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You will get a RAG and Document AI reliability audit with an actionable roadmap
Project details
If your RAG or Document AI system retrieves the wrong evidence, loses citations, mishandles tables, or answers when the source is silent, I will trace where reliability breaks and show what to fix first.
I audit an existing pipeline across ingestion/OCR, chunking, retrieval, reranking, prompting, grounding, citation provenance, and abstention—only where relevant to your system. Findings are tied to supplied failure cases and an agreed evaluation set, not generic best-practice advice.
Depending on your package, you will receive:
• an evidence-linked diagnostic report
• prioritized root causes and a remediation roadmap
• baseline results for 20 or 40 evaluation cases
• reusable Python evaluation scripts and setup guidance (Advanced)
This service is best for teams with a working or reproducible pipeline, representative documents, and at least five known failure cases. Please use sanitized data and never share secrets or personal data.
The scope is audit and evaluation. Full remediation implementation, deployment, model training, and ongoing monitoring are not included.
I audit an existing pipeline across ingestion/OCR, chunking, retrieval, reranking, prompting, grounding, citation provenance, and abstention—only where relevant to your system. Findings are tied to supplied failure cases and an agreed evaluation set, not generic best-practice advice.
Depending on your package, you will receive:
• an evidence-linked diagnostic report
• prioritized root causes and a remediation roadmap
• baseline results for 20 or 40 evaluation cases
• reusable Python evaluation scripts and setup guidance (Advanced)
This service is best for teams with a working or reproducible pipeline, representative documents, and at least five known failure cases. Please use sanitized data and never share secrets or personal data.
The scope is audit and evaluation. Full remediation implementation, deployment, model training, and ongoing monitoring are not included.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Hugging Face, PyTorchAI Models
GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$149
|
Standard
$349
|
Advanced
$699
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 8 days |
Number of Revisions | 1 | 1 | 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 | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$40
10 Additional Evaluation Cases
(+ 1 Day)
+$75Frequently asked questions
About Sevket
AI Engineer | Production RAG, AI Agents & LLM Integration
Istanbul, Turkey - 12:26 am local time
Selected systems I have built:
• Live B2B insurance SaaS: converts multiple carrier PDFs into a validated comparison in approximately 30 seconds instead of a 2–4 hour manual review. Structured schemas and explicit “not mentioned” states prevent plausible guesses when source data is absent.
• Private on-prem GraphRAG for an aerospace R&D partner: Qwen2.5-7B, 4-bit quantization, vLLM serving, hybrid retrieval, and evaluation with RAGAS and DeepEval.
• Government-record due diligence: an OCR, Vision-LLM, and Qdrant pipeline that processes scanned and digital documents and produces source-cited risk reports.
• Production engineering for a SaaS platform serving 35M+ users, including backend integration, PostgreSQL, testing, debugging, and production code review.
I can help you with:
• RAG knowledge assistants over PDFs, websites, internal databases, and cloud documents
• AI agents, tool calling, and third-party API integrations
• OCR and document ingestion, metadata design, hybrid search, reranking, and citations
• Local and on-prem LLM deployment, quantization, serving, and cost/latency optimization
• Production FastAPI services, PostgreSQL, Docker, GCP, evaluation, and observability
Core stack:
Python, FastAPI, PostgreSQL, Qdrant, ChromaDB, LangChain, LangGraph, CrewAI, OpenAI, Gemini, Hugging Face, vLLM, Docker, and Google Cloud Platform.
If your AI prototype is inaccurate, uncited, expensive, or difficult to deploy, send me the current architecture and one failing example. I’ll help identify what should stay deterministic, what belongs in retrieval, and where the LLM adds real value.
Steps for completing your project
After purchasing the project, send requirements so Sevket can start the project.
Delivery time starts when Sevket receives requirements from you.
Sevket works on your project following the steps below.
Revisions may occur after the delivery date.
Confirm scope and reproduce failures
I review the supplied materials, define the audit boundary, and reproduce the known failure cases using the provided test environment or a local reproduction.
Audit and measure the pipeline
I inspect ingestion, OCR, chunking, retrieval, reranking, prompts, grounding, citations, and abstention where relevant, then run the evaluation scope included in your package.