Intuitra
An ML-driven customer engagement platform that does in one unified layer what most organisations run ten separate tools to achieve. RAG-ML chat is just the visible surface — underneath sits intent-driven orchestration, an authoring CMS and codeless workflows, Voice-of-Customer analytics, and a continuous learning loop that turns every interaction into the next improvement, all from a single source of truth.
The idea
Most “knowledge” tooling stops at a chatbot. Intuitra is a customer engagement platform: machine learning drives how content, intent and action come together, and the RAG-ML chat people see is only the most visible surface. Customers don’t think in channels — they think in tasks, from discovering a product through buying, onboarding, getting support and staying. Each of those stages usually carries its own knowledge, rarely connected. Intuitra connects them: it orchestrates the right knowledge, the right workflow and the right channel for each interaction, and lets teams author and automate all of it themselves rather than leaning on a bureau.
One platform, not ten tools
Most organisations stitch together a chatbot, a CMS, a workflow tool, product and policy data and an analytics stack — each answering in isolation. Intuitra does that work in a single engagement layer, so knowledge, workflows, product data, policies and analytics are connected rather than siloed. And it’s built to integrate, not to rip-and-replace: it orchestrates the CRM, IVR, chatbot, CMS and ticketing already in place — working alongside what you have, and replacing only what you’re ready to move on from.
The learning loop
The breadth isn’t in the feature list — it’s in the loop. Every interaction is built to improve the next one. Intuitra ingests knowledge from any source, engages customers in one consistent conversation, listens for friction and gaps in real time, learns what drives repeat contact, and then closes the gap itself — drafting candidate fixes and ready-to-deploy content for an editor to approve. Voice-of-Customer intelligence goes well beyond a static dashboard: it turns what customers actually say into the next content, workflow and process improvement, continuously.
What it does
The capabilities sit in layers, each building on a single source of truth.
Foundation
- Canonical entities — a single source of truth for organisational data: customers, products, policies and assets modelled once, so every surface acts on definitive data with full context — and performs better for it.
- Three-click knowledgebase — stand up one searchable, governed store in three clicks, ingesting and processing multiple existing sources of varying format at once — web, PDF, Word, CSV and Excel.
- Authoring CMS — your team creates and publishes articles, interactive guides, troubleshooters and checklists in-house, with automatic translation to expand coverage.
- Codeless workflow engine — build flows, capture forms, dispatch documents and call your own systems via API, no code required.
Engagement
- RAG-ML knowledge surface — natural-language answers for customers, agents and engineers, grounded in your content so they resolve rather than hallucinate.
- Next-best action — turns an answer into a resolution, triggering the right walkthrough, workflow, product page or troubleshooter for what the customer actually needs.
- Guided selling and an agent single-pane — pre-purchase guidance that heads off avoidable returns, with full case context, history and actions in one agent view.
Intelligence & resolution
- Auto-built intent taxonomy — every interaction classified from your own calls and transcripts; no taxonomy to hand-craft.
- Voice-of-Customer interrogation — free-form, natural-language questioning of interactions and agent responses, with gap analysis and resolution.
- Journey network and intent alerts — a visual map of where journeys break and cost accumulates, with volume spikes flagged before they hit the contact centre.
- Team and agent performance — conversations scored against scripts, compliance and outcomes, with real-time monitoring of risky behaviour.
Activation
- Signals and campaigns — real-time alerts on customer status and AI-built segmentation that targets the right action at scale.
The architecture
Intuitra is a multi-service .NET platform with clean separation between the core domain, infrastructure, an ingestion worker pipeline and a web API — feeding a set of portals for different audiences (agent, client, supplier, support). Setup is deterministic and idempotent: new tenants are provisioned through layered packs (core, locale, industry, demo, validation), each with health checks, so every client lands on a working, pre-configured tenant.
- ML orchestration engine
- RAG-ML chat surface
- Vectorised knowledge store
- Canonical entity model
- Intent analytics
- Codeless workflow engine
- Existing-stack orchestration
- Idempotent multi-tenant provisioning
- Omni-channel delivery
Why it’s here
Intuitra is the proof behind platform architecture: an ML orchestration core wrapped in clean service boundaries, idempotent multi-tenant provisioning and multi-channel delivery — designed to integrate with the stack a business already runs, and to get better with every interaction. AI here isn’t innovation theatre; each model does a specific job — resolving an intent, building a workflow from a prompt, closing a knowledge gap that couldn’t be closed before. Engineered to scale, built to be operated, with the analytics to prove it’s working.
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