Application example · Operations
Operations Copilot
Supports operational tasks, consults tools and executes defined processes while preserving traceability and human escalation when needed.
Toolsactions
Workflowsprocesses
Controloversight
CUSTOM AI SYSTEMS FOR BUSINESSES
AI that knows your company, uses your tools and executes real work.
We design and implement AI assistants connected to your data, processes and tools. We integrate MCP, tools and skills, build the user experience and control dashboard, and optimize operations so AI is useful, measurable and sustainable.
≈ 3 monthsapproximate implementation cycle
THE TEAM BEHIND ARKEA
Real people behind every implementation.
Strategy, AI engineering, automation, product and user experience working as one team to build around your company.
How we work
WE INTEGRATE LEADING AI MODELS
OpenAI
Anthropic
Google
xAI
DeepSeek
Meta
Runway
Mistral AI
OpenRouter
Supabase
n8nCAPABILITIES
Everything needed to take AI from idea to production.
01
Diagnosis and architecture
We analyze processes, data, tools and opportunities to define where AI should enter and how the system should be built.
02
Custom AI assistants
We build assistants adapted to your company’s knowledge, context, rules and way of working.
03
MCP & integrations
We connect AI with existing systems, data and services so it can work inside your infrastructure.
04
Tools & actions
We create tools that let the assistant retrieve information, use services and execute real actions.
05
Skills & automation
We develop reusable capabilities and workflows for recurring tasks.
06
UX & interface
We design how your team interacts with AI so the system is intuitive, fast and genuinely adopted.
07
Dashboard & oversight
We create a control layer to observe activity, flows, behavior and system evolution.
08
Optimization & maintenance
We optimize architecture and token usage to control operating costs and maintain the system after launch.
WHY ARKEA
The difference is not using AI. It is how we integrate it.
Built for your operations, not for a demo.
Before development, we understand how your company works, where context lives and what outcome the system must produce.
Able to act, not just answer.
MCP, tools, integrations and skills let AI use your tools and participate in real processes.
Designed to operate after launch.
We plan oversight, user experience, maintenance and usage control from the start so the system can evolve.
IMPLEMENTATION
From your processes to an AI system in production.
We structure each implementation around an approximately three-month cycle to understand, design, build, validate and put the system into operation.
Step 0101
Diagnosis
Processes, tools, data, context and opportunities.
We identify where AI can create real value, what information it needs and which parts of the process should not be automated.
Step 0202
Architecture
Assistant, integrations, tools, skills and control criteria.
We design how the system will work before building: sources, permissions, connections, actions and user experience.
Step 0303
Build
Development, automation, interface and dashboard.
We turn the architecture into a functional implementation connected to the required components.
Step 0404
Validation
Testing, behavior, usage and user experience.
We test real scenarios, tune behavior, review friction and verify that the system works as expected.
Step 0505
Production & optimization
Launch, oversight and evolution.
We put the system into operation and prepare the next stage of maintenance and continuous improvement.
USE CASES
One AI architecture can support different parts of a business.
Application example · Knowledge
Knowledge Assistant
Consults documentation and internal sources using company-specific context while respecting configured sources and permissions.
RAGcontext
Sourcesinformation
Permissionsaccess
Application example · Commercial
Sales Copilot
Retrieves CRM context, prepares information and supports sales-process tasks without fragmenting work across tools.
CRMcontext
Toolsactions
Automationfollow-up
Application example · Leadership
AI Control Center
Centralizes activity, usage, behavior and system signals to maintain a shared operational view.
Dashboardvisibility
Logstraceability
Usageconsumption
WHEN IT MAKES SENSE
Three signs your company is ready to implement AI.
01
There is repetitive work that still needs context.
When a task repeats, consumes hours and depends on scattered information, there is a real opportunity for assistance or automation.
02
People spend time searching before they can act.
Documents, CRM, tools and internal knowledge can become accessible context for a connected system.
03
Your team already uses AI, but everyone works separately.
When AI exists without integration, control or a shared way of working, the next step is not another tool: it is infrastructure.
01 · UNDERSTAND
AI that knows your company context.
We connect information, documentation and internal sources so the system works with the knowledge each task requires.
02 · ACT
Give it tools. Not just answers.
With MCP, tools, skills and integrations, the assistant can move from conversation to consulting systems and executing actions inside real processes.
03 · CONTROL
AI oversight is part of the system.
We design dashboards and observation mechanisms to understand what the system does, how it is used and how its consumption evolves.
FREQUENTLY ASKED QUESTIONS
What you need to know before implementing AI in your business.
These are common questions before turning a use case into a real implementation.
Talk to ARKEAWe start with diagnosis and architecture, then build the components each case needs: an assistant, integrations, MCP, tools, skills, automation, user experience, dashboard, validation and production deployment. The exact scope depends on each company’s processes and tools.
Our projects are usually structured around an implementation cycle of approximately three months, from initial diagnosis to production. The final timeline depends on scope, integrations and system complexity.
Yes. We first assess the existing stack and available connection methods through APIs, MCP or other integration mechanisms. We prefer to build around current infrastructure when it is technically viable and operationally sensible.
We design the architecture with cost and performance in mind. We optimize context, calls, model selection and token usage to avoid unnecessary consumption without degrading the experience required by the use case.
We design traceability, limits, testing, logs and oversight mechanisms according to the process and required level of autonomy. Where needed, we add human approval or escalation.
Once the system is in production, it can move into ongoing maintenance, monitoring and optimization so it can adapt to changes in processes, tools, models and business needs.
ARKEA operates as an implementation partner. We do not stop at recommending tools or delivering a strategy: we design, build, integrate, validate and deploy AI systems adapted to each company’s operations.
CONTROL DASHBOARD
Your AI system should not be a black box.
We centralize the information needed to monitor activity, flows, usage and behavior in a control layer designed alongside the implementation.
Conceptual dashboard view
Concept
ActivityVisible
FlowsMonitored
UsageControlled