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Solution — New

Intelligent Process
Automation

We combine Agentic AI with Knowledge, Context, and Grounded Generative AI to deliver enterprise automation solutions — executed with speed, accuracy, and governance.

What It Is
An orchestrated combination of Agentic AI and RAG-based knowledge retrieval for end-to-end enterprise automation.
Why Now
Enterprises are moving toward hyper-automation — and GenAI alone isn't enough. IPA bridges the gap.
Key Technology
Agentic orchestration, RAG pipelines, CRAG, GraphRAG, and enterprise-grade guardrails.
Why IPA Now

Automation is no longer about scripting rules — it is about deploying intelligence that reasons, retrieves, and acts.

The Drivers

Why enterprises are
moving to IPA now

Automation and robotics
Driver 01
The Hyper-Automation Imperative

Modern enterprises are moving toward automating Business & IT processes using an orchestrated mix of AI, RPA, BPM, and integration. IPA sits at the centre of this shift — combining all these capabilities into a single, coherent intelligence layer.

Team collaboration
Driver 02
Free Up Your Best People

Dedicate valuable human resources to complex, high-judgment activities while reaping productivity gains and cost reductions from end-to-end automation. IPA doesn't replace people — it elevates what they can do.

AI knowledge retrieval
Driver 03
RAG Solves GenAI's Core Limit

LLMs alone cannot reliably answer from private or changing enterprise knowledge. Retrieval Augmented Generation addresses this by retrieving trusted context at query time — grounding every AI response in your actual enterprise data.

How It Works

Two pillars of
enterprise intelligence

AI agent orchestration
Pillar 01
Agentic AI Orchestration

AI agents combine foundation models with reasoning, planning, memory, and tool use to achieve goals in real workflows. Our orchestration layer interprets intent, selects tools and knowledge, and executes multi-step plans with enterprise-grade guardrails — autonomously.

Knowledge retrieval system
Pillar 02
Knowledge & Context AI (RAG)

We implement modern RAG pipelines combining parametric model knowledge with non-parametric retrieval — chunking and embeddings, vector and hybrid retrieval, grounded generation. We use specialised architectures including CRAG, Agentic RAG, and GraphRAG based on your requirements.

Start the Conversation

Automate your enterprise
workflows with AI

We evaluate your current workflows, identify optimisation opportunities, and execute with speed and governance.