AI-Powered Tech Advisor: Intelligent Technology Recommendation Engine

Project Overview

AI-Powered Tech Advisor is an intelligent technology recommendation engine built for the TTD Tech Radar platform that helps organizations make informed technology decisions through a comprehensive, context-aware assessment process. Using Azure Foundry, the system analyzes project requirements across multiple dimensions project context, team capabilities, technical preferences, business constraints, and delivery timelines to provide personalized technology stack recommendations from TTD's curated Tech Radar database.

The solution features an intuitive 5-step wizard interface that guides users through a structured assessment process, ultimately delivering 6-10 precisely matched technology recommendations with confidence scores, implementation reasoning, and strategic guidance all in under 60 seconds. The platform serves dual purposes: providing genuine value to prospective clients while capturing qualified leads with rich project context for TTD's sales pipeline.

The Challenge

TTD Consulting needed an intelligent way to help prospective clients understand which technologies from their Tech Radar best fit specific project needs, while simultaneously qualifying leads and capturing valuable market intelligence. The challenges included:

• Decision Complexity: 150+ technologies across 4 quadrants and 4 maturity rings create overwhelming choice paralysis for clients seeking the right technology stack.
• Context Sensitivity: Technology recommendations must consider project type, domain requirements, cloud platform constraints, team experience levels, budget levels, and delivery timelines each with significant implications.
• Ecosystem Compatibility: Recommended technologies must work together as a cohesive stack, avoiding conflicting frameworks or incompatible tool combinations.
• Lead Qualification: System needed to capture qualified leads while providing genuine value, balancing information collection with user experience.
• Personalization at Scale: Each recommendation set must be uniquely tailored to specific project context, avoiding generic "one-size-fits-all" responses.
• Confidence Transparency: Users needed to understand why each technology was recommended and how confident the system was in each suggestion.
• Conversion Path: Clear path from recommendations to consultation, with easy contact mechanisms and follow-up workflows.

Our Solution

We designed and implemented a sophisticated AI-Powered Assessment Engine that combines intelligent prompt engineering with Azure Foundry to deliver personalized technology recommendations while capturing qualified leads.

Multi-Step Wizard Interface

Progressive disclosure through a 5-step wizard breaks complex assessment into manageable chunks with mobile-first responsive design, Angular Material components, and real-time validation:

• Step 1 - Project Context: New/existing project, type (web, mobile, API, data, AI/ML), domain (insurance, fintech, healthcare, etc.), cloud platform preference
• Step 2 - Team Capability: Team size, experience level (junior-heavy, balanced, senior-heavy), DevOps support availability
• Step 3 - Technical Preferences: Preferred languages, frameworks, constraints (open-source, licensing, compliance) with multi-select capability
• Step 4 - Business Requirements: Delivery timeline (MVP, short, medium, long-term), budget level, key requirements, additional notes
• Step 5 - Contact Information: Name, email, company, phone, contact consent for lead capture and follow-up

Azure Foundry Integration with Advanced Prompt Engineering

Comprehensive system prompt that dynamically injects project context, maturity ring definitions, and intelligent guidance:

• Budget-Aware Guidance: Low budget favors open-source/cost-effective solutions; medium balances open-source and commercial; high considers premium tools; enterprise leverages comprehensive support
• Experience-Based Recommendations: Junior-heavy teams get technologies with excellent documentation and gentle learning curves; balanced teams get mainstream technologies; senior-heavy teams can handle advanced, cutting-edge tools
• Timeline-Driven Prioritization: MVP (1-3 months) focuses on mature ADOPT technologies; short-term (3-6 months) prioritizes proven solutions; medium-term (6-12 months) balances maturity with innovation; long-term (12+ months) can explore TRIAL and ASSESS technologies
• Structured Output Schema: Enforced JSON format with 6-10 recommendations, confidence scores (0.0-1.0), detailed reasoning, and 3-5 sentence summary

Tech Radar Data Service

• Azure Blob Storage: Centralized JSON storage with in-memory caching using ETag-based cache invalidation
• Structured Schema: 150+ technologies with metadata including quadrant, ring, description, tags, related technologies, and change history
• Dynamic Context: Complete Tech Radar catalog injected into Azure Foundry for intelligent matching and ecosystem compatibility validation

Security, Validation & Lead Generation

• reCAPTCHA Integration: Invisible bot protection with action-based verification and score threshold validation
• Multi-Layer Input Sanitization: Email normalization, name sanitization, XSS prevention, text length enforcement (2,000 char limit)
• Azure Table Storage Logging: Comprehensive assessment logging with date-based partitioning, full request/response context, token usage tracking
• Lead Analytics: Queryable data for popular technology interests, industry trends, budget/timeline distributions, conversion funnel analysis

Technical Excellence

Tech Advisor implementation followed modern software engineering practices and AI best practices:

• Clean Architecture: Service-based abstractions with dependency injection, SOLID principles, and interface-driven design for testability and maintainability.
• Async/Await Pattern: Fully asynchronous implementation for maximum scalability, enabling efficient handling of multiple concurrent assessment sessions.
• Advanced Prompt Engineering: Dynamic context generation with budget-aware, experience-based, and timeline-driven guidance for personalized recommendations.
• Caching Strategies: Tech Radar data cached with ETag validation, minimal API calls, improved performance through intelligent caching.
• Monitoring & Analytics: Complete Application Insights integration with custom metrics for token usage, costs, response times, and lead capture tracking.
• Security Best Practices: reCAPTCHA, multi-layer input validation, XSS prevention, CORS configuration, and secure secret management through Azure Key Vault.

Technology Stack

• Backend: .NET with Azure Functions (Consumption Plan), Azure Foundry, reCAPTCHA, Polly for resilience
• AI Services: Azure Foundry (Chat Completions API), advanced prompt engineering with dynamic context generation
• Frontend: Angular with TypeScript, Angular Material Design, Reactive Forms with validation, RxJS services, Azure Static Web Apps hosting
• Storage: Azure Blob Storage (Tech Radar JSON data), Azure Table Storage (assessment logs and analytics), in-memory caching (IMemoryCache)
• DevOps: GitHub Actions for CI/CD, Application Insights for monitoring, multi-environment configuration management
• Security: reCAPTCHA (Google), multi-layer input sanitization, CORS protection, Azure Key Vault for secrets

Results & Impact

AI-Powered Tech Advisor delivered exceptional business value and technical innovation for TTD Consulting: