Graphlit is an API-first platform built by Unstruk Data, designed to help developers build AI applications on top of unstructured content. The platform handles everything from data ingestion and knowledge extraction to semantic search and large language model (LLM) integration — all without requiring teams to manage their own infrastructure. Whether you're processing PDFs, podcasts, videos, or live web feeds, Graphlit turns raw, messy content into a structured, queryable knowledge graph. It's aimed squarely at developers and engineering teams who want to ship RAG-powered applications fast, without the DevOps overhead.
What is Graphlit?
Graphlit belongs to the growing category of Retrieval Augmented Generation (RAG) infrastructure platforms — tools that sit between your raw content sources and your AI models, handling the hard work of chunking, embedding, storing, and retrieving information. Unlike general-purpose vector databases or standalone document parsers, Graphlit provides a full, serverless pipeline: ingest content from virtually any source, extract structured entities using Schema.org's entity data model, and expose everything through a clean API that connects to leading LLMs like GPT-4. The result is a platform that positions itself as a complete backend for AI knowledge applications, rather than just one piece of the puzzle.
Key features
Broad unstructured data ingestion
One of Graphlit's clearest strengths is the sheer variety of content types it can process natively. PDFs, images, videos, podcasts, RSS feeds, web pages, and messaging platform outputs are all supported without requiring custom preprocessing pipelines. This breadth matters enormously in practice: real-world enterprise data is rarely clean or uniform, and building separate ingest logic for each content type is one of the most time-consuming parts of any AI project. Graphlit abstracts this complexity behind a single API surface.
Knowledge graph construction with semantic search
Once content is ingested, Graphlit converts it into a contextualized knowledge graph using Schema.org's entity model as its backbone. This approach goes beyond simple vector storage — entities, relationships, and metadata are all preserved, making retrieval more precise and context-aware. Developers can then query this graph using vector-based semantic search, enabling conversational AI features that surface genuinely relevant information rather than just lexically similar text. This is the foundation of robust RAG-based prompt engineering, and Graphlit bakes it in by default.
LLM integration and conversational AI
Graphlit connects to leading language models — including OpenAI's GPT-4 — allowing developers to build chat and Q&A interfaces directly on top of ingested knowledge. The platform handles the retrieval step automatically, meaning the LLM receives relevant, grounded context rather than relying solely on its training data. This reduces hallucination risk and makes it practical to build domain-specific assistants on top of proprietary or frequently updated content. The integration is handled at the API level, so developers retain control over prompts and response handling.
Enterprise-grade security and multimedia management
Beyond data processing, Graphlit includes a multimedia content management layer with encrypted storage, role-based access control (RBAC), image thumbnail generation, and preview creation. Granular usage tracking lets teams monitor costs and stay compliant with internal governance requirements. For organizations handling sensitive documents or operating in regulated industries, these features — often bolted on as afterthoughts in developer-first tools — are available out of the box. The serverless, cloud-native architecture also means there are no servers to patch or scale manually.
Pricing and plans
Graphlit offers a free tier, making it accessible for developers who want to prototype or evaluate the platform before committing. As with most infrastructure-as-a-service products in this category, pricing scales with usage — specifically with the volume of content ingested and processed. Teams building production applications with large or continuously updated content libraries should review the official Graphlit pricing page carefully, as costs can grow in line with ingestion demand. The free tier is a meaningful on-ramp for solo developers and small teams exploring what the platform can do.
Pros and cons
Graphlit brings a lot to the table for developer teams building knowledge-intensive AI applications. Here's a summary of where it excels:
That said, there are real trade-offs worth considering before adopting Graphlit for a production project:
Alternatives on HyperStore
Anara is a strong alternative for teams whose primary need is document interpretation and research organization. Where Graphlit focuses on developer infrastructure and programmatic pipelines, Anara offers a more accessible interface for interpreting and organizing documents across multiple formats — useful for research teams that don't want to write API code.
For developers building AI-powered applications who want to explore a broader set of AI model integrations, Coralflavor offers a flexible AI chat environment that also touches on app development use cases. It's less focused on knowledge pipelines but useful for rapid prototyping of conversational interfaces.
If your AI application involves location data or real-world sensor feeds alongside unstructured content, Natix Network presents an interesting complement. It combines IoT, AI, and decentralized mapping — a different flavor of unstructured data processing at scale that may pair well with Graphlit's knowledge graph capabilities.
Teams building AI search or discovery features for content marketing may also find value in 30characters, which applies AI to search ad copywriting. While not a knowledge infrastructure tool, it illustrates how extracted insights from platforms like Graphlit can feed downstream content and advertising workflows.
Frequently asked questions
What types of content can Graphlit process?
Graphlit supports a wide range of unstructured content types, including PDFs, images, videos, podcasts, RSS feeds, web pages, and messaging platform data. The platform is designed to handle this diversity natively, so developers don't need to build separate preprocessing logic for each format. That said, highly specialized or proprietary formats may fall outside what's currently supported.
Do I need to manage any infrastructure to use Graphlit?
No. Graphlit is a fully serverless, cloud-native platform. All infrastructure — storage, compute, vector indexing, and scaling — is managed by Graphlit on your behalf. This is one of its core value propositions for developer teams that want to focus on application logic rather than operational overhead.
Is Graphlit suitable for non-developers?
Graphlit is explicitly API-first and designed for developers and engineering teams. There is no no-code or drag-and-drop interface described in the product. Non-technical users or teams without development resources would likely be better served by a document-focused research tool like Anara or a low-code AI app builder.
How does Graphlit handle RAG (Retrieval Augmented Generation)?
RAG is a core, built-in pattern in Graphlit — not a feature you configure separately. When content is ingested, it's processed into a knowledge graph with vector embeddings. At query time, the platform retrieves the most semantically relevant content and passes it as context to the connected LLM. This grounds model responses in your actual data, reducing hallucinations and improving answer accuracy. If you want to understand RAG more deeply before building, our prompt engineering guide for beginners covers the fundamentals.
What LLMs does Graphlit support?
Based on available information, Graphlit supports integration with leading language models including OpenAI's GPT-4. The platform is designed to connect LLM capabilities to the knowledge graphs it builds from your content. For the most current and complete list of supported models, check the official Graphlit documentation directly.
Is there a free plan available?
Yes, Graphlit offers a free tier that is well-suited for development, testing, and early-stage prototyping. Costs scale with production usage — particularly the volume of content ingested — so teams moving to production workloads should plan accordingly. The free tier removes the barrier to initial evaluation, which is a meaningful advantage in a category where many competitors require paid plans from day one.
Graphlit is a well-architected, developer-focused platform that addresses a genuinely hard problem: making unstructured data useful for AI applications without requiring teams to build and maintain complex data pipelines from scratch. Its serverless approach, broad content support, and built-in RAG capabilities make it a compelling choice for engineering teams serious about shipping knowledge-powered AI products. Teams willing to invest in understanding the API and knowledge graph model will find a mature, enterprise-ready foundation to build on.
