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You will get LLM Workflow Automation for Reliable Data Inside Extraction
Rising Talent
Project details
I will build a reliable LLM workflow to extract structured data from emails, PDFs, web pages or CSV/DB records. You’ll get validated JSON/CSV outputs (or API/webhook delivery), with guardrails like schema checks, retries, and low-confidence routing to human review—so results are production-safe, not “LLM guesswork.”
AI Development Type
Knowledge RepresentationAI Tools
TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$400
|
Standard
$900
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 30 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | |
Source Code | |||
Taxonomy | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$200 - $1,000
Additional Revision
+$100
Additional Datasource
(+ 2 Days)
+$150
Human-in-the-loop review UI
(+ 3 Days)
+$250
Deployment to cloud
(+ 3 Days)
+$300
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Kristiyan M.
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Chatbot
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They delivered an outstanding solution that not only met our requirements but significantly exceeded our expectations. The chatbot is technically solid, well-structured, and perfectly aligned with our business goals.
What truly sets them apart is their professionalism and proactive approach. The process was smooth and largely hands-off on our side, thanks to their clear, consistent communication and ability to take full ownership of the project. Everything was delivered on time, and they went beyond the initial scope by providing valuable insights and actionable next steps.
It’s rare to find someone who combines technical excellence with reliability, autonomy, and strategic thinking. I would absolutely work with them again.
About Maria
AI Integration Engineer | Production RAG/LLM | Python Automation
100%
Job Success
Nesebar, Bulgaria - 3:49 am local time
I turn vague AI prototypes into production-ready systems. If you need a reliable Python backend, a secure RAG/LLM pipeline, or workflow automation, I build solutions that are maintainable, deployable, and easy to hand over. I combine a PhD in AI (final defense Feb 2026) with 10+ years of engineering experience at firms like JPMorgan, VMware, and Micro Focus.
How I add value
Discovery first: I clarify the real business goal, constraints, and success criteria before writing code—so expectations and timelines stay realistic.
Production mindset: Structured logging, robust error handling, and clear “how-to-run” documentation for smooth handover.
Scalable delivery: I move code beyond notebooks into secure, Dockerized services ready for real environments.
Core expertise
AI integration (LLM & RAG): Connect LLMs to private data safely (ingestion → vector DB → retrieval → API).
Example: Built a privacy-first system that converts unstructured email data into structured insights using local LLMs (Ollama/Llama 3) plus a hybrid BERT approach for traceable processing.
Python & workflow automation: Agents and scripts that remove manual bottlenecks in reporting and document processing.
Example: Delivered a “one-action” invoice workflow that automated the previously manual bookkeeping process end-to-end.
Production-ready backends: High-performance FastAPI/Django services with secure OAuth2/JWT authentication.
Example: Designed a centralized SSO server providing unified identity management across multiple enterprise products.
Why hire me
Senior engineering maturity: Enterprise experience means I build for security, scale, and reliability.
Deep research foundation: 6 published papers—pragmatic, evidence-based decisions for AI architecture.
Full ownership: Discovery → design → implementation → deployment → handover.
Ready to start? Send me a few sentences about your goal, privacy constraints, and timeline, and I’ll respond with a concrete plan and next steps.
Steps for completing your project
After purchasing the project, send requirements so Maria can start the project.
Delivery time starts when Maria receives requirements from you.
Maria works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & acceptance tests
Review samples + define extraction schema and validation rules. Create “golden test cases” so we agree what correct output/behaviour looks like.
Build extraction pipeline (LLM + rules)
Implement extraction prompts/parsers and deterministic fallbacks for critical fields. Return structured JSON.