Title: ARKEA IA | Enterprise AI implementation Description: We design and implement AI assistants and systems connected to business data, tools and processes, with MCP, tools, skills, UX, dashboards and maintenance. # 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. [Explore an implementation](contacto.html#proyecto) [How we work](como-trabajamos.html) Design Custom Integration Real Operations Supervised ≈ 3 months approximate implementation cycle End-to-end from architecture to production Dashboard + maintenance control after launch ## Real people behind every implementation. Strategy, AI engineering, automation, product and user experience working as one team to build around your company. ## WE INTEGRATE THE LEADING AI MODELS OpenAI · Anthropic · Google · xAI · DeepSeek · Meta Runway · Mistral AI · OpenRouter Supabase · n8n ## Everything needed to take AI from idea to production. ### Diagnosis and architecture We analyze processes, data, tools and opportunities to define where AI should enter and how the system should be built. DIAGNOSIS · ROADMAP · ARCHITECTURE ### Custom AI assistants We build assistants adapted to your company’s knowledge, context, rules and way of working. AI AGENTS · CONTEXT · RAG ### MCP & integrations We connect AI with existing systems, data and services so it can work inside your infrastructure. MCP · APIs · INTEGRATIONS ### Tools & actions We create tools that let the assistant retrieve information, use services and execute real actions. TOOLS · ACTIONS · FUNCTIONS ### Skills & automation We develop reusable capabilities and workflows for recurring tasks. SKILLS · WORKFLOWS · AUTOMATION ### UX & interface We design how your team interacts with AI so the system is intuitive, fast and genuinely adopted. UX · UI · ADOPTION ### Dashboard & oversight We create a control layer to observe activity, flows, behavior and system evolution. DASHBOARD · LOGS · CONTROL ### Optimization & maintenance We optimize architecture and token usage to control operating costs and maintain the system after launch. TOKENS · OPTIMIZATION · MAINTENANCE ## 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. ## 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. ### 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. ### Architecture Assistant, integrations, tools, skills and control criteria. We design how the system will work before building: sources, permissions, connections, actions and user experience. ### Build Development, automation, interface and dashboard. We turn the architecture into a functional implementation connected to the required components. ### Validation Testing, behavior, usage and user experience. We test real scenarios, tune behavior, review friction and verify that the system works as expected. ### Production & optimization Launch, oversight and evolution. We put the system into operation and prepare the next stage of maintenance and continuous improvement. ## One AI architecture can support different parts of a business. ### Operations Copilot Supports operational tasks, consults tools and executes defined processes while preserving traceability and human escalation when needed. Tools actions Workflows processes Control oversight ### Knowledge Assistant Consults documentation and internal sources using company-specific context while respecting configured sources and permissions. RAG context Sources information Permissions access ### Sales Copilot Retrieves CRM context, prepares information and supports sales-process tasks without fragmenting work across tools. CRM context Tools actions Automation follow-up ### AI Control Center Centralizes activity, usage, behavior and system signals to maintain a shared operational view. Dashboard visibility Logs traceability Usage consumption ## Three signs your company is ready to implement AI. ### 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. ### People spend time searching before they can act. Documents, CRM, tools and internal knowledge can become accessible context for a connected system. ### 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. ### AI that knows your company context. We connect information, documentation and internal sources so the system works with the knowledge each task requires. [See AI assistants](inteligencia-artificial.html) ### 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. [See integrations](integraciones.html) ### 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. [See dashboard](dashboard-ia.html) ## 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 ARKEA](contacto.html#proyecto) ### What does an ARKEA implementation include? We 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. ### How long does an implementation take? 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. ### Can you work with the tools our company already uses? 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. ### How do you control AI usage and cost? 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. ### How is the assistant supervised? 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. ### What happens after launch? 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. ### Is ARKEA an AI agency or a consulting firm? 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. ## 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 [Explore the dashboard](dashboard-ia.html)