Predictable Scale.Zero Surprises.
Start free with developer tools, scale across your engineering organization, or deploy in your sovereign VPC.
All plans include syntax-preserving AST parsing, checkable inline citations, and honest abstention under ADR 0010. No hidden token overage fees.
Unified high-throughput proxy with semantic caching, dynamic routing & token firewall.
Visual graph orchestration for multi-agent loops, sandboxed tools & human verification.
Read-only context server for Claude Desktop, Cursor, and Windsurf IDEs.
Continuous git repository ingestion with branch patterns and glob filters.
Compile-time OrganizationScope injection guarantees zero cross-tenant leakage.
Parses code trees, Markdown, and technical manuals without tearing scope.
Every answer is constructed strictly from retrieved evidence with source anchors.
Deterministic refusal when evidence is absent, eliminating plausible hallucinations.
Deploy on private AWS, GCP, or on-premises Kubernetes with zero telemetry.
Lightning-fast Server-Sent Events delivering token streams with minimal latency.
Unified high-throughput proxy with semantic caching, dynamic routing & token firewall.
Visual graph orchestration for multi-agent loops, sandboxed tools & human verification.
Read-only context server for Claude Desktop, Cursor, and Windsurf IDEs.
Continuous git repository ingestion with branch patterns and glob filters.
Compile-time OrganizationScope injection guarantees zero cross-tenant leakage.
Parses code trees, Markdown, and technical manuals without tearing scope.
Every answer is constructed strictly from retrieved evidence with source anchors.
Deterministic refusal when evidence is absent, eliminating plausible hallucinations.
Deploy on private AWS, GCP, or on-premises Kubernetes with zero telemetry.
Lightning-fast Server-Sent Events delivering token streams with minimal latency.
Perfect for individual developers and open-source projects building with Model Context Protocol.
Designed for engineering, support, and product teams scaling internal AI knowledge automation.
For organizations demanding private VPC custody, air-gapped sovereign deployment, and strict compliance.
Feature comparison
Detailed side-by-side comparison of platform capabilities across tiers.
| Capability | Developer | Growth Team | Enterprise |
|---|---|---|---|
| Universal Ingestion (GitHub, Sitemaps, PDFs, Docs) | 5 sources | Unlimited | Unlimited + Private Connectors |
| Checkable Inline Source Citations | ✓ | ✓ | ✓ |
| Honest Abstention Guarantee (ADR 0010) | ✓ | ✓ | ✓ |
| Model Context Protocol (MCP) Server for Cursor / Claude | ✓ | ✓ | ✓ |
| Embeddable Web Widget with Origin Defense | — | ✓ | ✓ |
| Autonomous Multi-Step Agent Execution | Single-turn | Multi-turn | Custom Graph Workflows |
| Deployment Model | Shared Multi-Tenant Cloud | Isolated Tenant Partition | Private VPC or Air-Gapped Kubernetes |
| Uptime SLA | Best effort | 99.9% | 99.99% with Financial Guarantee |
An assistant your customers can act on
The problem with putting a model in front of customers is not that it cannot answer. It is that it answers anyway when it should not. MITHUNAI is built around the opposite behaviour: an answer is checked against your own content before it is given, and withheld when that content does not support one.
Grounded in your content
Retrieval assembles evidence from your corpus, and the answer engine is required to build its response from that evidence rather than from what the model happens to recall.
Citations a reader can follow
Each answer carries the passages it was built from, so the person reading it can check the source instead of trusting the system.
An honest "I don’t know"
When the evidence does not support an answer, the engine abstains and says so. That is the product working — an assistant that answers everything is one you cannot trust on anything.
Isolated per organisation
Tenant scope is a parameter that has to be passed, not a convention to remember. Code that omits it does not compile into a working query — it fails closed.
What to check before you trust an AI platform
Security review questions, answered as they stand today. Where something is not built yet, this page says so — the roadmap is below, and it is kept separate from what is delivered.
No outbound calls you did not configure
The default posture makes no telemetry or analytics call. Any host the platform talks to is configured and documented.
Secrets are encrypted at rest and never logged
Credentials do not appear in source, tests, fixtures or logs, and log-bound text is sanitised on the way out.
Parameterised queries, enforced by tooling
Query construction is checked by repository tooling rather than left to reviewer attention.
Per-request ceilings and rate limiting
Request volume and per-request cost both have bounds, so one caller cannot consume the deployment.
Not built yet
These are being built and are not part of the platform today. They are listed so an evaluation can plan around what exists now.
Validation against a live provider
No model provider is bundled, and the platform has not yet been run end to end against a live one. Every claim on this page rests on the source and its tests, not on a production deployment.
Agent actions and workflows
Assistants answer today. Agents that plan over several steps, call tools and act in external systems are a later direction, and are not built.
MCP client
The MCP server half is delivered as three read-only tools. Consuming external MCP servers from inside MITHUNAI is deferred.
Per-tenant quota and spend caps
Rate limiting bounds request volume today. Measuring a tenant against a usage or spending budget is not built.
SSO, SCIM and fine-grained RBAC
Enterprise identity integration and finer permission granularity are later milestones.
Technical questions, answered directly.
Everything an enterprise architect or security officer needs to verify before deploying MithunAI.
Connect your documentation. Give your customers and teams answers they can check.
MITHUNAI is in active development toward its first release. If you are evaluating how your organisation will answer questions from its own knowledge, we would like to hear what you need.