Okapii

Services

OKAPII AI: artificial intelligence applied to business processes.

We design AI discovery, PoC, and integrations to automate tasks, assist decisions, and extend existing systems with proprietary data.

Visualization of artificial intelligence applied to business data

Problems it solves

AI connected to real work, not isolated pilots.

OKAPII AI helps when a company needs to query scattered information, classify documentation, automate repetitive steps, or improve decisions with existing data.

Analytics dashboard with enterprise data

Document classification and reading

Automation to organize documents, extract data, and assist operational decisions, similar to the work delivered for Kriptos.

Person working with an artificial intelligence assistant

Assistants on internal processes

Copilots connected to proprietary information to query statuses, exceptions, courses, sales, support, or internal documentation.

Server rack with connected systems

Automation with existing systems

AI integrated with databases, APIs, and current platforms to reduce repetitive tasks without replacing the whole operation.

Methodology

Discovery, PoC, integration, and scale-up.

01

Discovery

We identify business processes where AI can create concrete value.

02

PoC

We validate technical feasibility and business usefulness before scaling.

03

Integration

We connect the solution to existing systems, data, and workflows.

04

Scale-up

We support evolution with continuous improvement and maintainability in mind.

FAQ

Direct answers about enterprise AI.

What types of AI projects does OKAPII build?

We work on discovery, proofs of concept, copilots, document classification, data-driven automation, and augmented analytics connected to business processes.

Does AI integrate with existing systems?

Yes. The goal is to integrate AI with existing data, APIs, databases, and workflows so it creates operational value instead of becoming an isolated tool.

When should a company start with a PoC?

A PoC is useful when the opportunity is clear, but data quality, technical feasibility, user value, and risks still need validation before scaling.

How do you avoid abstract AI projects?

We start from a concrete process, define an operational metric, and limit the first scope to validate value before investing in a broader solution.

Next step

Let us talk about the process you want to improve.

In an initial call, we validate goals, current systems, risks, and opportunities for automation or AI to define the best starting point.

Book a call