Launch your first enterprise AI assistant in weeks, not months
IgnitionRAG gives innovation teams everything they need to turn enterprise documents into a production assistant: ingestion, retrieval, agents, evaluation and observability, ready out of the box. Stop rebuilding the same RAG stack for every project.
Compatible with every LLM provider, with no token markup
Connect your own OpenAI, Anthropic, Mistral or Azure OpenAI keys. You pay the providers directly.
Your team keeps rebuilding the same infrastructure
Innovation teams are under pressure to ship AI fast. Yet most projects spend months rebuilding the same plumbing before a single business user sees value.
- Document ingestion
- Search & retrieval
- Chat interfaces
- Agent orchestration
- Evaluation
- Monitoring
- Access control
Months of engineering before the first business user sees value. Budgets burn, stakeholders wait, and proofs-of-concept stall before they ever reach production.
Every month spent on plumbing is a month without adoption
Rebuild the stack yourself, or start from one that already runs in production.
Without IgnitionRAG
- Months spent building infrastructure
- The same engineering effort, repeated on every project
- Business validation delayed by quarters
- Slow, uncertain time-to-value
With IgnitionRAG
- A working assistant in weeks
- Infrastructure you stand up once, reuse everywhere
- Stakeholder feedback in the first sprint
- A clear, fast path to production
IgnitionRAG is the fastest path from enterprise knowledge to a production-ready AI assistant.
From proof-of-concept to a signed engagement.
An IT services firm built its proof-of-concept on IgnitionRAG and used it to win a new engagement with its client. Fast enough, and credible enough, to close the deal.
IT services firm (ESN) · France
Why innovation teams choose IgnitionRAG
Spend your time on use cases and adoption, not on infrastructure you'd only have to rebuild.
Accelerate delivery
Launch AI assistants in weeks instead of months. The infrastructure is already built, tested and production-ready.
Focus on use cases
Put your team on business problems and adoption, not on reassembling ingestion, retrieval and agents for the tenth time.
Production-ready
Built for real enterprise deployments: evaluation, observability, governance and access control from day one.
Developer-friendly
A REST API, TypeScript and Python SDKs, and a native MCP server. Your engineers integrate in minutes. No black box.
Business-friendly
A dashboard your business stakeholders run themselves: imports, feedback, costs and ROI. No engineer in the loop.
A complete RAG platform from POC to production
Multimodal ingestion, hybrid search, reranking, agents with tools — without assembling five frameworks.
Text, PDF, images, tables
Import PDFs, DOCX, PPTX, Excel, Parquet, JSON and images. OCR, figure extraction, contextual chunking — we handle it all.
Describe the workflow, the agent builds it
Tell the in-builder agent what you want in plain language, or drive it from Claude and Cursor via MCP. 35+ nodes assemble and connect. Drag-and-drop stays there to fine-tune.
One platform, every stakeholder aligned
Leadership sees the ROI. Business teams run the assistant. Engineering integrates. Everyone works on the same data, with no friction.
For innovation & leadership
ROI dashboards, audit logs, RBAC governance and full traceability. Production metrics to bring to your stakeholders, not promises.
For business teams
Drag-and-drop imports, user feedback, widget deployment, cost and quality monitoring. Not a single line of code to write.
For engineering
REST API with OpenAPI, TypeScript and Python SDKs, native MCP server. Plug your agent tools in under 5 minutes, with no black box.
// 1. Install
$ bun add @ignitionai/sdk
import { IgnitionAI } from "@ignitionai/sdk";
const ai = new IgnitionAI({ apiKey });
const stream = await ai.chat.stream({
collectionId: "docs",
message: "How do I deploy?",
});
for await ({ delta } of stream) {
process.stdout.write(delta);
}Your data in, your answers out
Ingest your data where it lives, answer where your teams work. Native connectors, no assembly.
Data sources
Delivery channels
One license for your clients, plans to evaluate
The main channel remains Cloud, support and dedicated deployments. Free, Pro and Scale plans exist to evaluate the platform self-service before deploying.
Deploy IgnitionRAG for your clients with a firm license
Dedicated deployment, guided onboarding, nested firm → client multi-tenancy, priority support, and commercial terms tailored to your portfolio.
Pro
- 15 Collections
- 2,000 Documents
- 20 Workflows
- 5,000 Runs/month
- 5 MCP Servers
- All triggers
- API + SDK access
- 1 User (solo)
Scale
- 50 Collections
- 10,000 Documents
- 100 Workflows
- 50,000 Runs/month
- 50 MCP Servers
- All triggers
- API + SDK access
- 15 Members
- Priority support
Enterprise License
Firm license
Self-hosted or hosted by us
- Hands-on onboarding
- Nested multi-tenant (firm → your clients)
- Dedicated deployment
- Unlimited everything
- Your own LLM keys
- Guaranteed SLA
- Dedicated support
- Custom domain
- SSO / SAML
Questions? Answers.
Everything you need to know.
What is IgnitionRAG?
IgnitionRAG is the AI infrastructure layer that turns your enterprise knowledge into production-ready AI assistants. It bundles the whole RAG stack (ingestion, retrieval, agents, evaluation, observability, access control) into one platform, so your team ships in weeks.
How fast can we launch our first assistant?
Most teams go from documents to a working, production-grade assistant in weeks, and a first proof-of-value can run in days. You spend your time on the use case and adoption, not on infrastructure.
Do we have to rebuild ingestion, retrieval and agents ourselves?
No. Multimodal ingestion, hybrid search with reranking, agents with tools, evaluation and observability come as one platform. You don't assemble five frameworks and glue them together.
Is it production-ready for enterprise?
Yes. Evaluation (Precision, Recall, MRR and LLM-judge), full observability over traces, latency, tokens and cost, plus governance and role-based access control are part of the platform, not add-ons you build later.
How do our engineers integrate it?
Through a REST API with OpenAPI, official TypeScript and Python SDKs, and a native MCP server (32 tools) for Claude, Cursor and your IDE. Everything the platform does is reachable from code, never locked behind our UI.
Can business teams run it without engineers?
Yes. Business stakeholders manage imports, user feedback, costs and ROI from a dashboard, and answers ship through an embeddable widget or your existing channels. No engineer in the loop for day-to-day operation.
Which LLMs can we use, and how does pricing work?
IgnitionRAG is model-agnostic and BYOK (bring your own key): OpenAI, Anthropic, Mistral, Azure OpenAI, or any OpenAI-compatible endpoint. You connect your own keys and pay the providers directly, with zero markup on your tokens.
Where is our data hosted, is it compliant, and can we self-host?
Hosting is in France/EU with GDPR compliance, audit logs and right-to-erasure built in. With BYOK your data isn't routed through our accounts, and you can self-host via Docker or run a dedicated deployment with SSO/SAML, SLA and onboarding (Enterprise).
See your first enterprise AI assistant come to life
Book a 30-minute demo. We'll map your documents, your use case and the fastest path from enterprise knowledge to a production assistant.