Most AI tools dump raw data into an LLM and hope for the best. Tabwise uses context engineering - pre-processing data structure, optimizing prompts for analytical tasks, and post-processing outputs into business-ready formats.
You upload a CSV, ask questions in plain English, and get charts, insights, and executive summaries. No formulas, no SQL, no Python notebooks - just answers you can share with stakeholders.
Early feedback has been incredible. A data analyst analyzing product usage data told me "Tabwise can do my 3-4 hours of work in just 20 queries (5 minutes)." A digital marketing agency said "It used to take them all week to analyze ad spending data, now they can do it in a day." Recent benchmarking across 10+ large datasets shows Tabwise consistently outperforms ChatGPT (GPT-5 Thinking) and Claude (Sonnet-4): 100% answer accuracy with 4x faster response times, better data visualisation and in-depth story telling.
Try it at https://tabwise.ai - would love feedback from the community. Thanks! Demo: https://www.youtube.com/watch?v=uuiPmyPE_Js
abhegd•2h ago
Previously I built Allyce AI (https://allyce.ai/), which helps answer questions, qualify leads, and handle complaints on websites (AI for sales/SDR + CS). When I started as an independent builder, the first thing I built was a sophisticated GraphRAG pipeline that powers Allyce. The customers using Allyce needed something to analyze sales data and ad performance data, which wasn't possible with RAG - that's what led me to build Tabwise as a separate product.
The key insight was that general AI tools fail at data analysis because they skip three critical steps: 1. Pre-processing: Cleaning data, inferring schema and structuring context specifically for analytical queries 2. Context engineering: Crafting prompts that understand data relationships, business context, and expected output formats 3. Post-processing: Converting raw LLM outputs into properly formatted charts, executive summaries, and actionable insights
This pipeline is why Tabwise can consistently outperform ChatGPT (GPT-5 Thinking) and Claude (Sonnet-4) on data analysis tasks - it's not just the model, it's the entire system.
Tech stack: Next.js, Vercel AI SDK, E2B, I automatically route tasks to the best model based on complexity (Powered by Claude sonnet-4 and OSS models via Fireworks AI)
Right now I'm focused on nailing spreadsheets (.xlsx, .csv), but have more data source integrations planned based on what users actually need.
Would love your thoughts.