Service
From AI possibilities to systems that actually work.
AI agents can plan, reason, and execute — but that's not the same as an enterprise AI system. Keleno connects intelligence to your data, applications, and workflows to turn AI potential into measurable outcomes.
The problem
AI is easy to demonstrate.
Production AI is engineering.
A chatbot can answer. A production system must operate safely, reliably and usefully inside the business.
The difference is the system around the model.From demo to production
An AI system has to do more than respond.
Context / Tools / Controls / Evaluation / Feedback
What production readiness looks like
Not a longer checklist. A set of engineering capabilities designed into the system from the start.
Business context
Work, users and intent.
Enterprise data
Authorized, grounded data.
Integration
Tools and workflows.
Human control
Escalation when needed.
Governance
Security and policy.
Keleno view
The difference between an AI demo and an AI system is engineering.
What Keleno does
We engineer intelligence into the way your business works.
Keleno combines software engineering, AI engineering, and business-process thinking to build Generative AI and agentic systems tailored to your organization.
The traditional,
tech-first approach
“Which AI model should we use?”
The Keleno, outcome-first approach
“What business outcome are we trying to improve?”
Models
Curated model selection & optimization.
Data
Structured & secure data management.
Workflows
Process design & orchestration.
Agents
Goal-oriented digital collaborators.
Tools
Bespoke internal tools & frameworks.
Integrations
Connecting existing enterprise systems.
Human controls
Oversight & control panels.
Then we determine the right combination.
What we build
Six ways we put AI to work inside your business
Turn your enterprise knowledge into intelligent applications.
Generative AI Applications
We build AI-powered applications that understand your business context and help people find, create, analyze and act on information.
Examples
Move from AI that answers to AI that acts.
AI Agents
We design AI agents that can reason through tasks, use tools, interact with enterprise systems and execute multi-step workflows.
Examples
The goal isn't autonomous AI for its own sake.
It's useful autonomy within clearly defined boundaries.
Complex work rarely belongs to one agent.
Multi-Agent Systems
Some business processes require multiple capabilities working together. Keleno can design agent teams where specialized agents collaborate under an orchestration layer.
For example
Sales Research Agent
Company Intelligence Agent
Solution Mapping Agent
Proposal Agent
Human Approval
CRM Agent
This allows complex processes to be decomposed into manageable, observable and governable tasks.
Give AI access to the knowledge your business actually owns.
RAG & Enterprise Knowledge Systems
Your most valuable information is often buried inside:
We build knowledge systems that retrieve the right information at the right time and ground AI responses in your enterprise context.
The objective isn't simply “Ask your documents a question.”
It's making organizational knowledge usable at the moment a decision needs to be made.
Automate work across systems, not just within them.
Intelligent Workflow Automation
Agentic automation understands context, applies policy and completes work across the business systems you already use.
A customer request, executed end to end
A typical intelligent workflow combines understanding, governed decision-making and system action.
Customer request
Understand intent
Classify & route
Retrieve context
History & policy
Determine action
Eligibility & rules
Human approval
When required
Execute & update
Systems & CRM
Close the loop
Notify customer
Key message
From workflow automation to intelligent workflow execution.
Make your existing software AI-ready.
AI Modernization & Integration
AI doesn't have to replace your existing systems. We integrate intelligence into the technology you already use.
We can add AI capabilities to existing applications or build new AI-native experiences around them.
How Keleno decides
Not every problem needs an AI agent.
We don't force AI into your business. We engineer the right solution — sometimes a GenAI application, sometimes RAG, an AI copilot, a workflow automation, a single agent, or a multi-agent system.
No one-size-fits-all stack.
Only the architecture that earns its complexity.
Built for production
Decision framework
Find the right architecture
Five questions. One system design.
Recommended architecture
GenAI Application
Generate, summarize and answer from your content.
Our engineering approach
Built for production. Not just for the demo.
Keleno's AI-native development cycle
The AI-native development cycle: intent, context, plan, generate, validate, evaluate, deploy, observe, learn — repeat.
The technology layer
Model-agnostic. Architecture-first.
The model is a component. The system is the product. We work with the appropriate combination of:
Frontier Models
OpenAI • Anthropic • Google • other leading models
Open Models
Llama • Mistral • Gemma and others
Knowledge
RAG • Vector Search • Knowledge Graphs • Enterprise Data
Agentic Frameworks
CrewAI • LangGraph • other orchestration technologies
Integration
APIs • MCP • Enterprise Applications • Databases
Infrastructure
AWS • Azure • Google Cloud • Private/Hybrid environments
Keleno
System Framework(the product)
We choose technology based on the problem — not the other way around.
Security and governance
Autonomy needs boundaries.
The more capable AI becomes, the more important engineering discipline becomes. Keleno builds appropriate controls around AI systems, including:
Data protection
Keep sensitive business information appropriately protected.
Access control
Agents only access the systems and information they are authorized to use.
Human-in-the-loop
Critical decisions can require human approval.
Guardrails
Define what the system can and cannot do.
Evaluation
Test AI behaviour against defined scenarios before and after deployment.
Observability
Understand what the system is doing and where it is failing.
Auditability
Maintain appropriate records of important AI interactions and actions.
- Data
- Tools
- Policies
- Humans
- Applications
Governed autonomy
Business outcomes
Productivity
How much manual work disappeared?
Speed
How much faster does the process run?
Quality
Are decisions and outputs more accurate?
Experience
Are customers and employees getting better experiences?
Cost
What does each completed task actually cost?
Revenue
Can AI help create new revenue or improve conversion?
Autonomy
How much of the workflow can be completed without intervention?
This is particularly important because current enterprise AI discussions are increasingly focused on moving beyond pilots toward measurable production outcomes, rather than simply deploying models.
— Capgemini
Use cases
Your AI workforce in action: an interactive department viewer
See how the same agentic pattern adapts to different teams. Pick a department.
Overview
Key responsibilities
Your AI workforce
The future isn't humans versus AI.
Don't think of an AI agent as a chatbot. Think of it as a digital worker with a defined role, tools, permissions and measurable responsibilities. For example:
| Agent role | Primary actions | Tools used | Impacted metrics |
|---|---|---|---|
| Research Agent | Finds and synthesizes information | Search & retrieval, knowledge base | QualitySpeed |
| Sales Agent | Researches prospects and executes outreach | CRM, outreach tools | ProductivityRevenue |
| Support Agent | Resolves customer requests | Ticketing, knowledge base | ExperienceSpeed |
| Finance Agent | Processes and reconciles transactions | ERP, document extraction | CostQuality |
| Operations Agent | Monitors and coordinates operational workflows | Monitoring systems, workflow tools | ProductivityAutonomy |
| Knowledge Agent | Finds answers across enterprise knowledge | RAG, enterprise search | SpeedQuality |
| IT Agent | Investigates and resolves routine incidents | Ticketing, diagnostics | SpeedAutonomy |
It's people working with intelligent systems designed around the way they work.
Why Keleno
AI creates value when it works in the real world.
We connect AI to the data, systems and controls where your business already runs.
Keleno principle
Build for the work. Measure the outcome.
Not more AI.
Better systems.
What sets us apart
Engineering choices that turn promising AI into dependable business capability.
01
Start with the business
We begin with your work before selecting the model.
Business-first engineering
02
Build the whole system
Connect AI with enterprise data, software and operations.
AI + software engineering
03
Apply autonomy deliberately
Automate where it helps. Keep people in control where it matters.
Practical autonomy
04
Operate for the long term
Design evaluation, security and observability in from day one.
Production mindset / outcome focused
Our proven work
Production agentic AI case studies
Agentic Data Routing — MedSyncAI
- Built a data exchange layer with an autonomous routing agent.
- Users configure their preferred ingestion sources; the agent handles downstream distribution logic.
- Reduced manual configuration and routing errors.
- Prioritized correctness and compliance in a domain where both matter.
Guest-Facing Action Agent — Vividity, for Hubloft
- Built a conversational agent that guests interact with directly.
- Guests make requests for:
- Room changes
- Service bookings
- The agent executes those actions in the connected PMS in real time.
- No staff intermediary required.
A note on proof
Real, delivered engineering
The agentic work above is real, delivered engineering. We're still publishing named case studies for our applied ML, generative AI, and infrastructure work — rather than show you a borrowed logo or an invented metric, we'd rather walk you through a relevant engagement directly. Ask us for specifics.