LLM Hallucination Detector
Problem / task
What was built
What this demonstrates
- Python
- Sentence Transformers
- Logistic Regression
- Docker
- ML
I help businesses reduce manual work, handle customers and data faster, and turn ideas into practical digital systems.
I build automation, AI assistants, intelligent document search and complete online products — from an idea to a working system.
From business problem → architecture → working product.
A selection of AI, RAG and backend systems focused on practical engineering rather than demos.
Problem / task
What was built
What this demonstrates
Problem / task
What this demonstrates
Problem / task
What this demonstrates
Problem / task
What this demonstrates
From AI automation to complete software products — systems designed around real business processes.
AI AUTOMATION
The system can process incoming requests, move data between services, assist customers, work with documents and pass tasks to employees.
AI AGENTS
More than a chatbot. An AI assistant can understand a task, find information, work with your services and complete multi-step actions.
RAG / KNOWLEDGE SYSTEMS
Connect instructions, catalogs, contracts or a knowledge base, and AI can answer questions using your own materials instead of relying only on general model knowledge.
SAAS / WEB PRODUCTS
I build complete online systems: customer accounts, dashboards, internal employee tools and services for clients.
BACKEND & INTEGRATIONS
I build the internal logic that lets websites, CRM systems, databases, messengers and external platforms exchange data automatically.
MULTIMODAL AI
The system can work with images, video, voice and text together — for example to analyze operations, documents, visual information or conversations.
I start with how the work actually happens today, then choose the smallest reliable solution that removes the problem without adding unnecessary technology.
I map what happens now, where time is lost, what gets copied manually, and which decisions really matter.
I avoid complexity for its own sake and define the smallest reliable system that can solve the problem.
Automation, AI, software, data and integrations are used only where they create practical value.
Before launch I check errors, exceptions, security, mobile behavior, monitoring and a rollback path.
WORKING PRINCIPLES
Business process before AI hype.
Critical actions use trusted data, rules or human confirmation.
Security and access are considered before production.
Systems can start small and grow.
Testing, monitoring and rollback are part of delivery.
In plain language, describe a manual process, problem, website, service, CRM, integration, AI assistant or another digital business task. You will get a concise recommendation without unnecessary complexity.
Describe what people currently do manually, what gets repeated, where information is lost, or what is hard to control. I can help turn that into a concrete first scope.
No. If normal automation, rules, an integration or a simple system is safer and more efficient, that is what should be used. AI is added only when it genuinely helps.
Often yes, when the service provides a safe supported way to connect. If it does not, I will not pretend an integration exists; we choose another reliable workflow.
Yes. A focused first version is often the best way to prove the workflow, reduce risk and then expand only where it makes sense.
Important facts should come from a confirmed source, deterministic rule or authorized person. AI should not guess whether a payment happened or approve critical actions.
A plain-language description of how the process works today, who is involved, what is done manually and what result you want. Technical terminology is not required.
Yes. I am based in South Korea and work remotely with projects where the process, access and communication can be organized safely.
I'm Eduard Sarmanov. I design and build digital systems that help businesses automate work, use AI and connect different services into one practical workflow.
My specialization is AI Systems Architecture and Full-Stack Engineering. That means I can design the complete system — from what users see to the internal logic, AI, data and integrations.
I'm especially interested in problems where simply connecting ChatGPT or building a bot is not enough. I first understand how the business actually works, then design the solution and turn it into a product people can use every day.
Business problem → system architecture → implementation → measurable working result.
Tell me what is currently manual, repetitive or difficult to control. You do not need to know what technology is required.
WHAT HAPPENS AFTER YOU MESSAGE