Agentic AI Multi Agent Solutions
From agentic AI experiments to measurable EBIT impact
One clever agent makes a good demo. Real work runs across teams, systems and approvals, and that takes a team of agents that share context, hand work to each other and answer to the same rules. We design those systems around your business problem and take them all the way to production.
Agentic AI Multi Agent Enterprise AI Solutions
Built to Scale. Designed to Pay Back.
Our end-to-end solution approach to help organisations move beyond Agentic AI pilots and scale AI across the enterprise. We embed innovative Agentic AI Multi Agent Systems into how your business actually works i.e. into workflows, redesigning critical functions to achieve real efficiency, growth, and EBIT outcomes without the bloat.
We provide end-to-end AI implementation services including use case discovery, proof-of-concept development, production deployment, and ongoing optimization. We cover the complete Enterprise AI lifecycle from strategy and design through build, deployment, and scale across your enterprise.
Partnering Across Every Stage of Your AI Maturity & Adoption Stage. From exploration to enterprise-wide deployment, helping validate ideas, launch pilots, scale solutions, and build AI-native systems that deliver measurable and sustainable business value.
The short answer
It’s a team of specialised AI agents working on one job together. One reads the documents, another checks the rules, another updates the ERP, and an orchestrator keeps them in step. Each agent does one thing well, so the whole system is faster, easier to govern and far more reliable than one agent trying to do everything.
Our starting point
How we build
1
Multi agent orchestration across functions
Agents work across departments, not inside one tool. Finance, supply chain, marketing and service agents share context and pass work to each other through governed agentic workflows.
Orchestrator
Finance
Supply chain
Marketing
Service
2
Model agnostic, with a decision layer
Our enterprise AI agent platform picks the right LLM for each task. Light tasks go to lean models and hard reasoning goes to frontier models. Prompts and outputs are optimised on the way, so token costs fall and quality holds.
User task
Decision layer
Lean LLM
routine
Mid LLM
standard
Frontier LLM
complex
3
Agent Communication Protocol, built in
ACP is native to the platform. Agents from different teams, vendors and systems talk through one standard protocol, so new agents plug in without custom integration and every exchange is logged.
Your agent
ACP
Partner agent
INTEROPERABLE
LOGGED
GOVERNED
How we help you scale and adopt AI
AI is no longer the future, it’s the engine driving today’s most successful organisations. By mastering the three intervention pivots, automation, augmentation and innovation, your organisation can move beyond pilot projects and unlock real, measurable impact across your business.
PIVOT 01
Automate
Make AI part of the work
Most enterprise AI initiatives fail because they live outside real workflows. This pivot embeds intelligence directly into the systems and tools your teams already use, so AI becomes invisible, intuitive and unavoidable. Adoption accelerates naturally, productivity compounds, and value shows up from day one. AI stops being an experiment and starts becoming habit.
PIVOT 02
Augment
Re-engineer outcomes
Here agents don’t just speed up a task. They change the result. An analyst who used to review a sample now reviews every contract. A buyer sees every supplier price in the market, not three quotes. People keep the judgment calls, and agents give them more to work with and more time to use it.
PIVOT 03
Innovate
Integrate AI native solutions
Some things only become possible once agents exist. New services, new revenue lines, new ways to serve customers at a cost that used to be impossible. This pivot is where we build those AI native solutions, often on top of the workflows the first two pivots already modernised.
AI native by design
AI should be part of the fabric of the business, not a layer on top. Our solutions understand your context and workflows, connect to your enterprise systems, and run multi step processes on their own, so your teams spend their time where it matters.
Agentic AI multi agent systems
Orchestration, language understanding, automation and human review combined, for customer service, manufacturing, supply chain and operations.
Agentic workflows
Autonomous agents that run your business processes from trigger to result, with people in the loop where it counts.
Conversational AI
Chat and voice assistants that cut service costs and give customers personal, accurate answers in their own language.
RAG and RAG as a service
Retrieval augmented generation that grounds every answer in your own live data, delivered as a build or as a managed service.
Advanced LLM integration
Leading language models tuned to your region, culture, languages and internal rules.
Multi model solutions
Text, image and voice in one application, each using the model that suits it best.
Business AI modeling
We map AI opportunities across your customer lifecycle and keep the scope practical.
Frictionless experience design
Answer engine optimisation, one to one personalised journeys and checkouts that need almost no clicks.
Rapid prototyping
Working prototypes in weeks, refined with real users before anyone commits to a full build.
Already in production
Not every problem needs a build from scratch. These products are multi agent systems we’ve already built and deployed. We tailor them to your data and workflows, which gets you to production faster.
Cortex
Enterprise AI agent platform
The orchestration, decision layer and ACP behind every system on this page.
Octimo
Autonomous customer service
Agents that listen, resolve and learn across voice, chat and email.
EXTRCT
Unstructured data intelligence
Agents that read, reason over and act on documents, emails and reports.
Kairix
Agentic pricing intelligence
Agents that watch competitor prices and recommend yours in real time.
Agentic AI x ERP
Agents beside your ERP
An agent layer that works with SAP, Dynamics and other ERPs without replacing them.
Aug11
Agentic commerce
Conversational shopping and agent ready retail.
By industry
Manufacturing
Predictive maintenance, autonomous scheduling and supply chain orchestration.
Start with one production line, from equipment alert to repair order.
Retail
Inventory, dynamic pricing, personal shopping agents and merchandising.
Start with demand forecasting that places restock orders on its own.
Healthcare
Revenue cycle, prior authorisations, clinical decision support and patient monitoring.
Start with claims processing agents, with privacy controls in place.
BFSI
Fraud detection, underwriting, compliance checks and claims.
Start with KYC and onboarding agents, end to end.
Proof
Uplift in eCommerce sales from real time competitor pricing agents
Leading home appliances company
Customer interactions a year across voice, chat and email, 85% resolved the same day
One of the largest banks in the US
Higher extended warranty conversions by reading unstructured data alongside ERP data
Automotive insurer, GCC region
What happens next
AI governance and EU AI Act
Controls and audit trails for every agent.
AI strategy and roadmap
Decide which systems to build first.
Legacy tech modernisation
Give agents the data and systems they need.
C-suite solutions
See the agents we build for your office.
Agentic AI and multi agent systems, answered
What is agentic AI and how is it different from traditional AI?
Agentic AI works toward a goal on its own. It plans, uses tools and systems, checks its work and completes multi step tasks with little human input. Traditional AI is reactive. It does one narrow task, like classifying or generating text, only when asked.
What is a multi agent system?
A multi agent system is a team of specialised AI agents that work together on one workflow. Each agent handles one part, such as reading documents, checking rules or taking action in a system. An orchestrator assigns the work, shares context and passes results between them.
What is the difference between single agent and multi agent architectures?
A single agent does everything itself. It is simpler to build but slows down and makes more mistakes on long, complex workflows. A multi agent setup splits the work between specialists that run in parallel and check each other, which scales better across departments.
What is model agnostic AI?
Model agnostic means the platform is not tied to one AI model. A decision layer sends each task to the model that fits it. Routine work goes to small, cheap models and hard reasoning goes to frontier models, so you control both cost and quality.
What is the Agent Communication Protocol?
The Agent Communication Protocol, or ACP, is a standard way for AI agents to talk to each other. With ACP built into the platform, agents from different teams, vendors and systems exchange work without custom integration, and every exchange is logged and governed.
What is Model Context Protocol and why does it matter?
Model Context Protocol, or MCP, is an open standard introduced by Anthropic in 2024. It gives AI agents one consistent way to connect to tools, data and APIs. Fewer custom connectors means faster builds, fresher data and fewer made up answers.
What is the Validation First Framework?
It is how Agentics builds. We prove a use case on your own data before you commit a large budget, then keep validating data quality, accuracy, compliance and bias at every stage. Problems get caught early, when they are cheap to fix.
Which frameworks do you use to build multi agent systems?
We pick the framework to fit the job. LangGraph for controlled, production workflows. CrewAI for role based agent teams. Microsoft’s agent stack, including AutoGen and Semantic Kernel, where clients run on Azure. Everything runs and is governed on our Cortex platform.
Bring us your business problem
Not a technology wish list. The problem that costs you the most time or money today. We’ll tell you honestly whether a multi agent system is the answer, and what it would take to build.
