Isolation by tenant
Tenant boundaries keep data, sessions, and access context separated.
Multi-tenant B2B chatbot platform
Turn business knowledge into an AI chatbot ready for real operations
Noha Bot helps teams configure service-specific chatbots, ingest knowledge documents, stream answers in realtime, and manage sessions from one clear operational platform.
See how Noha Bot answers with your real business data before rollout.
- RAG isolated by service
- Realtime answers over SSE
- Manage sessions, documents, and config
RAG by chatbot
Prompt, model, and knowledge are isolated per service for cleaner control.
Live response delivery
Answers stream in realtime for a smoother and more trustworthy chat experience.
Document pipeline
Knowledge files move through an async ingest flow before they are used in retrieval.
Implementation capabilities
Designed to keep answers on-context and operations manageable
From configuration to runtime, Noha Bot focuses on practical rollout control: the right context, the right service boundary, and the right monitoring path.
Context stays aligned to each service
Every chatbot can hold its own prompt, model, and knowledge scope so different business flows do not leak into each other.
- Configuration per service
- Retrieved context is injected before answering
- Response language can be directed when needed
Knowledge ingestion is clear for operations teams
Documents are uploaded, queued into ingest jobs, and tracked with explicit status so teams know when a bot is truly ready.
- Single or batch upload support
- Track job status until completion
- Delete documents and refresh knowledge when needed
Integration surface is compact and practical
X-API-Key authentication, chat sessions, and realtime streaming make it easier to connect Noha Bot to websites, service portals, or chat experiences.
- Session create, list, history, and delete
- Regular chat and stream chat are separated
- Provider and model can be chosen per service
Rollout workflow
Three steps to move from knowledge setup to live conversations
The structure focuses on how teams actually launch a chatbot, not just on a scattered feature list.
Configure each chatbot for its business scope
Define the service, set the system prompt, choose the model, and align the answer style to the target use case.
Upload knowledge and wait for ingest completion
Send documents through the ingest pipeline, watch job status, and confirm the knowledge base is ready before live usage.
Launch conversations and observe operations
Create sessions, stream answers in realtime, review history, and adjust service config as the rollout expands.
Use cases
A fit for B2B flows that need consistent, grounded answers
These scenarios stay close to the actual product strengths: knowledge-based chat, tenant isolation, and session-level operations.
Customer support
Automate repetitive inbound questions, reduce pressure on support teams, and keep response quality steady during busy periods.
- Reduce repetitive tickets
- Shorten response time
- Keep answers consistent
Admissions or information intake
Standardize how teams answer questions about programs, fees, schedules, or intake requirements across multiple audiences.
- Standardize advisory flows
- Stay aligned to official documents
- Update content with less friction
Internal operations and policy lookup
Help internal teams find procedures, policies, and operational guidance without manual searching across scattered documents.
- Speed up internal lookup
- Reduce dependence on manual handoff
- Separate bots by service need
FAQ
Common questions before rollout
These answers help teams assess how Noha Bot fits before moving into rollout.
Can Noha Bot separate data by tenant and by chatbot?
Yes. Tenant boundaries isolate data at the platform level, and service keeps prompt, model, and knowledge scope separated for each chatbot.
Does the chatbot support realtime responses?
Yes. Noha Bot supports SSE streaming so responses can arrive in realtime instead of waiting for a full answer payload.
Can each chatbot have its own prompt and model?
Yes. Every service can hold a separate system prompt, provider, and model selection so teams can shape behavior per rollout.
Is a document immediately usable after upload?
Not yet. Documents go through an async ingest pipeline first. Once the job completes, the knowledge becomes available for retrieval during chat.
Ready for a product demo
Turn your existing knowledge into a more reliable chatbot experience
Start with a clear demo flow to validate how the chatbot learns from business knowledge, stays on-context, and prepares for a real rollout.
- Grounded in your business knowledge
- Keeps context by service
- Ready to grow into production rollout