AI WhatsApp Message Parser

@Prutheus

Parsing WhatsApp messages reliably is less about the LLM and more about the mess: forwarded text, voice-note transcripts, half-sentences and the same information written five different ways. - Ingestion via the WhatsApp Business Cloud API (official) or from exported chats, depending on your setup - Extraction into a strict schema (LLM with structured output plus rules where the format is predictable), so downstream systems get clean fields, not prose - Confidence handling: anything the parser is unsure about goes to a review queue instead of into your database as a guess - Everything stored relationally so you can query and correct it later Proof: I run document and message extraction pipelines in production for industrial clients — LLM steps with structured output, unattended 24/7 runs. What information should be pulled out (orders, addresses, appointments?), and does the data come live from a WhatsApp Business number or from exports? Happy to start with a sample batch of messages. Martin

@TeraByte26

Hi, I will deliver a Python script that reads raw_messages, sends each text to GPT‑4, extracts product details, enquiries and sales data, and writes them into clean product, enquiry and sales tables with DDL, README and a test run on 1,000 messages. I can finish in 7 days for 70,000 INR and share a sample extractor today. Best, Rahul Waiting for your response in chat! Best Regards.

@TechizeBuilder

Dear Client, I read "AI WhatsApp Message Parser" carefully and understand you need a WhatsApp chatbot that automates customer interactions and streamlines your business processes. My hands-on experience with Python, Node.js aligns directly with what you need. I've built WhatsApp chatbot solutions with automated conversations, product catalogs, order placement, lead generation, appointment booking, payment integration, AI-powered responses, CRM/API integrations, multilingual support, notifications, and admin dashboards — all designed to improve customer engagement and reduce manual effort. A few quick questions to get us started: 1. Will the chatbot use the WhatsApp Business Cloud API or another WhatsApp API provider? 2. What are the primary use cases (sales, customer support, bookings, order tracking, etc.)? 3. Do you need integrations with your existing CRM, ERP, website, or payment gateway? Thanks & Regards, Deepak

@AZTURK1

Hi there, Your setup with 500+ groups feeding raw_messages is solid - the gap is the extraction layer. I'll build a Python async pipeline that batches messages, calls GPT-4 for structured extraction, and writes normalized rows to your products, enquiries, and sales tables with full traceability back to each raw message id. What I'll do: ✅ Async Python worker with batching and retry logic to handle thousands of messages per hour without backlog growth ✅ DDL for products, enquiries, sales tables plus raw_message_id mapping for full traceability ✅ README with env vars, setup steps, and cron/CLI examples ✅ Test run on your 1,000-message sample proving 90%+ field accuracy within 5 minutes ✅ Full backup of existing schema before any migration, tested on a staging copy first Skills: ✅ Python async (asyncio/aiohttp) and OpenAI API integration ✅ SQLAlchemy ORM and relational schema design ✅ Batch processing pipelines for high-throughput data ✅ Database migrations and DDL authoring ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ 300+ projects delivered, 280+ five-star reviews Why me: I'm available 24 hours and respond fast, with unlimited revisions until you're fully satisfied. Quick question: are you currently using PostgreSQL or MySQL for the raw_messages table, and do you have a preference for the target schema's engine? I can deliver this in 2 days for 43,487 INR and can start right now.

@Shrutibimpl

Hello, I can build your high throughput WhatsApp message parsing and extraction pipeline using Python and SQLAlchemy. I can write an asynchronous batch processing worker using Python asyncio and SQLAlchemy to handle message spikes. I can design relational PostgreSQL database tables for products inquiries and sales data linked back to your raw_messages primary keys using Alembic migration scripts. I can structure system prompts using OpenAI GPT-4o with Pydantic JSON schema constraints to extract clean product names inquiry details and numeric sales figures. I can also add error logging wrappers so any failed LLM parsing jobs stay flagged for manual review while keeping processing latency low. 1) Which relational database engine like PostgreSQL or MySQL holds your existing raw_messages table? 2) Do you prefer using OpenAI GPT-4o structured outputs or another LLM API for extracting the JSON entities? 3) Do you want the worker to run as a continuous polling daemon or be triggered by database event hooks? Thanks, Bharat

@shlomohassid

Parsing unstructured text into a strict relational schema is exactly what we do. We recently built a proprietary parsing engine (poli-parser) to extract highly nested, complex data from unstructured insurance documents. Taming a WhatsApp firehose is right in our wheelhouse. To process 1,000 messages in under 5 minutes, sequential API calls won't cut it. We will architect this using Python, SQLAlchemy, and strict asynchronous batching. Here is exactly how we will hit your acceptance criteria: High-Throughput Async Pipeline: We will use Python's asyncio to process messages in parallel batches. This maximizes throughput while respecting API rate limits, easily clearing your 5-minute benchmark on a mid-tier VPS. Guaranteed Data Structure: We won't just ask the AI nicely for JSON. We will implement OpenAI's native Structured Outputs. This forces the model at the API level to return data that perfectly matches your database schema - meaning zero parsing failures. Traceability: We will design the DDL for your new tables (Products, Enquiries, Sales) so that every single extracted row maintains a strict Foreign Key relationship back to your raw_messages table for instant auditing and manual correction. The Handoff: You’ll get clean, PEP-8 compliant code, the SQLAlchemy models/migrations, and a clear CLI command to run the async worker via cron. Let’s review a sample of your raw data and get this pipeline flowing.

@TechWagger2026

I'm Arun, an experienced end-to-end developer with a solid track record of delivering successful projects across web and mobile. My strengths lie in my wide range of proficiencies, ensuring that every part of your project is handled with expertise. As a full stack developer, I’m skilled at Python and Node.js- staples for high-performance operations like the ones your project demands. When it comes to your needs for a AI WhatsApp Message Parser, my proficiencies in API Integration, Data Extraction and Data Processing will prove invaluable. I’ll leverage my working knowledge of Hugging Face, known for its state-of-the-art NLP models like OpenAI GPT-4, to ensure accurate extraction of product details, customer inquiries, and sales data from the incoming messages. Ultimately, as an experienced developer with a deep understanding of not just AI but also how it integrates with applications at scale, I’m more than equipped to build a system that will not only meet but exceed your expectations for high throughput and reliability. Let's have a discussion on how we can tailor this solution specifically so it meets all the acceptance criteria you've laid out in the project description!

@Zaiyan

I can create a Python script that processes the incoming rows from your "raw_messages" table, extracting product details, customer inquiries, and sales data using an AI model. My first step will be to outline the relational schema and ensure it aligns with your requirements before starting the coding process. Based in Toronto, I work quickly and maintain open communication throughout the project.

@shayankhan333

Hitting 90% field accuracy across 500 groups usually comes down to prompt discipline plus a strict JSON schema on the model response, not a bigger model. I'll build a Python worker that pulls unprocessed rows in batches, sends them through GPT-4 with function-calling for structured output, and writes to products, enquiries, and sales tables each linked back to raw_message_id for traceability. One catch worth flagging: WhatsApp messages often bundle multiple products in one post, so the schema needs a one-to-many from message to product, not one-to-one. 1) Postgres or MySQL on the target DB? 2) Should ambiguous rows land in a review queue, or just get a confidence flag? Cheers Shayan

@alluankit01

Hi, I can build the AI message parsing pipeline that reads from your raw_messages table, extracts product details, customer enquiries and sales data, then writes structured records into a clean relational schema. My approach will be to use Python with SQLAlchemy and async/batch processing so the system can handle high message volume without backlog. Each extracted record will stay linked to the original raw message ID for audit and correction. I can help with: * Raw message table consumption * OpenAI/GPT-based extraction * Prompt and parsing rules * Product/enquiry/sales classification * Normalized database schema * Batch/async processing * Error handling and retry logic * Source message traceability * CLI/cron-ready execution * Test run and accuracy review Deliverables: * Clean documented source code * DDL for products, enquiries, sales and mapping tables * DB migration files * Prompt/config files in repo * README with setup and env variables * Sample CLI/cron command * Test run on provided dataset * Accuracy and processing-time notes I’ll focus on simple maintainable code, fast batch processing, and accurate structured extraction that can process 1,000 messages within the required time target. Best regards Ankit

@rohank2502

Your raw_messages table already has the flood from 500+ groups. I will turn each row into products, customer enquiries, and sales you can query and report on. I can start right now. In 24-48 hours you get a live sample on your own messages: products, customer questions, and sales figures, each linked to the original row so a miss is easy to fix. The script then keeps pace with thousands of messages an hour and writes only clean records. Rules stay with the code so you can correct them later. Can you share a small slice of real rows so I can run the sample on your data first?

@naeem1114

When the inbound stream spikes to thousands of messages per hour, the OpenAI API's rate limits can quickly become a bottleneck. I'll build an async queue that batches calls, respects the rate limit, and stores each response ID to avoid re‑processing the same row. Each extracted record will include the original rawmessageid, so any mis‑classifications stay linked for easy correction. A common pitfall is assuming the LLM always returns well‑formed JSON; a stray newline can break the parser. I'll add a lightweight validator that falls back to a safe string parse and logs the raw output for later review. Ready to start right away and get the first batch running on your VPS.

@iterativeTech

As a highly seasoned AI and Cloud Data Engineering Specialist, I have successfully delivered transformative solutions for organizations grappling with similar data complexity to yours. Building on your solid foundation, I will create a new script that process thousands of messages hourly with absolute accuracy, enabling seamless querying and efficient reporting. My use of standard tech like Python alongside tried-and-tested frameworks such as SQLAlchemy, Node+Prisma, or equivalent ensures maximum maintainability and easy integration with your existing infrastructure. My well-honed skills in AI, machine learning (NLP included), cloud data engineering, and real-time analytics perfectly align with your project's core requirements. I am well-prepared to design and deploy predictive models to classify product details, customer inquiries, and sales data using standard parsing rules. Rest assured that I'll provide clean, documented source code with clarity at its core, making it easily understandable for future maintenance.

@petropankovdev

Your raw_messages table is the easy half. The hard half is making LLM output land in a schema you can actually query, and stay traceable when a field comes out wrong. How I would build it: Extraction worker in Python. It reads raw_messages by an id watermark, groups messages into batches (20-40 per call, which is where the token cost drops sharply), and calls the model with a strict JSON schema via structured outputs, not free-form prose parsing. Every response is validated with pydantic before it touches the database. Async workers behind a bounded queue, so a spike of a few thousand messages an hour drains instead of piling up. Anything that fails validation or comes back low-confidence goes to a review table, never silently dropped. Schema: products, enquiries, sales, each carrying raw_message_id as a foreign key back to your table, plus an extraction_runs table (model, prompt version, confidence per field). That is what makes a mis-classified field a two-minute fix instead of an investigation: you can always walk back from a wrong row to the exact message and the exact prompt that produced it. Alembic migrations live in the repo alongside the prompts and parsing rules. Accuracy: I run your 1000-message batch, produce a per-field accuracy report, and tune prompts until it clears 90 percent. Under five minutes on a mid-tier VPS is realistic with async batching, and I will show the timing from the actual run rather than promise it. What you get, in 7 days: documented source, DDL plus migrations, a README with env vars and a cron or CLI example, and the accuracy report from the test batch. About me: one completed project on this account, rated 5 out of 5, delivered on time and on budget. Outside the platform I have 15 merged pull requests into third-party open-source projects, mostly a 187-star Go security tool, each reviewed and accepted by the maintainers. Closest recent work: a Python harvesting pipeline that collected, deduplicated and validated a large contact database from public sources, and a Python bot running on a Linux VPS. One thing worth deciding before I start: should the extractor run as a scheduled batch, or as a continuous worker tailing new rows as your scraper inserts them? It changes how I handle the watermark and back-pressure. Petro Pankov, BotCraft Group

@camilob44

The five-minute number on a thousand messages is a rate-limit problem before it is a code problem. One GPT-4 call per message, run sequentially, lands around forty-five minutes. Batching twenty to fifty messages into a single call with a structured-output schema, then running those calls concurrently up to your tier's limit, brings it inside five minutes and cuts token spend by roughly the same factor. That decision gets made first and the schema follows it. Every extracted row carries raw_message_id and the batch id, so a mis-classified field is one join away from the original chat line. That is the traceability you asked for. Products, enquiries and sales data each land in their own table keyed back to raw_messages, with the DDL shipped as migrations rather than a one-off script. On the 90% target: field-level accuracy needs a hand-labelled gold set to be measurable at all, so milestone one builds a few hundred labelled messages alongside the extractor and reports accuracy per field. A blended number hides the case where one field is easy and another is not. Closest thing I've shipped: a Go ingestion pipeline parsing an email API into PostgreSQL with incremental sync and deduplication, where the difficulty was turning unstructured text into rows that stayed queryable. Rs 45,000 across three milestones: schema and gold set, extractor and batching, then the test run on your 1,000 messages.

@guptakishan805

As an experienced Full Stack Developer with over 5 years in the industry, I've specialized in developing and implementing custom AI-powered solutions and creating platforms that handle large-scale data processing. Your project requires precisely the skill set I've honed throughout my career. My proficiency includes API Integration, Large Language Models, Node.js, and Python - all the key tech stacks your project demands. In the course of my work, I've successfully implemented machine learning models like OpenAI GPT-4 to process vast volumes of data with high precision. With your dedicated purpose in mind, I am committed to developing a script that will efficiently read WhatsApp messages from the "raw_messages" table, apply AI models for extracting product details, customer inquiries, and sales data while keeping a well-designed relational schema to enable easy storing, querying and report generation. Moreover, my capability spans beyond just code-writing. I prioritize providing clear documentation and maintaining clean source codes which are vital for future maintenance and personalization. I'll ensure that the delivered project includes comprehensive documentation including DDL for target tables, environment variables details setup steps, CLI or cron sample command and a test-run on real datasets prior to final code delivery so that it fulfills all your requirements. Let's partner together for a quality solution built on clarity and simplicity as you need.

@aakaashberi

Hi — this is a clean LLM-ETL pipeline over your existing raw_messages table, exactly the kind of high-throughput extraction I build. My approach: Architecture: a worker that reads raw_messages incrementally (by last-processed id / watermark, so it never re-does rows and never misses new ones), batches them, and sends each through GPT-4 (or a comparable LLM) using structured/function-calling output — so the model returns strict JSON for your three targets (product details, customer inquiries, sales data), not free text I'd have to re-parse. Throughput: async + batched processing with a worker pool and rate-limit-aware backoff, so even at thousands/hour the backlog never grows. Obvious/simple rows can route to a cheaper model to keep token cost down. Accuracy + traceability (you flagged this): every extracted record keeps a foreign key back to its source raw-message id plus a confidence flag — anything mis-classified stays traceable to the original, and you can re-run just those. Schema: separate normalized tables — products, inquiries, sales — cleanly linked, so you query and build sales reports without touching the raw chat logs. To scope: which DB is raw_messages in (Postgres/MySQL)? GPT-4 specifically, or open to an open model to cut cost? And can you share 5–10 sample rows so I calibrate the extraction prompt first? Everything in your repo — you own it.

@olehh6

Hello, The main challenge is turning unstructured WhatsApp messages into reliable relational data at high throughput while preserving a direct audit trail back to every raw message. I’d build an asynchronous Python pipeline using SQLAlchemy, with workers reading new rows from raw_messages, batching messages where appropriate, and sending structured extraction requests to GPT-4 or a comparable LLM. The response would be validated against a strict schema, normalized, and written into separate products, enquiries and sales tables with the original message ID retained on every extracted record. To handle thousands of messages per hour, processing would use batching, concurrent API requests, retry/backoff handling and database transactions, while preventing duplicate processing through message/status tracking. Prompts, extraction schemas, normalization rules and migrations would remain version-controlled for easy maintenance. I’d first test against the provided dataset, measure field-level accuracy, tune extraction rules for ambiguous messages, and benchmark 1,000 messages against the five-minute requirement. The final delivery would include documented source code, DDL/migrations, environment configuration, CLI/cron usage and a reproducible test run. Could you share the existing raw_messages schema and target database engine so I can design the extraction and indexing strategy around your current infrastructure?

@karthikresonite

Dear Client, We at Resonite Technologies are excited to submit our proposal for your AI WhatsApp Message Parser project. With proven expertise in AI-driven solutions and a dedicated team of developers, we are well-equipped to meet your requirements. Our approach will ensure high throughput and accuracy while processing thousands of messages per hour. Leveraging Python with SQLAlchemy or Node + Prisma, we will create a robust script that extracts product details, customer inquiries, and sales data from your “raw_messages” table. Our focus will be on maintaining traceability of misclassified fields for easy correction. Deliverables will include clean, documented source code, DDL for the target tables, a concise README for setup, and a test run demonstrating at least 90% field-level accuracy on a sample dataset. We prioritize clarity and simplicity in our code, ensuring you can easily maintain the solution post-deployment. We are confident that our expertise aligns perfectly with your project requirements. Best regards, Karthik B Resonite Technologies