EnDevSols

EnDevSols is an applied AI engineering organization working on production-ready RAG systems, AI agents, document intelligence, LLM evaluation, and open-source tooling for reliable AI applications.

We use Hugging Face to share practical AI experiments, Spaces, model workflows, datasets, and engineering tools connected to real-world AI systems — especially retrieval-augmented generation, document processing, agentic workflows, and LLM reliability.

Our focus is not only model experimentation. We care about how AI systems behave after deployment: retrieval quality, hallucination control, latency, observability, data quality, evaluation, and maintainability.

Website · GitHub · Open Source

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Focus Areas

• Retrieval-Augmented Generation (RAG) for private documents, internal knowledge bases, and enterprise search
• AI agents and agentic workflows using tools, APIs, memory, and structured business logic
• Document intelligence for PDFs, DOCX, PPTX, XLSX, CSV, OCR, tables, and layout-aware parsing
• LLM evaluation and observability for hallucination detection, tracing, retrieval testing, and regression checks
• Small Language Models (SLMs) and domain-specific model workflows for lower-latency, privacy-aware AI systems
• Custom AI chatbots and assistants for support automation, knowledge retrieval, and business workflows

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Open Source Tooling

We maintain tools focused on the production lifecycle of RAG, AI agents, document processing, and LLM reliability.

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What We Share on Hugging Face

This organization is used for practical AI resources, including:

• Model demos and inference experiments
• Spaces for AI workflows and prototypes
• RAG and document intelligence experiments
• Evaluation workflows for LLM applications
• Dataset and preprocessing experiments
• Small model and domain-specific AI testing
• Applied examples connected to production AI systems

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Technical Interests

RAG and Knowledge Retrieval

We work on retrieval pipelines where source quality, chunking, metadata, reranking, citations, and evaluation directly affect answer reliability.

Key areas:

• Document ingestion
• Layout-aware parsing
• OCR and table extraction
• Chunking strategy
• Vector search
• Hybrid retrieval
• Reranking
• Citation handling
• Retrieval evaluation
• Hallucination detection

AI Agents

We explore AI agents that can work with tools, APIs, structured workflows, and business rules.

Key areas:

• Tool calling
• Multi-step reasoning workflows
• Agent memory
• Workflow automation
• API-connected agents
• Human-in-the-loop review
• Observability and tracing

LLM Reliability

We are interested in systems that can be tested, monitored, and improved over time.

Key areas:

• Claim verification
• Source-grounded evaluation
• Prompt regression testing
• Retrieval quality checks
• Trace collection
• Evaluation datasets
• Failure analysis

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Engineering Stack

Common technologies across our AI work include:

• Hugging Face Transformers
• Python
• FastAPI
• LangChain
• LangGraph
• Vector databases
• MongoDB
• PostgreSQL
• Redis
• Docker
• AWS
• OpenAI, Anthropic Claude, Gemini, and open-weight models

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About EnDevSols

EnDevSols builds custom AI systems, RAG applications, AI agents, LLM integrations, document intelligence pipelines, and AI-powered SaaS platforms for real-world use cases.

For commercial projects, consulting, or custom AI development:

• Website: https://endevsols.com
• AI Development Services: https://endevsols.com/services
• Generative AI Development: https://endevsols.com/services/generative-ai
• GitHub: https://github.com/ENDEVSOLS
• Email: technology@endevsols.com