Need to build Agent AI for Quotation Creation

@mayankinnovative

Hi, As per my understanding: You need an AI-powered quotation automation agent that can process enquiries from emails, PDFs, images, and website forms, extract structured requirements, match them with ERP products intelligently, generate accurate quotations, validate results, and route them through sales approval before sending them to customers. The AI must use semantic understanding for product mapping while strictly avoiding hallucinated or invented product recommendations. Implementation approach: I can build this solution using two approaches. First, a fully custom AI agent using Python, LangGraph, LLM orchestration, OCR, vector search, and ERP API integrations for maximum control, scalability, workflow automation, and enterprise customization. Second, a faster hybrid approach using existing AI models like Claude/OpenAI combined with semantic retrieval and validation layers to reduce development time and infrastructure complexity. In both cases, the ERP APIs will securely connect product catalogs, stock, pricing, and quotation workflows while human approval checkpoints ensure accuracy before customer delivery. A few quick questions: 1. Approximately how many products and product attributes exist inside your ERP catalog? 2. Do quotations follow predefined templates and pricing rules, or are there dynamic calculations involved? 3. Do you want the AI agent hosted on your own infrastructure or cloud-managed deployment?

@KarthikTechCon

Hi, I’m Karthik with 15+ years of experience in AI automation, LLM applications, ERP integrations, and workflow systems. I can help build an AI Agent for automated quotation generation integrated with your existing PHP ERP. Key features: ✔ Email/PDF/Image/Web enquiry processing ✔ OCR & AI-based data extraction ✔ Semantic product matching with ERP data ✔ Automated quotation generation ✔ Validation & approval workflows ✔ Sales approval & customer email automation ✔ Secure ERP API integration Recommended stack: • Python + LangGraph/LangChain • OpenAI/Claude or custom LLM workflows • Vector database for semantic search • OCR pipelines for documents/images Two approaches available: 1. Existing AI APIs (Claude/OpenAI) → Faster & cost-effective MVP 2. Fully custom AI agent → Greater control & enterprise scalability My focus will be on: • Accurate product mapping without hallucination • Human approval checkpoints • Scalable and maintainable architecture • Fast and secure automation workflows Relevant experience: • Built AI agents, ERP-integrated automation systems, RAG workflows, and intelligent document processing platforms using Python, APIs, and LLMs. I can also provide phased cost & timeline estimation for both MVP and full-scale implementation. Best Regards, Karthik

@adinanipavan

Hi, This is a very strong use case for an AI Agent workflow integrated with your existing ERP. I can help build an intelligent quotation automation system that: => Reads enquiries from Email, PDFs, Images, and Website forms => Extracts structured requirement data using OCR + LLM pipelines => Connects with your PHP ERP through APIs => Matches products semantically based on specifications and availability => Generates quotation drafts without inventing products or specs => Validates quotations against ERP/product rules => Sends quotations to the sales team for approval => Automatically sends approved quotations to customers The important part here is grounding the AI strictly on ERP/product data so hallucinations are avoided. This can be handled using semantic search/vector matching + rule-based validation. Suggested stack: => Python + FastAPI => LangGraph/LangChain for workflow orchestration => OpenAI/Claude models for extraction and reasoning => OCR for PDFs/images => Vector DB for semantic product mapping => PHP ERP API integration Estimated timeline: => MVP using existing AI models (OpenAI/Claude): 2–4 weeks => Fully custom multi-agent architecture with advanced workflows: 1–2 months depending on ERP complexity and product catalog size I would recommend starting with an MVP using existing LLMs first, then scaling into a more advanced custom agent architecture after validation.

@Srashtasoft

Hi there, I have read your project requirement you need an AI Agent that processes enquiries from multiple sources extracts data maps it with ERP products generates quotations validates them and routes for approval before sending to customers We can build a production ready Agentic system using Python LangGraph RAG and LLMs integrated with your PHP ERP via secure APIs ensuring accurate product mapping without hallucination using vector search and rule based validation We can deliver two approaches ======================= Custom AI Agent Fully controlled pipeline using LangGraph vector DB and LLM with higher accuracy customization and long term scalability Claude or existing LLM based Agent Faster to deploy lower initial cost but slightly less control compared to fully custom system Quick questions ============= What type of products and complexity of catalog in ERP? Do you already have structured product data or need preprocessing? Expected volume of enquiries per day? Do you need multilingual support for inputs? Best Regards Srashtasoft Team

@adriank06

Hello I've carefully reviewed your requirements for building an AI Agent to automate your quotation workflow, and I'm confident this is exactly the type of system I specialize in building. What makes this project interesting: You've clearly identified the pain pointsvmanual quotation processing is slow, error prone, and ties up your sales team. More importantly, you understand the critical challenge: the Agent must match products semantically without inventing non-existent items. That's the difference between a useful system and a liability. My Approach I'll build a multi-agent system that handles the entire workflow: 1. Input Processing Layer - Email parser (IMAP/API integration) - PDF extraction (structured and unstructured) - OCR for images (Tesseract/PaddleOCR) - Web form integration 2. Extraction Agent - Uses LLM with structured output (JSON schema enforcement) - Extracts: customer info, product requirements, quantities, specifications - Validates completeness before proceeding 3. Product Matching Agent (RAG-based) - Vector database of your ERP products (embeddings) - Semantic search to match customer requirements to actual products - Critical safeguard: Only retrieves existing products from ERP via API - No hallucination if no match found, flags for human review 4. Quotation Generation Agent - Calls ERP API for real-time pricing and availability - Applies business rules (discounts, taxes, terms) - Generates professional PDF/Excel quotation 5. Validation Agent - Cross-checks all products exist in ERP - Verifies pricing accuracy - Ensures customer data completeness - Flags anomalies for review 6. Approval Workflow - Sends to sales team dashboard/email - Tracks approval status - Auto-sends to customer once approved - Logs entire process for audit Two Implementation Options Option A: Custom Build (LangGraph + Python + Self-hosted LLM) Stack: LangGraph for orchestration, Python backend, Llama 3.1 70B or Qwen 2.5 (self-hosted or via API), Qdrant vector DB, FastAPI for ERP integration Pros: - Full control and customization - Can be deployed on-premise - Lower long-term API costs - Deep integration with your business logic Cons: - Longer development time - Higher upfront cost Timeline: 8-10 weeks Option B: API-based (Claude/GPT-4 + Lightweight orchestration) Stack: Claude API or GPT-4o for LLM tasks, Python backend, Qdrant/Chroma for RAG, FastAPI for ERP integration, simpler orchestration Pros: - Faster development - Superior language understanding out-of-the-box - Easier maintenance - Proven reliability Cons: - Dependency on external API - Variable monthly costs based on volume Timeline: 5-6 weeks Why I'm the Right Person: I specialize in Agentic AI and workflow automation. I've completed advanced courses in LLM & Agentic AI and AI Agents, and I've built production systems that combine LLMs with real business logic not just chatbots, but systems that make decisions, call APIs, and execute tasks autonomously. Key differentiators: - I understand the hallucination problem deeply and know how to prevent it through RAG constraints, structured outputs, and validation layers - I'm expert in n8n, Python, LangGraph, and API integration I can connect your PHP ERP seamlessly - I build production-grade systems, not prototypes with error handling, logging, monitoring, and security - I've worked with ERP integrations before and understand the importance of data accuracy in quotations What You'll Get: Fully functional AI Agent system . ERP API integration (I'll work with your PHP team or build the APIs myself if needed). Admin dashboard to monitor agent activity. Complete documentation an.

@Web1devloper

Drawing on my extensive experience in Data Analytics and Science, I'm confident that I possess the necessary skills to build your Agent AI for Quotation Creation. Having worked with a diverse range of businesses, I've developed a deep understanding of how data can be effectively applied to enhance operational efficiency and customer service. My proficiency with Python and REST APIs aligns perfectly with your requirement of creating APIs to communicate with the ERP, built on PHP. Additionally, my expertise in Machine Learning equips me to make intelligent use of existing AI systems or even build an Agent AI from scratch using LLM, LangGraph, Python, or any other suitable technology. My work with TensorFlow and PyTorch further bolsters my capacity to ensure the development of a sophisticated and precise solution that maps customer requirements accurately without inventing new products.

@dushyant317

We can build an AI quotation agent that extracts RFQs from emails, PDFs, images, and web enquiries, maps requirements against your PHP ERP through secure APIs, validates product matching using semantic AI/RAG workflows, generates quotations, and routes them to sales approval before customer delivery.

@manoval09

Hi, You're looking to automate quotation creation from mixed input formats—PDF and email—using an AI agent. That's a solid workflow problem: pulling structured data from unstructured sources and turning it into consistent quotations. I'd build this with Python + Claude API for the agent logic, using PyPDF2 for PDF parsing and email integration via IMAP or webhook handlers. The agent would extract line items, pricing, and client details, then format them into your quotation template. The architecture separates parsing (async workers) from agent reasoning, so you can scale without re-processing documents. Here's what matters right now: your description cuts off mid-requirement. Before I scope this properly, I need to know—are quotations generated from templates with fixed fields, or do you need custom logic per client type? That answer determines whether this is 40 hours or 80. My first 24 hours: document the exact quotation fields you need, map sample emails/PDFs to those fields, and sketch the agent's decision logic. Best regards, Val

@timotius1998

Hi, I have 7+ years of experience in full-stack and AI-integrated systems, and this is very achievable with the right architecture. Your flow is clear: Email/PDF/Image/Web enquiry → AI extraction → ERP product matching → quotation generation → validation → sales approval → customer delivery. I would recommend a hybrid AI architecture using Python + LangGraph + LLMs (GPT/Claude) connected to your PHP ERP through secure APIs. The important part is the semantic product matching layer so the AI does not hallucinate or invent products. This can be handled using embeddings/vector search + strict ERP validation before quotation generation. Two approaches: Custom AI Agent (Recommended) LangGraph + Python + LLM Full workflow control Better long-term scalability Higher upfront cost but more reliable Claude/GPT Assisted Workflow Faster to build Lower cost initially Less control/flexibility compared to fully custom agent systems Estimated timeline for MVP: ~4–8 weeks depending on ERP complexity and quotation rules. I can help design both the AI workflow and ERP API communication layer cleanly and securely. Ready to discuss architecture in detail.

@Br1je5h

The two-path question you asked is exactly the right one to answer before writing a single line of code. Path A — Custom LLM (LangGraph + Python + open-source models): Full control, lower inference cost at scale, and no vendor lock-in. Build time: 6–8 weeks for a production-ready agent. Higher upfront cost (~₹3–4L), lower monthly opex. Risk: you own model behavior. Path B — Hosted LLM (Claude / GPT-4o via API): Faster to ship (3–4 weeks), stronger out-of-the-box reasoning, and easier "agent should not invent" constraints via RAG grounding. Higher monthly API cost at volume. Best for validating the workflow before committing to custom infra. My recommendation: start with Path B to validate the quotation logic in 3 weeks, then optionally migrate critical inference to a fine-tuned open model in Phase 2. What I've built that maps here: At OpenAI, I worked on RLHF evaluation—meaning I understand exactly where LLMs hallucinate and how to constrain them. For British Petroleum's BioVerse platform, built a multi-input ingestion pipeline (PDFs, APIs, structured data) on AWS + Python, orchestrating 10+ engineers. Same architecture class as your Email/PDF/Image → ERP → Quotation flow. Phase plan: Week 1–2: ERP API design + document ingestion layer (Email, PDF, image OCR) Week 3–4: Semantic product-matching via RAG + vector DB (no hallucination) Week 5: Approval workflow + sales team notification Week 6: Testing, edge cases, handoff — Brijesh, Sonraj Labs

@toqeer74

Extracting quotation requests from disparate sources like emails, PDFs, and images poses a challenge in effectively integrating those inputs with your existing ERP system through APIs. Leveraging a Large Language Model like LLaMA 2, combined with a semantic approach, ensures accurate product matching against customer requirements without generating erroneous outputs. Developing an agent from scratch using Python and efficient retrieval-augmented generation techniques can streamline your workflow significantly. The initial deliverable can be accomplished within 30 days. Do you have a specific timeline for the first milestone?

@Knihal03

Hi, I can build this quotation automation agent with a reliable, non-hallucinating workflow using Python + LLMs + RAG + ERP API integration. Proposed flow: - Extract enquiry data from Email/PDF/Image/Web forms - Parse and structure requirements using LLM + validation layers - Match products from ERP using semantic search + rule-based checks - Generate quotation draft automatically - Validate quotation against ERP/product constraints - Send to sales team for approval workflow - After approval, send final quotation to customer Tech stack I would recommend: - Python + FastAPI - LangGraph / Agent workflows - Vector DB for semantic product matching - OCR for PDFs/images - ERP API integration (PHP backend compatible) - Guardrails to prevent AI from inventing products/specs I can build: 1. Custom AI Agent from scratch (more flexible & scalable) 2. Hybrid system using Claude/OpenAI APIs (faster deployment) Estimated timeline: - MVP: 2–4 weeks - Production-ready system: depends on ERP complexity and approval workflow Before final estimation, I would like to understand: - ERP structure/API availability - Product catalog size - Quotation format/sample - Approval workflow requirements Looking forward to discussing this further.

@edlim4db1

With extensive experience in building AI systems from scratch and implementing existing AI technologies into real business workflows, I am confident that I can deliver a cost-effective and efficient agent AI for your quotation creation process. My background in using LLM, LangGraph and Python to develop cognitive agents perfectly aligns with your project requirements. An example of my skillset includes the creation of AOS, which is an AI orchestration platform that seamlessly integrates multiple agents for real-time back-office operations. This involved developing decision-making pipelines, ensuring data is ingested accurately, and coordinating complex workflows effectively. Similarly, for your project, I will establish communication between the ERP system built in PHP and the Agent AI utilizing well-documented APIs. Moreover, I perceive, validate, and refine the outputs provided by AI systems to ensure accuracy and reliability while deploying them (like GPT or Claude) into production. This approach fits perfectly with your need for an Agent that does not invent but understands your products and maps them acutely to customer requirements. My commitment to delivering clean working builds assures you’ll receive a well-structured solution. Let's discuss how I can create an exceptional AI-driven Quotation Agent that streamlines your workflow and maximizes efficiency.

@mazoirz

Hi, I have carefully reviewed your requirement for building an AI Agent for automated quotation creation, and I can help you design and develop a scalable and production-ready solution. I am a Python and AI developer with experience in LLM-based systems, RAG pipelines, API integrations, and building intelligent automation workflows. Proposed solution approach: I would recommend building this system in phases: Phase 1 – MVP AI Agent Extract data from Email / PDF / Images using OCR + NLP Build structured product matching system using embeddings (semantic search) Integrate with your existing PHP ERP via REST APIs Generate draft quotation based on matched products Phase 2 – AI Agent Layer Add LLM-based reasoning layer (OpenAI / Mistral / LLaMA) Implement validation step to avoid hallucinations Improve product matching using RAG + vector database Phase 3 – Workflow Automation Approval workflow (sales team review) Automated quotation sending system Logging and monitoring system Tech stack I would use: Python (FastAPI backend) LLMs (OpenAI / Mistral / LLaMA) RAG architecture with vector database (FAISS / Pinecone) LangChain or LangGraph for agent workflow REST APIs integration with your PHP ERP Note: I can start with a fast MVP version first, so you can validate the AI workflow early before scaling into a full production system. Looking forward to working on this advanced AI system.

@AsifTech21

With my experience in building AI assistants for email handling, CRM automation, and document search, I strongly believe I can create an outstanding Agent AI system for your quotation needs. My specialization in AI Agents and Python aligns perfectly with your project requirement. Leveraging powerful technologies such as LLM, LangGraph, and available AI like CLAUD, I will build a system that extracts, analyzes, matches, prepares and verifies quotations efficiently. One of my key strengths lies in understanding the nuances of businesses and constructing comprehensive AI systems that not only perform but also address real-world challenges. My recent projects include an AI email responder reducing manual workload by 80%, a robust system for instant document search, and a lead qualification AI agent using GPT for one of my clients. Using my technical expertise in LangChain, OpenAI, Python FastAPI, Vector DBs and API integration to connect with your existing ERP system built in PHP ensuring seamless communication between them. By employing a semantic approach, the quotation compilation process by the Agent will be driven by understanding customer requirements first rather than inventing. Let’s automate your process today!

@bpatidar2793

How structured is your ERP product catalog and quotation history currently, because the quality of semantic product matching and hallucination prevention will depend heavily on product metadata, specifications, and historical quotation patterns? I understand you need an AI quotation agent that extracts enquiry data from emails, PDFs, images, and forms, maps requirements against ERP products using semantic understanding, generates accurate quotations, validates them, and routes them through approval workflows before customer delivery. With strong experience in AI agents, RAG pipelines, LangGraph workflows, ERP integrations, vector databases, and LLM orchestration, I can build a reliable non-hallucinating system using either custom open-source stacks or Claude/OpenAI-assisted architectures optimized for accuracy, traceability, approval control, and scalable automation.

@somendersingh96

Hi, I can build an AI-powered quotation agent that: • Reads enquiries from Email, PDFs, Images, and Website Forms • Extracts customer requirements using OCR + LLMs • Connects with your PHP ERP via APIs • Matches products semantically (without hallucinations) • Generates accurate quotations automatically • Sends quotes to sales for approval before emailing customers Tech Stack Python + LangGraph/LangChain OpenAI/Claude APIs OCR & document parsing REST APIs for ERP integration Vector DB for semantic product matching Two Options Custom AI Agent – scalable & fully customizable (6–10 weeks) Claude/OpenAI-based MVP – faster deployment (3–5 weeks) Deliverables • AI quotation workflow • ERP API integration • Approval system • Automated quote generation • Documentation & deployment support I can also help design a no-hallucination RAG workflow with validation and approval checks to ensure quotation accuracy. Best Regards Somender Singh

@iwebphpdeveloper

With 15+ years of experience in AI-integrated software development and ERP-based automation systems, I can help build an intelligent Agent AI that automates quotation processing from emails, PDFs, images, and website enquiries while integrating seamlessly with your existing PHP ERP system through secure APIs. The solution can intelligently extract enquiry data, map products using semantic AI-based matching, generate accurate quotations, validate responses, and route them for sales approval before customer delivery. I have experience working with LLM-based workflows, Python automation, LangGraph-style agent systems, and AI integrations using existing models such as Claude/OpenAI as well as custom-built architectures. The system will be designed to minimize hallucinations, ensure product accuracy, and maintain a controlled approval workflow. I can provide a detailed timeline, architecture approach, and cost estimation for both a fully custom AI agent and an implementation leveraging existing AI platforms.

@princekaushal354

Hello, Your project matches closely with the AI automation systems I work on. I understand the main goal is to build a reliable AI agent that can accurately process customer enquiries, map them with ERP products semantically and avoid hallucinated outputs. I can help build a workflow that: • Captures enquiries from emails, PDFs, images and website forms • Extracts structured customer requirements using OCR and AI pipelines • Connects with your existing PHP ERP through APIs • Matches products intelligently based on availability and requirements • Generates quotation drafts with validation checks • Sends quotations to the sales team for approval before customer delivery I work with Python, LLMs, LangGraph, API integrations and AI workflow automation tools including n8n. The solution can either be fully custom-built using LangGraph and LLM architecture or accelerated using existing AI models like Claude/OpenAI with controlled validation layers. My focus is on building scalable and production-ready systems with reliable ERP synchronization and structured approval workflows. I’d be happy to discuss your ERP structure, quotation logic and preferred AI architecture to provide the best implementation approach.