Selected Projects
Travel Discovery Chatbot
The Challenge
- Travel agency needed to convert paid ad traffic into qualified leads without growing the sales team
- Customers arrive with vague wishes ("a beach week in Turkey in July, around €1000"), not specific searches, so matching them to real offers took a salesperson's time
- Customers wanted to browse in natural, conversational Bulgarian, not fill out rigid search forms
The Solution
- Built a conversational agent that extracts travel parameters (destination, dates, travellers, budget) from natural chat
- Queries live tour-operator APIs in real time instead of indexing stale inventory, presenting offers as interactive cards
- Captures leads and booking intent, handing qualified prospects directly to the sales team
- Wired server-side conversion tracking so ad spend is attributable to real leads
The Outcome
- Live in production at search.pochivki-turcia.bg
- Turns paid ad traffic into qualified, attributable leads
- Sales team engages warm prospects instead of fielding repetitive first-contact questions
Automated Take-Home Review Assistant
The Challenge
- Engineering teams running take-home assignments burn senior time on submissions that don't warrant a second look
- Review quality and consistency drift over time and between reviewers
- Running candidate-submitted code safely is a genuine security problem
The Solution
- Built HRANI, an AI agent that reviews candidate take-home submissions and writes structured verdicts back into the team's existing Notion workflow
- Treats candidate code as untrusted: read-only local tools, with any execution forced through an isolated, secretless sandbox
- Produces schema-validated output (scores, strengths, weaknesses, hire/maybe/reject) instead of free-form prose, so verdicts are comparable and hard to game
- Event-driven serverless pipeline triggered by a status change in Notion, with no always-on infrastructure to maintain
The Outcome
- Live in production, giving every submission a consistent first-pass review
- Reviewers' time goes to candidates that genuinely warrant a deeper look
- Structured, comparable verdicts keep the hiring bar steady across reviewers
Order Processing with Agentic AI
The Challenge
- E-commerce company with multiple sales channels (multiple web + physical stores)
- Order processing required significant manual operator intervention
- Operators spending time on routine decisions instead of complex cases
The Solution
- Built centralized, event-based order processing platform
- Implemented agentic AI system to handle routine order decisions
- Designed Event Sourcing architecture to capture decision context for AI training
- Integrated multiple data sources for unified order management
The Outcome
- Operators freed to focus on edge cases requiring human judgment
- Faster order fulfillment across all channels
- System captures data for continuous AI improvement
Insurance Broker AI Assistant
The Challenge
- Insurance broker needed to automate customer inquiry handling
- Complex product catalog requiring contextual understanding
- High volume of repetitive questions preventing focus on complex cases
The Solution
- Designed LLM-powered agent to handle initial customer inquiries
- Built system to understand insurance products and route appropriately
- Integrated with existing CRM for seamless handoff to human agents
- Created conversation templates for common inquiry patterns
The Outcome
- Reduced response time for initial customer inquiries
- Freed brokers to focus on complex cases requiring expertise
- System learns from broker responses to improve over time
HVAC Systems Company - Digital Foundation
The Challenge
- B2B/commercial HVAC company managing projects manually
- Customer inquiries, quotes, and maintenance schedules handled via email and phone
- No centralized system for tracking equipment installations or maintenance cycles
- Inventory management largely manual
The Solution
- Built and launched the company's first website
- Established a digital presence and lead-generation channel where there was none
- Structured the foundation so future automation can plug in cleanly (Event Sourcing principles for capturing operational data)
What the Foundation Enables
Maintenance Schedule Optimization
Automate client maintenance reminders based on system installation dates and usage patterns
Inventory Management
Track parts across job sites, predict needs based on upcoming projects, automated reorder triggers
Customer Portal
Self-service for maintenance requests, documentation access, system history
The Approach
- Start with digital strategy and foundation before automation
- Identify high-impact, low-risk automation opportunities
- Build systems that capture data for future AI use (Event Sourcing principles)
- Sequence work to match the business's real operational pain, not AI hype
Key Insight
- Most small B2B companies need digital infrastructure before AI automation
- The right foundation makes future automation straightforward
- ROI comes from solving real operational pain points, not chasing AI hype
Other Experience
Over the years, I've worked across various domains including FOREX trading automation with ML models, healthcare systems, and fintech backend infrastructure. Most projects involve complex integrations, API design, and distributed systems architecture.
If you're wondering whether I've solved a problem similar to yours, let's talk. Chances are I've encountered something close.