Agentset provides developers with a platform to create AI chat and search applications that deliver accurate, reliable answers without requiring expertise in retrieval‑augmented generation. The service offers multimodal support for images, graphs, and tables, and includes metadata filtering and customizable citation previews to tailor responses to specific data subsets. By delivering high‑accuracy results on benchmarks like MultiHopQA and FinanceBench, Agentset helps teams ship AI‑powered products with confidence.
Funding
Funding not disclosed
Founders
Product
Problem
Developers building AI chat and search applications must assemble complex retrieval‑augmented generation (RAG) pipelines—handling document parsing, chunking, embedding, vector storage, retrieval tuning, and citation generation—which is time‑consuming and error‑prone, especially for multimodal data.
Solution
Agentset offers a managed RAG platform that provides production‑grade accuracy out of the box without requiring deep RAG expertise. Users upload documents in over 20 formats via JavaScript or Python SDKs, and the service automatically parses text, images, tables, and graphs, creates embeddings, and stores them in a searchable vector index. Built‑in metadata filtering, citation generation, and customizable preview links enable developers to deliver reliable, source‑attributed answers. The platform supports both cloud‑hosted and self‑hosted deployments, allowing teams to scale from small prototypes to enterprise workloads while focusing on product features rather than infrastructure. Benchmark results on MultiHopQA and FinanceBench demonstrate high answer quality across diverse data sources.
Target Audience
Agentset targets software engineers and product teams building AI‑powered chat, search, or knowledge‑base applications that require reliable RAG functionality, from startups to large enterprises.
Features
- Multimodal ingestion supporting images, tables, graphs and 22+ file types (PDF, DOCX, CSV, etc.) via JavaScript and Python SDKs
- Automatic document parsing, chunking, embedding, and vector storage with state‑of‑the‑art retrieval and ranking models
- Metadata filtering to restrict answers to specific data subsets
- Built‑in citation and source linking for transparent, verifiable responses
- Customizable preview links and chat interface for rapid external feedback collection
- Flexible deployment options: managed cloud service, bring‑your‑own‑infrastructure, or on‑premise for enterprise compliance
- Scalable pricing tiers with free tier (1,000 pages, 10,000 retrievals) and pay‑as‑you‑grow options