The apps we added to this market most recently.
| # | App | Added |
|---|---|---|
| 1 | KernelAI Run 50+ AI models locally on iPhone and Android. Chat, read PDFs and documents with on-device RAG, understand images with vision models, and write code offline. Dictate prompts, customize responses and import GGUF models (experimental). On iPhone, use Siri, Shortcuts and Apple's built-in AI on supported devices. Add optional web search with your own API key. Free, with no account, ads or subscription. Download a model once, then use it offline. Your local chats and documents stay on your device. | |
| 2 | QAgent Automate AI agent quality testing. Score correctness, detect hallucinations against ground truth, verify policy adherence, and benchmark RAG before bad responses reach real customers. | |
| 3 | PageIndex PageIndex gives you accurate, trustworthy answers across long, professional documents your work depends on. Bring in your entire document set, ask your hardest question, and click any citation to jump to the exact highlighted source line, so you can verify it in seconds. | |
| 4 | IQ Routing IQ Routing is a drop-in gateway. We classify each request, serve from cache, and route to the cheapest model that clears the quality bar and completes the task. The insight others miss: an agent is a trajectory, not a stream of independent calls. We route each step based on where it sits in the run: a cheap model on boilerplate, the strongest model on what matters. | |
| 5 | MarkItDown Markitdown Online is an independent browser-based converter inspired by Microsoft's open-source MarkItDown project. It converts common documents, spreadsheets, presentations, web pages, structured dat | |
| 6 | PE PenguinHarness PenguinHarness is an open-source self-improving harness built by the team behind LlamaFactory. Its AI-native SDK enables agents to build, evaluate, and optimize other agents. It supports 1,000+ models, reusable skills, tool and context management, automatic data generation, multi-agent evaluation, and closed-loop harness evolution. With one prompt and about $0.02, an agent can build a complete RAG application. | |
| 7 | KI Kit For AI Persistent memory and grounded knowledge for AI agents — native MCP tools your agent calls directly. Drop in any file or URL; skip building and babysitting a RAG stack. One API, works with any model. Free to start. | |
| 8 | Algoran Why we built this: | |
| 9 | pumaDB Most AI agent workflows lose useful context between sessions, tools, and chats. The usual fixes are either too manual, like copying notes into docs, or too heavy, like setting up a database, vector store, or custom RAG stack. pumaDB gives agents a simple shared place to save and reuse notes, facts, preferences, project context, transcripts, task state, and other useful memory. No database setup, vector DB, or infrastructure to manage | |
| 10 | NeuroRank NeuroRank® is the patent-pending AI visibility intelligence platform that deconstructs how ChatGPT, Gemini, Claude, and Perplexity represent your brand, diagnoses where your AI presence is broken, and prescribes exactly what to fix. Influences the RAG layer and accelerates AI memory. Tracks inclusion growth. NeuroRank provides the foundational infrastructure for your GEO/LLMO practice. Allowing teams to manage their AI presence with total visibility and controlled execution. | |
| 11 | Powabase Powabase is a backend-as-a-service for AI-native applications, combining Postgres, RAG, agents, memory, workflows, and automation primitives in one platform. It helps agencies and in-house IT teams build new AI apps or add AI automation to existing products without stitching together fragmented infrastructure. Designed to work seamlessly with modern coding agents, Powabase helps teams ship faster while building more robust, token-efficient systems. | |
| 12 | AgentSwarms Learn to build AI agents in your browser with guided live lessons |
Grouped by what each app says it does. 57 of 93 apps fit a group so far.
| Type | Apps | Share | Examples |
|---|---|---|---|
| Agent platforms and builders | 17 | 29.8% | agentcloud, AI Hive, Algoran |
| AI infrastructure | 13 | 22.8% | Agentset, Airweave, IgnitionRAG |
| AI assistants and apps | 9 | 15.8% | Delos, FairPact AI, Fluent |
| Data crawling and parsing | 8 | 14.0% | Crawler.sh, DataFuel, Document Parser by Contextual AI |
| Memory for agents | 4 | 7.0% | DeltaMemory, HelixDB, Kit For AI |
| Evaluation and monitoring | 3 | 5.3% | Athina AI, NeuroRank, QAgent |
| Learning and careers | 3 | 5.3% | AgentSwarms, Learn how to build rag, Remote Ai |
What we could detect on their websites.
| Framework | Apps | Share |
|---|---|---|
| Next.js | 38 | 71.7% |
| WordPress | 6 | 11.3% |
| Express | 3 | 5.7% |
Of 53 apps whose framework we could detect.
| Host | Apps | Share |
|---|---|---|
| Vercel | 30 | 96.8% |
Of 31 apps whose host we could detect.
How many of the 93 apps show a price, and how many sell to large companies.
| Measure | Apps | Share of 93 |
|---|---|---|
| Publish a pricing page | 32 | 34.4% |
| Offer an enterprise plan | 4 | 4.3% |
A RAG platform lets a language model answer questions using your own data. It combines fast retrieval with generation so the model stays grounded in the right sources.
You start by connecting content. The platform ingests files, pages, tickets, chats, and databases, then cleans, splits, and embeds the text so it can be searched semantically. Tools like crawlers and data APIs help pull in fresh material. Olostep can gather web data, Paragon can connect third party apps, and MarkItDown can turn documents into clean text before indexing.
When a user asks a question, the system turns it into a vector search and keyword search to find the most relevant passages. It may re-rank results, filter by metadata, and expand queries with synonyms. The top passages become the context.
The platform builds a prompt that includes the user question plus the retrieved passages, instructions, and policies. The language model then writes an answer that cites or quotes the sources. Some tools like Delos expose this as document Q&A.
Post-processing checks the answer for completeness, adds links or citations, and masks sensitive data if needed. Results can flow into agents and workflows. Powabase provides RAG with memory and automation, and AI Hive can wrap RAG answers in agents that take actions. Assistants like Fluent bring the same flow to the desktop.
Feedback closes the loop. Users can rate answers, flag gaps, and suggest better sources. The platform updates indexes, tunes chunking and filters, and can cache frequent answers to speed up future queries.
Product and engineering teams use RAG to add grounded answers to apps, speeding feature delivery with ready-made ingestion, indexing, and retrieval.
Support and success teams deploy knowledge bots that reduce queue volume and improve consistency, with clear citations to internal articles.
Sales and marketing teams self-serve briefs, talk tracks, and asset search, cutting time spent hunting across slides, docs, and deal rooms.
Operations and HR teams turn policies and procedures into an interactive handbook that stays current as documents change.
Research and analytics teams run concept search across large corpora, extract facts with traceability, and build repeatable research workflows.
IT and knowledge management centralize sources, control access, and enforce governance. Connectors like Paragon and pipelines like Olostep help them keep indexes fresh.
Startups and agencies prototype AI assistants quickly using platforms like Powabase and Delos, while enterprises adopt no-code builders like AI Hive to scale agents across departments. Individual professionals use desktop tools like Fluent for fast, local querying of their files.
Vendors mix subscriptions with usage-based components tied to data, queries, or automation.
| Pricing model | How it works |
|---|---|
| Tiered subscription | Feature and capacity bundles with higher tiers unlocking advanced retrieval, connectors, or automation. |
| Per seat | Each person with an account adds to the bill. |
| Usage based | Charges scale with queries, tokens, or compute used by retrieval and generation. |
| Data at rest | Pricing based on stored documents, embeddings, or index size. |
| Per integration or workflow | Each connected data source, agent, or active workflow contributes to the total. |
| Free tier | Limited projects or queries to evaluate the platform before upgrading. |