You will get a domain-specific AI assistant
Top Rated
Project details
You will get a production-ready AI assistant that answers questions based on your own documents and data – not on what the model guessed.
Unlike generic chatbots, a domain-specific RAG assistant retrieves answers from your actual knowledge base and indicates when information is not available. This makes it suitable for regulated industries like Legal, Healthcare, and Finance where accuracy and auditability matter.
What you get depending on your tier:
– Starter: basic RAG assistant on up to 20 documents, API endpoint, English only, ready in 1 week
– Standard: production assistant with eval pipeline, quality gates, source citations, and integration into your existing product
– Premium: full RAGops setup with monitoring, incremental updates, multilingual support, and AWS/GCP deployment
Not included in any tier: model fine-tuning, mobile app, external API or CRM integrations, ongoing content moderation, and queries
beyond your tier limit.
Starter also excludes: chat UI, source citations, custom prompts, eval pipeline, and frontend integration.
Standard also excludes: web pages as source, RAGops monitoring, incremental updates, and AWS/GCP deployment.
Unlike generic chatbots, a domain-specific RAG assistant retrieves answers from your actual knowledge base and indicates when information is not available. This makes it suitable for regulated industries like Legal, Healthcare, and Finance where accuracy and auditability matter.
What you get depending on your tier:
– Starter: basic RAG assistant on up to 20 documents, API endpoint, English only, ready in 1 week
– Standard: production assistant with eval pipeline, quality gates, source citations, and integration into your existing product
– Premium: full RAGops setup with monitoring, incremental updates, multilingual support, and AWS/GCP deployment
Not included in any tier: model fine-tuning, mobile app, external API or CRM integrations, ongoing content moderation, and queries
beyond your tier limit.
Starter also excludes: chat UI, source citations, custom prompts, eval pipeline, and frontend integration.
Standard also excludes: web pages as source, RAGops monitoring, incremental updates, and AWS/GCP deployment.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, StreamlitAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$2,000
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | - | - |
Model Documentation | - | ||
Model Monitoring | - | ||
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | |||
Source Code |
Frequently asked questions
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CB
Cory B.
Apr 17, 2026
AI Developer Needed - Construction Document Parser & Data Extraction Tool
Refat did a great job hitting deadlines and keeping the development schedule on track. The product I'm showing off to potential customers is way above expectations and I would highly recommend his team for future work.
JK
Jurre K.
Mar 18, 2026
Lead Backend Developer - AI Information Platform (RAG/Vector Database)
Refat is very good developer with deep expertise on AI and how to apply it. Together we did a great project that really moved the business forward. He took the liberty to explore and suggest alternative solutions to what I was asking, providing value to our business. This goal-oriented approach is much better than just delivering us what we are asking for. This way, his expertise guides us in creating useful projects for the business in the best way possible. The whole process of working with Refat is just great and I would recommend his services to everyone!
CS
Chris S.
Feb 17, 2026
LLM Powered Product Search Performance & Relevance Optimization
Refat delivered on exactly what we asked him to do, would recommend him to anyone looking for help in this space.
JT
Jane T.
Nov 26, 2025
Custom AI SAAS Tool
JT
Jane T.
Oct 17, 2025
Custom AI SAAS Tool
Refat exceeded my expectations on the development of our AI Saas MVP! He has keen insights, organization and ability to come up with creative, streamlined solutions. Looking forward to working together again! Thank you!
About Refat
AI Engineer & Full-Stack Developer | LLM, RAG, AI Agents & Workflows
100%
Job Success
Kyiv, Ukraine - 5:23 pm local time
Our AI engineering team builds production systems, not research demos. We design agent architectures, RAG pipelines, and AI workflows using TypeScript (Mastra, Vercel AI SDK) and Python (LangGraph, Agno, Pydantic AI, LangChain). We work with Claude, OpenAI ChatGPT models, Gemini, and open-weight models (including GLM, Kimi, DeepSeek and many more), choosing based on task fit and cost constraints. We practice eval-driven development. Automated eval pipelines, LLM-as-judge scoring, quality gates, all of that runs before anything ships to production. We build MCP integrations and custom Agent Skills, implement structured outputs with guardrails, set up agent observability with tracing, and design context architectures so models get the right information at every step. We have delivered domain-specific AI assistants with RAG and ongoing eval monitoring, agentic tools with multi-step reasoning and tool use, legal document processing at 95% accuracy, AI data aggregation with automated validation, voice agent systems, and generative UI products.
Our full-stack team supports AI products with solid web infrastructure: React, Next.js, Vue.js, TypeScript, Node.js, GraphQL, and REST APIs. We deploy on AWS, GCP, and Vercel, and work with PostgreSQL, Redis, and vector stores (Pinecone, Qdrant, pgvector). For mobile we use React Native and Flutter.
Delivered results: RAG-powered support bot reduced resolution time by 70% and lifted NPS by 20%. AI event aggregation pipeline scaled B2B SaaS onboarding from 1 to 5 clients per month at 95% data quality. LLM document system cut legal research from days to hours. AI landing page builder matched agency-level output with zero designer involvement. First-year ROI across AI projects averages 3.2x.
We have built AI systems for LegalTech (tax advisory, contract analysis), Healthcare (medication search with voice agents), Transportation (fleet management, real-time optimization), E-Commerce (content generation), SaaS platforms with AI-native features, and B2B products with intelligent data pipelines.
Our clients are startups embedding AI into their product, and companies replacing manual workflows with intelligent automation. We cover the full cycle: discovery, architecture, development, eval pipeline setup, deployment, and ongoing optimization.
Steps for completing your project
After purchasing the project, send requirements so Refat can start the project.
Delivery time starts when Refat receives requirements from you.
Refat works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Scoping
We review your documents, domain requirements, and integration needs. We confirm the knowledge base structure, retrieval logic, and response format before work begins.
Knowledge Base Setup
We ingest and chunk your documents, create embeddings, and configure the vector database. We implement metadata tagging for accurate filtering and retrieval.