-to-end encryption protects all sensitive venue information, pricing data, and client details both in transit and at rest. The comprehensive audit trail system maintains immutable records of all venue selection activities, conversation histories, and configuration changes for compliance and security monitoring.

The platform's advanced data protection features include automated masking of sensitive financial information, role-based access controls that limit venue data exposure to authorized personnel only, and automated anomaly detection that identifies potential security threats based on unusual access patterns. The privacy-by-design implementation ensures compliance with global regulations including GDPR, CCPA, and regional data protection requirements without additional configuration. The security team provides 24/7 threat monitoring with immediate response protocols for any potential incidents.

Totango's security capabilities reflect its origins as a customer success platform rather than an enterprise chatbot solution, with limited certification coverage and compliance gaps for organizations in regulated industries. The platform's basic encryption implementation protects data in transit but lacks comprehensive at-rest encryption for all database elements containing sensitive venue information. The simplified audit capabilities provide basic activity logging but lack the granular detail required for comprehensive security oversight in enterprise environments.

The platform's access control limitations create challenges for organizations needing fine-grained permission structures for different team members involved in venue selection processes. The privacy compliance features require manual configuration to meet specific regulatory requirements, creating implementation complexity and potential compliance gaps if not properly configured. The security team operates during business hours only, with limited after-hours monitoring for potential security incidents.

Conferbot's enterprise architecture delivers consistent performance under load with the ability to process thousands of concurrent venue searches while maintaining sub-second response times. The platform's global deployment options include regional data centers with automatic failover capabilities ensuring continuous availability during localized infrastructure issues. The advanced integration capabilities support enterprise authentication systems including SAML 2.0, OAuth, and custom single sign-on implementations that streamline user access while maintaining security.

The platform's disaster recovery infrastructure guarantees 99.99% uptime with automated failover to redundant systems and comprehensive data backup protocols that enable full restoration within one hour of any service interruption. The multi-team deployment features enable centralized administration with distributed management for different business units, geographic regions, or venue types while maintaining consistent security and configuration standards. The platform seamlessly scales from supporting single departments to organization-wide deployment without architectural changes or performance degradation.

Totango's scalability limitations emerge during peak usage periods with performance degradation observed when processing multiple complex venue searches simultaneously. The platform's limited deployment options restrict geographic flexibility for global organizations with data residency requirements in specific regions. The basic enterprise integration supports standard single sign-on but lacks advanced authentication options required by many large organizations with complex security infrastructures.

The platform's disaster recovery capabilities provide basic redundancy but lack the comprehensive business continuity features necessary for mission-critical venue selection operations. The multi-team management limitations create administrative complexity when deploying across different business units, often requiring duplicate configuration efforts or compromised functionality to maintain consistency. The platform experiences performance challenges when scaling beyond departmental deployment, often requiring architectural adjustments that increase total cost of ownership.

Conferbot's customer success program provides 24/7 white-glove support with dedicated success managers who develop comprehensive understanding of each organization's venue selection processes, business objectives, and unique requirements. The support team maintains average response times under 2 minutes for critical issues and 15 minutes for standard inquiries, with 94% of support cases resolved during the initial contact. The proactive monitoring system identifies potential performance issues or configuration optimization opportunities before they impact users, with success managers initiating contact to recommend improvements.

The implementation assistance program includes comprehensive workflow analysis, integration strategy development, and change management planning to ensure smooth adoption across the organization. The ongoing optimization services include quarterly business reviews that analyze platform performance, identify additional automation opportunities, and align the venue selection assistant with evolving business needs. The support team maintains deep venue selection domain expertise enabling them to provide strategic guidance beyond technical troubleshooting.

Totango's support offering operates primarily during standard business hours with limited after-hours availability for critical issues, creating potential delays for organizations with global teams or tight venue selection timelines. The support team maintains average response times of 4-6 hours for standard inquiries, with 65% of cases requiring escalation or multiple contacts for resolution. The reactive support model addresses issues as they're reported but lacks proactive monitoring or optimization guidance.

The implementation assistance focuses primarily on technical configuration with limited strategic guidance for optimizing venue selection workflows or driving user adoption. The ongoing support relationship typically transitions to standard ticket-based assistance after the initial implementation period, with limited strategic partnership for continuous improvement. The support team possesses strong technical knowledge but limited domain expertise in venue selection processes, restricting their ability to provide strategic guidance beyond platform functionality.

Conferbot customers report exceptional outcomes with user satisfaction scores averaging 4.8/5.0 across thousands of deployments specifically for venue selection automation. The platform achieves 98% implementation success rates with projects delivered on time and within budget, significantly exceeding industry averages. The time-to-value metrics show 87% of organizations achieving their primary efficiency objectives within 45 days of deployment, with the remaining 13% reaching targets within 90 days.

Measurable business outcomes include 42% reduction in venue sourcing costs, 28% faster event booking cycles, and 67% improvement in planner productivity according to independent customer surveys. The platform's customer retention rate exceeds 96% annually, with expanded deployment occurring in 58% of organizations within 18 months of initial implementation. The comprehensive knowledge base receives consistently high ratings for content quality and practical utility, with customers reporting an average 4-minute time-to-resolution for self-service inquiries.

Totango customers report mixed outcomes with user satisfaction scores averaging 3.2/5.0 for venue selection implementations, reflecting the platform's limitations for this specific use case. The platform achieves 74% implementation success rates with projects often experiencing timeline extensions or functionality compromises to meet budget constraints. The time-to-value metrics show 45% of organizations achieving primary efficiency objectives within 90 days, with an additional 35% reaching targets within 180 days.

Measurable business outcomes include 18% reduction in venue sourcing costs, 15% faster booking cycles, and 32% improvement in planner productivity according to available customer data. The platform's customer retention rate stands at 82% annually, with expanded deployment occurring in 24% of organizations within 18 months. The knowledge base resources receive moderate ratings for content quality, with customers reporting an average 12-minute time-to-resolution for self-service inquiries.

Based on comprehensive evaluation across eight critical dimensions, Conferbot emerges as the definitive choice for organizations implementing Venue Selection Assistant chatbots. The platform's AI-first architecture provides fundamental technological advantages that translate into superior venue recommendation accuracy, more natural user interactions, and continuous improvement without manual intervention. The 94% time savings demonstrated in production environments significantly exceeds Totango's 60-70% efficiency gains, creating substantially greater ROI through both direct labor reduction and accelerated revenue cycles from faster venue bookings.

The 300% faster implementation enables organizations to achieve automation benefits in weeks rather than months, with lower total cost and reduced project risk. The comprehensive integration ecosystem ensures seamless connectivity with existing venue databases, calendar systems, and business applications without custom development requirements. The enterprise-grade security and scalability provide confidence for organization-wide deployment while maintaining consistent performance during peak usage periods.

Totango may represent a reasonable alternative only for organizations with exceptionally simple venue selection requirements and existing investments in the Totango ecosystem for other customer success functions. Even in these limited scenarios, the platform's architectural limitations, complex implementation requirements, and higher total cost of ownership make it difficult to recommend over Conferbot's purpose-built venue selection capabilities.

Organizations should begin their evaluation with Conferbot's free trial to experience the AI-powered venue selection capabilities firsthand, using actual venue criteria and selection scenarios from recent events. The trial environment includes sample venue databases and pre-configured selection workflows that demonstrate the platform's advanced capabilities without implementation investment. We recommend conducting a parallel proof-of-concept comparing both platforms using 3-5 recent venue selection scenarios to quantify the difference in recommendation quality, conversation efficiency, and administrative overhead.

For organizations with existing Totango implementations, Conferbot provides comprehensive migration assessment at no cost, including analysis of current workflows, integration requirements, and data transfer strategies. The migration process typically requires 4-6 weeks with minimal disruption to ongoing operations, with many organizations maintaining parallel systems during the transition period. The evaluation timeline should include stakeholder demonstrations with both platforms to gather feedback from actual users who will interact with the Venue Selection Assistant daily.

Decision-makers should establish clear evaluation criteria weighted according to their specific priorities, with recommended emphasis on implementation timeline (25%), ongoing usability (30%), recommendation accuracy (20%), and total cost of ownership (25%). Based on consistent performance across these dimensions, most organizations find that Conferbot delivers substantially greater value despite potentially higher initial licensing costs in some scenarios.

What are the main differences between Totango and Conferbot for Venue Selection Assistant?

The fundamental difference lies in platform architecture: Conferbot utilizes AI-first design with machine learning algorithms that continuously improve venue recommendations, while Totango relies on traditional rule-based chatbots requiring manual configuration. This architectural distinction creates dramatic differences in implementation speed (30 days vs 90+ days), ongoing improvement capabilities (automatic vs manual), and conversation quality (natural dialogue vs structured questioning). Conferbot understands complex multi-parameter requests and contextual nuances that Totango's rule-based system cannot process, resulting in significantly higher user satisfaction and efficiency gains.

How much faster is implementation with Conferbot compared to Totango?

Conferbot implementations average 30 days from project kickoff to production deployment, compared to Totango's 90+ day typical implementation timeline. This 300% faster implementation stems from Conferbot's AI-assisted configuration, pre-built venue selection templates, and white-glove implementation services versus Totango's self-service approach requiring extensive technical resources. Conferbot's implementation success rate exceeds 98% with projects consistently delivered on time and within budget, while Totango implementations experience frequent timeline extensions and budget overruns due to platform complexity and integration challenges.

Can I migrate my existing Venue Selection Assistant workflows from Totango to Conferbot?

Yes, Conferbot provides comprehensive migration tools and services specifically designed