One AI employee. Every conversation, workflow, and channel — handled.
AI Employee unites an agentic chatbot, no-code workflow automation, knowledge-graph retrieval, and a memory system that learns over time — on a multi-tenant core you can deploy in weeks, not quarters. It reads your documents, answers with citations, completes multi-step tasks, and hands off to your team only when it should.
Multi-agent orchestration with grounded answers
A chatbot that reasons, not just replies
At the core is a conversational engine that routes each request to the right specialist — answering a question, retrieving a document, or kicking off a workflow — using a ReAct reasoning loop and deterministic conversation state. Answers are grounded in your own content and cite the exact source, so every response is traceable and defensible. When confidence is high it resolves the request; when it isn't, it escalates with full context.
- Intent detection routes between conversational, retrieval, task, and workflow agents automatically
- Mandatory source citations (e.g. document section and date) make every answer auditable
- Confidence scoring decides when to resolve vs. escalate to a human
- Conversation state persists in Postgres for reliable, resumable threads
Multi-agent orchestration
A single orchestrator detects intent and routes to the right specialized agent, so one assistant covers Q&A, retrieval, and transactional tasks.
Cited, grounded answers
Every response points back to the exact source section. No invented facts — answers you can stand behind in disputes and audits.
Model flexibility
A LiteLLM router supports Claude, OpenAI, and local models per tenant, with token tracking and seamless provider switching.
Agentic, no-code workflow automation
From answering questions to completing tasks
Most chatbots stop at Q&A. AI Employee runs multi-step business processes end to end — book, confirm, pay, remind — using a LangGraph state engine with conditional branching, pause/resume checkpointing, and human-in-the-loop interrupts. Build flows with YAML definitions and a visual editor across seven step types, triggered by intent, schedule, webhook, or manually. No engineering bottleneck to ship a new automation.
- Seven step types: HTTP, AI processing, user input, condition, transform, delay, and notification
- Human-in-the-loop interrupts pause a workflow for approval, then resume exactly where it left off
- Postgres checkpointing means long-running flows survive restarts and reconnects
- Pre-built templates for scheduling, billing, document submission, and lead capture
No-code builder
Compose multi-step automations in YAML or a visual editor — ops teams ship new flows without waiting on developers.
Human-in-the-loop
Insert approval checkpoints anywhere. The agent pauses, asks, and continues, so high-stakes steps always get a human sign-off.
Reliable execution
Conditional branching plus Postgres-backed checkpoints make even long, multi-API workflows pause, resume, and complete cleanly.
Hybrid RAG + knowledge graph + KAG retrieval
Knowledge that reasons across your documents
AI Employee ingests PDFs, DOCX, spreadsheets, text, and entire websites — with OCR for scanned documents and section-aware chunking that preserves structure. Retrieval blends vector search, an entity knowledge graph, and knowledge-augmented graph reasoning, with reranking and configurable fusion. The result: multi-hop, reasoning-capable answers that keyword or vector-only search miss, plus a quality dashboard that shows exactly which content is pulling weight and where coverage gaps remain.
- Ingest PDF, DOCX, XLSX, TXT, and crawled web pages — including linked PDFs and scanned 1990s-era documents via OCR
- Section-aware chunking preserves legal and document structure for precise, accurate citations
- Hybrid retrieval combines vector search, entity graph traversal, and KAG reasoning with neural reranking
- Knowledge-quality metrics surface most- and least-used content, low-score queries, and coverage gaps
Hybrid retrieval
Vector search plus a knowledge graph plus KAG reasoning deliver multi-hop answers and fewer hallucinations than search alone.
Any document, any quality
OCR and pluggable preprocessors handle scanned and unstructured files. Section-aware chunking keeps citations exact.
Knowledge quality dashboard
See retrieval score distribution, document coverage, and queries returning nothing — so you know what content to add next.
A four-tier memory system that learns per tenant
An assistant that remembers and improves
Stateless chatbots forget everything between conversations. AI Employee runs a four-tier memory architecture: conversation summaries for long-thread continuity, semantic facts recalled across sessions, episodic examples of past tool sequences for in-context learning, and procedural instructions auto-generated by weekly self-reflection and written back into the system prompt. Every tier is scoped per tenant and user, so each customer's agent gets smarter on its own — no retraining required.
- Conversation summaries keep 20+ message threads coherent without re-entering context
- Semantic memory recalls facts and preferences across separate sessions
- Episodic memory reuses successful past tool sequences as few-shot examples
- Weekly procedural reflection writes tenant-specific instructions back into the agent's behavior
Cross-session recall
The agent remembers facts, preferences, and prior context across conversations instead of resetting on every message.
Self-improving behavior
Weekly reflection turns observed patterns into tenant-specific instructions — agents improve automatically, without retraining.
Per-tenant isolation
Memory is scoped by tenant, user, and agent. What one customer's assistant learns never leaks to another.
Embeddable web widget, plus voice and phone
Meet customers on every channel
Deploy the chat experience with a single script tag — a lightweight, shadow-DOM-isolated widget with theme presets, full CSS control, proactive engagement triggers, consent gating, and multi-language support. Add real-time voice via Gemini Live and inbound or outbound phone via Twilio, with tuned pacing and persona templates for callers who prefer to talk. Voice agents share the same knowledge, memory, and workflows as text — one brain, every channel.
- One-script-tag widget with theme presets, proactive triggers, dark/light mode, and consent controls
- Real-time voice via Gemini Live and inbound/outbound phone via Twilio
- Voice persona configuration: pacing, vocabulary level, and repeat-back confirmation per tenant
- Voice and chat agents share identical knowledge, memory, and workflow access
Embeddable widget
Drop one script tag on any site. Shadow-DOM isolation, theme customization, and proactive triggers — live in minutes.
Voice and phone
Real-time voice and inbound/outbound calling reach demographics who prefer to talk, with the same cited answers as chat.
Consistent everywhere
Every channel runs on one engine, so knowledge, memory, and workflows stay identical across web, voice, and phone.
Connectors, integration SDK, and true multi-tenancy
Connects to your systems. Isolates your data.
AI Employee sits on top of your existing systems of record rather than replacing them. A standardized integration adapter handles auth, entity mapping, webhooks, and health checks — proven with LearnSuite and Vantaca, extensible via MCP connectors to CRMs, scheduling, and billing. Underneath is a true multi-tenant core with row-level security: a parent organization can manage many sub-tenants, each with isolated documents, vectors, configuration, and branding, and cross-tenant access blocked by default.
- Integration adapter SDK standardizes auth, entity mapping, webhook handling, and sync — with LearnSuite and Vantaca as reference builds
- MCP connector framework extends to external CRMs, scheduling, and billing systems
- Parent + sub-tenant hierarchy: one organization, many isolated communities or locations under a single sales motion
- Postgres row-level security, per-tenant vector namespaces, and scoped cache keys prevent cross-tenant data leakage
Integration SDK & connectors
A reusable adapter base plus MCP connectors bridge your system of record for authenticated self-service — read live data, never replace your stack.
Multi-tenant hierarchy
One parent account manages many sub-tenants, each with isolated docs, branding, and config. One deal unlocks an entire portfolio.
Hardened isolation
Row-level security, per-tenant vector namespaces, and scoped caches keep every tenant's data strictly separate.
Analytics, audit logging, and a human-in-the-loop inbox
Visibility, control, and a human safety net
A 12-page admin console gives operators cross-tenant visibility: conversation analytics, response-quality metrics, satisfaction tracking, and topic trends. The north-star deflection metric shows the share of inquiries resolved without staff — a direct line to ROI. When the AI can't resolve something or detects intent to escalate, the agent inbox routes the conversation to a person with full context, wait-time and resolution tracking, and a clean accept-resolve workflow. Compliance-grade audit logging records every sensitive access with actor, reason, and timestamp.
- Deflection-rate analytics quantify the percentage of inquiries resolved without human involvement
- Response-quality dashboards track knowledge coverage, confidence rates, and average response time
- Agent inbox queues handoffs with full context, wait-time and resolution metrics, and assignment workflow
- Audit logging plus consent, right-to-delete, and right-to-export endpoints support compliance requirements
Outcome analytics
Track deflection rate, response quality, satisfaction, and topic trends across every tenant from one console.
Human-in-the-loop inbox
Unresolved or high-intent conversations route to a person with full context, wait-time tracking, and a clear resolution flow.
Compliance & audit
Every sensitive access is logged with actor and reason. Consent gating plus delete/export endpoints keep you audit-ready.
See AI Employee work on your own content
Point it at your documents or website and watch it answer with citations, complete workflows, and learn over time — across HOA management, tutoring centers, and any vertical you bring it.