IntentAI – Detect Intent. Deploy the Right Agent.

How it works

From signal to action in seconds

Three steps. Zero human routing. The right agent, every time.

Step 01

Detect

Capture customer signals across every channel — chat, email, behaviour, voice — and surface intent the moment it emerges.

See it in action →

Step 02

Route

Our intent engine classifies the signal and selects the optimal specialised AI agent — matched to context, urgency, and desired outcome.

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Step 03

Activate

The right agent acts instantly — resolving, qualifying, onboarding, or retaining — autonomously, without waiting for a human handoff.

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Under the Hood

Built for production-grade intelligence

Four stages. Sub-200ms. No manual rules.

Observe

Any channel. Any format. Real-time ingestion.

Classify

Transformer inference across 50+ intent types.

Route

Policy engine selects the optimal agent path.

Execute

Agent acts. Outcome delivered. Loop closed.

< 200msend-to-end latency

50+intent types

97%routing accuracy

Pluggableagent SDK

Signal Layer

Universal Ingestion

Webhooks, SDKs, and native connectors across chat, email, voice, and API.

Classification

Intent Engine

Transformer-based model with confidence scoring and fallback handling.

Routing

Policy Orchestrator

Hybrid rules + ML routing with priority queues and SLA enforcement.

Execution

Agent Runtime

Stateful agent execution with tool calls, memory, and audit logging.

Use Cases & Capabilities

Every intent, covered

Across the full customer journey, IntentAI deploys the agent that fits the moment.

Support resolution

Detect help-seeking intent and route to a support agent that resolves without escalation.

Explore workflow →

Purchase intent

Spot buying signals early and activate a sales agent to guide the customer to conversion.

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Onboarding

Identify new-user confusion and deploy a guided onboarding agent before drop-off happens.

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Churn prevention

Detect disengagement signals and trigger a retention agent to re-engage at the right moment.

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Lead qualification

Surface high-intent prospects instantly and route them to a qualification agent before interest fades.

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Internal workflows

Route employee requests — IT, HR, ops — to the right internal agent without manual triage.

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Multi-Agent Orchestration

Agents that talk to agents

Agentic workflows powered by agent-to-agent (A2A) communication across a dynamic agent mesh — no human in the loop.

Multi-Agent Orchestration Agentic Workflows A2A Protocol Agent Mesh

Example chains

🔴 Billing dispute detected

Orchestrator → Billing Agent → Fraud Agent → Policy Agent → refund approved

🟢 Upgrade intent detected

Orchestrator → Sales Agent → Billing Agent → plan switched, email sent

🟡 New user confusion detected

Orchestrator → Onboard Agent → Support Agent → guided walkthrough delivered

🔵 Churn risk detected

Orchestrator → Support Agent → Sales Agent → retention offer applied

Parallel Execution

Multiple agents work simultaneously on different sub-tasks and merge results.

Feedback Loops

Agents query each other, validate outputs, and re-route if confidence drops.

Shared Memory

All agents in a chain share context so no information is lost across handoffs.

Live Demo

See IntentAI in action

Live demos of the agents IntentAI detects intent for and routes to. Real outputs, real AI.

intentai · live routing demo

Customer Message

Paste any inbound customer message and watch the engine classify intent and route it to the right agent.

My card was charged twice

I want to upgrade to premium

How do I export my data?

My order never arrived

I need to cancel my account

▸ Analysis complete

IntentAI routing engine · Powered by Claude Haiku

Product Problem

Describe a product challenge or opportunity. The copilot returns user stories, success metrics, and a prioritized roadmap.

65% checkout drop-off at payment step

Users aren't adopting our mobile app

Onboarding takes 3 days, competitors do it in 1

We're losing enterprise deals due to missing SSO

▸ PM brief generated

IntentAI routing engine · Powered by Claude Haiku

Product Idea

Describe a product idea or feature concept. Get structured PM feedback: strengths, risks, market fit, and a verdict.

AI that summarizes your Slack daily

Social app for dog owners to arrange playdates

Subscription box for indie board games

B2B tool to auto-generate sales proposals from CRM data

▸ Critique complete

IntentAI routing engine · Powered by Claude Haiku

Interactive Deep Dive

Agent Architecture

Pick a scenario and run the simulation. Watch how each architectural layer processes the signal in real time.

Select a scenario

Billing

"I was charged twice — need a refund"

Churn

"This product isn't working for us anymore"

Upgrade

"What's included in your enterprise plan?"

Support

"The export feature is broken — getting 500 errors"

Perception

—

Memory

—

Reasoning

—

Planning

—

Action

—

Trace log

Run a simulation to see the agent trace...

▸ Agent output

Architecture reference — click any component

Perception

Input processing and signal extraction

Memory

State, context, and retrieval

Reasoning

LLM core and thinking chain

Planning

Task decomposition and sequencing

Action

Tool calls and real-world execution

Perception Layer

Transforms raw inputs into normalized representations the agent can reason over. Includes tokenization, embedding generation, entity extraction, and signal classification. Most agent failures start here.

"Garbage in, garbage out. Pre-process everything before it hits the LLM."

Key Technologies

EmbeddingsTokenizationNERChunking

# Perception pipeline input = normalize(raw_message) embedding = model.embed(input) signals = extract_entities(input) context = build_context(embedding, signals)

Memory System

Short-term memory lives in the context window. Long-term memory lives in vector stores, retrieved semantically on demand. The design decision is knowing what to keep, what to summarize, and what to discard.

"What an agent remembers determines what it can do. Design memory before prompts."

Key Technologies

Vector DBRAGSliding Windowpgvector

# Memory retrieval query_vec = embed(user_message) memories = vector_db.search(query_vec, k=5) context = [summary] + memories + [message]

Reasoning Core

Production agents use structured thinking: Chain-of-Thought forces step-by-step reasoning, ReAct interleaves reasoning with tool use, and self-critique loops catch errors before they become outputs.

"Structure the LLM's thinking. Don't leave reasoning to chance."

Key Technologies

Chain-of-ThoughtReActSelf-critiqueClaude

# ReAct reasoning loop while not done: thought = llm.think(context) action = llm.decide(thought) result = tools.run(action) context = update(context, result)

Planning Engine

Complex tasks can't be solved in one LLM call. Planning breaks goals into ordered sub-tasks, resolves dependencies, handles parallel execution, and manages retries. Good planning separates demo agents from production agents.

"Plan first. Act second. Validate always."

Key Technologies

Task graphsDAG executionRetry logicLangGraph

# Task decomposition plan = planner.decompose(goal) graph = build_dag(plan.steps) for step in topological_sort(graph): result = execute_with_retry(step)

Action Layer

Actions are how agents affect the world — API calls, database writes, emails, tickets. Every action should be observable (logged), typed (structured output), and where possible, reversible. Tool definitions are the API contract between LLM and your systems.

"Every action should be observable, typed, and reversible."

Key Technologies

Tool callingJSON schemaAudit logsIdempotency

# Tool definition tool = { "name": "refund_charge", "idempotency_key": True, "audit_log": True, }

Detect Intent.Deploy the Right Agent.

From signal to action in seconds

Detect

Route

Activate

Built for production-grade intelligence

Every intent, covered

Support resolution

Purchase intent

Onboarding

Churn prevention

Lead qualification

Internal workflows

Agents that talk to agents

See IntentAI in action

Agent Architecture

Get early access

Request a demo

Detect Intent.
Deploy the Right Agent.