EnDevSols
Software company
Software CompanyOpen 24 HoursOpen Late
Hours
Open 24 Hours, 7 Days a Week
About EnDevSols
"EnDevSols is a custom AI development and software company building production-ready AI agents, RAG systems, custom AI chatbots, LLM integrations, automation platforms, and scalable SaaS products. We help startups, mid-market teams, and enterprises turn AI ideas into secure software connected to real workflows, private data, APIs, CRMs, and cloud infrastructure. Our work covers Generative AI, enterprise software development, machine learning, computer vision, NLP, document intelligence, customer support automation, lead qualification, and AI-powered business automation."
Services
Custom Software Development Services
Custom operational platforms and business software for companies that have outgrown spreadsheets, disconnected tools, or inflexible off-the-shelf systems. We build the system your workflow actually needs, integrate it with what you already run, and hand over full ownership of the code.
EnDevSols is a custom AI development and software company building production-ready AI agents, RAG systems, custom AI chatbots, LLM integrations, automation platforms, and scalable SaaS products. We help startups, mid-market teams, and enterprises turn AI ideas into secure software connected to real workflows, private data, APIs, CRMs, and cloud infrastructure. Our work covers Generative AI, enterprise software development, machine learning, computer vision, NLP, document intelligence, customer support automation, lead qualification, and AI-powered business automation.
FAQ
Yes, over 90% of our clients are in the US, UK, UAE, Australia, Singapore, and Germany. Our team operates with timezone overlap across US, UK, and Middle East business hours. We use agile sprints with bi-weekly video demos, shared project dashboards, and async communication on Slack or Teams. Location is never a barrier to quality delivery.
For AI/RAG: LangChain, LangGraph, Qdrant, OpenAI, Claude, Gemini, Hugging Face. For backend: FastAPI (Python). For frontend: Next.js, React, Tailwind CSS. For database: Supabase/PostgreSQL. For deployment: AWS, Vercel, Docker, GCP. For mobile: Flutter. We choose tools based on your requirements, not a fixed template
Yes, this is one of our most common engagements. We add AI-powered search, document assistants, recommendation engines, and workflow automation to existing SaaS platforms without a full rebuild. We work within your existing stack and deployment pipeline. Most AI feature additions take 4–8 weeks. We also ensure the features are monitored and tested in production, not just demoed.
A focused RAG system, document ingestion pipeline, vector database, retrieval API, and chat interface, typically takes 4–8 weeks to production. Timeline depends on document volume, required accuracy, integration complexity (CRM, SSO, existing tools), and whether you need a custom UI or an embeddable widget. We deliver working builds every 2 weeks via agile sprints.
We implement multiple layers: strict retrieval grounding (the LLM can only use retrieved content, not its training data), citation enforcement (every answer must cite its source), confidence scoring (low-confidence answers trigger fallback responses), and production monitoring with LongTracer, our open-source hallucination detection tool with 49k+ downloads. We also run regression testing with LongProbe before every deployment.
RAG (Retrieval-Augmented Generation) connects a large language model like GPT or Claude to a vector database containing your own documents. When a user asks a question, the system first searches your documents for the most relevant content, then generates an accurate, cited answer from that content, not from the AI's general training data. This eliminates hallucinations because the AI only answers from your verified information. We build RAG systems using LangChain, Qdrant, and LangGraph.
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