AI-Native Operational Infrastructure for SMBs

Orbitali is building the operational layer SMBs never had

We start by deploying custom systems for real operators. We scale by turning recurring patterns into vertical AI-native infrastructure.

The market has a structural gap

SMBs are expected to run complex operations on tools that were never designed for them. This is not a tooling preference issue. It is a systemic delivery failure.

SaaS is too generic

Horizontal software covers broad use cases, but misses the operational edge cases that define each business.

Consulting is too slow

Traditional service models produce custom outcomes at low speed, with weak continuity and little product compounding.

Internal teams are too expensive

Most SMBs cannot justify building and retaining software teams just to keep operational tooling aligned with daily changes.

Our model: custom delivery on top of reusable infrastructure

Orbitali combines implementation speed with platform compounding: one AI-native core, adapted front layers, and an embedded agent that keeps each system evolving.

Reusable AI-native core

Data models, workflow primitives, permissions, integrations, and AI interfaces are built once and reused across deployments.

Custom front layer

Each client gets an operational system shaped around their real process, not a rigid template.

Embedded AI coding agent

The agent is part of the product architecture, not an add-on assistant. It executes controlled changes inside the live system.

Compounding infrastructure

Each implementation strengthens the shared core: better modules, cleaner abstractions, faster future deployment.

Pattern extraction engine

We track repeated workflows and constraints across clients to identify what should be standardized as product.

Path to vertical base apps

Recurring patterns are packaged into base applications by segment, reducing delivery time while preserving depth.

The embedded AI agent is the new interaction layer

This is not a chatbot feature. It is how operators and managers directly shape workflows, fields, validations, and reports inside the system over time.

Operational changes happen in hours, not backlog cycles
Teams reduce long-term dependency on external development
Institutional process knowledge stays inside the product layer

orbitali agent

Online

Route any order above 5,000 EUR to finance approval before fulfillment.

orbitali

Workflow updated. A finance approval step now triggers automatically for orders above 5,000 EUR.

Split today's backlog by region and assign to team leads.

orbitali

Done. I created the assignment view and grouped open tasks by region with lead ownership.

Create a weekly report with cycle time and blocked tasks by team.

orbitali

Report scheduled every Monday at 08:00 with cycle time trends and blocked-task breakdown.

Describe the change you need...

Productization strategy

Scalability is built into the operating model from day one.

01

Custom Systems

Deploy operational systems with real clients, real constraints, and clear performance outcomes.

02

Pattern Recognition

Capture repeated process logic, data structures, and agent interactions across deployments.

03

Base Apps

Package recurring patterns into reusable applications for specific operational categories.

04

Vertical AI Systems

Evolve base apps into full vertical platforms with faster onboarding and deeper defensibility.

Market opportunity

The opportunity is broad, practical, and already visible in day-to-day operations.

SMBs are the largest operating base

There are millions of SMBs globally managing critical workflows with fragmented tools and manual processes.

Operational inefficiency is universal

Inventory, billing, field operations, and internal coordination share the same execution bottlenecks across sectors.

AI lowers internal tooling cost

AI-native development and embedded agents make previously expensive internal systems feasible for this segment.

Vision

Orbitali is building the operational infrastructure layer for the next generation of SMBs: systems that start custom, learn from usage, and mature into vertical AI-native platforms.