Expanded capacity
People complete complex work with better context, consistency and speed.
02 · Making intelligence actionable
Enterprise AI agent, product and automation development.
We design and implement copilots, agents, intelligent workflows and digital products connected to real data, systems, controls and people.
Discuss a project↗Direct answer
Building an enterprise AI agent is not just connecting a model to a chat interface. It requires product design, context, tools, integrations, identity, evaluation, guardrails, observability and human operations. MAKINAI builds the complete experience and measures whether it improves capability, quality or speed.
A demo can impress in days. Production must handle permissions, exceptions, cost, latency, security, incomplete data and processes that cross several systems. Without product design and continuous evaluation, automation merely moves work and risk somewhere else.
What changes
Value that shows up in operations.
Connected processes
Agents and automations operate across existing tools and data under explicit controls.
Observable operations
Quality, cost, failure, usage and human review can be measured and improved.
How we help
From decision to a working system.
AI product discovery
Define the user, job, context, risk, metric and smallest complete experience.
Copilots and assistants
Experiences for marketing, sales, service, operations and knowledge teams.
AI agents
Systems that plan and execute work using tools, enterprise data and business rules.
Workflow automation
Orchestration across models, APIs, queues, approvals and legacy platforms.
RAG and enterprise knowledge
Retrieval systems with sources, permissions, freshness and traceability.
Evaluation and observability
Tests, metrics, tracing, cost, safety, feedback and human-in-the-loop controls.
Where it applies
Problems this work is built to solve.
- Copilots for planning, content, proposals, analysis and customer service.
- Agents that query systems, qualify requests and execute controlled actions.
- Automation for processes spanning multiple tools and manual handoffs.
- Intelligent products and interfaces for customers, partners or employees.
How we make
A short path to real evidence.
Define the job
Select a valuable, frequent and measurable task with clear limits and accountability.
Prototype the system
Test experience, context, tools and evaluations before increasing autonomy.
Integrate and protect
Connect data, identity, APIs, controls, human review and observability.
Operate and evolve
Measure quality, cost, adoption and exceptions to decide where autonomy can grow.
Frequently asked questions
Before we begin.
What is the difference between a chatbot, copilot and AI agent?+
A chatbot converses. A copilot helps a person perform a job. An agent can plan and execute steps using tools within defined limits. The right pattern depends on risk, autonomy and the process being changed.
How long does it take to build an enterprise AI agent?+
A functional proof can appear within weeks, but an operational system usually requires integration, evaluation, safety and observability. Timing depends on the number of tools, data readiness and desired autonomy.
Can an AI agent integrate with CRM, ERP and internal systems?+
Yes, when secure APIs, connectors or controlled access exist. The architecture must enforce identity, permission, auditability, action limits and fallback paths.
How should AI automation be measured?+
We establish a baseline and track time per task, completion, quality, rework, exceptions, adoption, cost and customer impact. A fluent answer is not proof that the system works.
What we make
Capabilities designed to connect.
MAKINAI · Making intelligence actionable