Artificial Wit
Summary
Connecting a modern LLM to a legacy ERP without writing a pile of glue code is the project that quietly absorbs entire quarters — Artificial Wit exists to short-circuit that.
The platform sits between your existing APIs, documents, and knowledge bases on one side and any LLM — Claude, ChatGPT, Gemini, or a local model — on the other. You connect REST or GraphQL endpoints, upload docs or point at a database, then the platform exposes every configured API as a Model Context Protocol tool, discoverable by any MCP-compatible client. No schema migration, no re-platforming. The free tier caps you at three API connections, which covers a proof of concept but hits the wall fast for a real ERP environment. Role-based access control is included, which matters the moment clinical documents or order data enter the picture.
Bottom line: Pick this to get a working AI layer over a single legacy ERP in days without touching the underlying system — plan a richer tooling strategy when you need more than three live API connections or require self-hosted deployment for data-residency compliance.
Pricing Plans
Subscription- Free Tier
- Up to 3 API connections
Free
Up to 3 API connections, no credit card required
- MCP server access
- Knowledge base ingestion
- Basic agent configuration
View full pricing on artificialwit.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- No-code MCP tool generation from any configured API, which means Claude Desktop or Cursor can call your internal ERP endpoints without a custom integration build for each LLM client.
- Retrieval-augmented generation with cited answers baked into the knowledge base, so the assistant returns sourced responses from your actual documents rather than the model's training data — which removes the audit problem for healthcare and compliance contexts.
- Role-based access control included at the platform level, so permission-aware queries against clinical documents or financial data do not require a separate access layer bolted on afterward.
- Provider-agnostic LLM routing — OpenAI, Anthropic, Gemini, or a local model — so swapping the underlying model when pricing or performance shifts is a configuration change, not a rebuild.
- Agents coordinate multiple APIs and knowledge sources in a single workflow, so a query that needs to cross-reference an ERP record and an internal policy document does not require you to wire those calls together manually.
Cons
Sign in to edit- The free tier limits you to three API connections — a single ERP integration with a knowledge base and one additional service exhausts it. Teams scoping a real enterprise deployment hit the paid tier before the pilot is done.
- No self-hosted deployment option is available. Organizations under data-residency mandates, HIPAA BAA requirements that prohibit cloud egress, or air-gap security policies cannot use the platform at all — those teams evaluate self-hostable alternatives instead.
- Agent configuration happens through a no-code admin panel, which covers straightforward tool-call chains but gives no indication of supporting complex branching logic based on intermediate results. Teams that need multi-step conditional flows — branching on what the ERP returned before deciding which knowledge base to query — report adding a custom orchestration layer, at which point they are maintaining two systems.
- The platform is not open-source, so debugging unexpected agent behavior or auditing how credentials are handled requires trusting vendor documentation rather than inspecting the runtime directly. For security-sensitive enterprise procurement, that gap extends review cycles.
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About
- Platforms
- Web, MCP clients (Claude Desktop, Cursor, ChatGPT)
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-07-15T18:29:44.755Z
Best For
Who it's for
- Enterprises modernizing legacy systems without migration
- Teams already using Claude, ChatGPT or Gemini who need tool access
- Organizations requiring cited answers from internal documents
- No-code configuration of MCP tools from existing APIs
What it does well
- Natural-language queries against SAP, Oracle or Dynamics ERP data
- Order lookups and catalog Q&A on real retail data via MCP
- Permission-aware search across clinical documents and internal healthcare systems
- AI agent workflows that orchestrate multiple APIs and knowledge sources
Integrations
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Frequently Asked Questions
- Is Artificial Wit free?
- Artificial Wit has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is Artificial Wit open source?
- No — Artificial Wit is a closed-source tool. Source code is not publicly available.
- Does Artificial Wit have an API?
- Yes. Artificial Wit exposes a developer API. See the official documentation at https://artificialwit.com for details.
- What platforms does Artificial Wit support?
- Artificial Wit is available on: Web, MCP clients (Claude Desktop, Cursor, ChatGPT).
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Enterprises running SAP, Oracle, or Dynamics have spent years defending those systems against rip-and-replace proposals — and spent just as long explaining to every new AI initiative why the data is not accessible. Artificial Wit wraps those systems in a unified API aggregation layer: you plug in REST or GraphQL endpoints, ingest PDFs, wikis, or database content, and the platform turns everything into a single conversational interface. Agents are configured from a no-code admin panel, credentials are centralized, and the assembled tool set is immediately callable by the LLM of your choice.