From 3D Printing to AI-Built Software: How MakerBot is “Printing” Digital Products with a Multi-Engine Approach

We are excited to announce that MakerBot has partnered with Intellectible to accelerate how they build and operate the digital ecosystem around their printers. MakerBot is not adopting a single “platform” or one-size-fits-all tool. They are deploying a coordinated set of AI Engines, each designed for a specific outcome, so teams can ship new capabilities fast without rebuilding everything from scratch.

About MakerBot

MakerBot is a leader in desktop 3D printing for education and professional use. From classrooms to engineering labs, their products help educators, engineers, and designers turn ideas into real objects. As MakerBot’s footprint grows, the software and operations behind the hardware matter just as much as the machines themselves.

The Challenge: A Growing Digital Ecosystem with Unique Requirements

Modern 3D printing is powered by more than hardware. It includes portals, learning experiences, device management workflows, support operations, content libraries, and community-facing experiences. Each of those areas has its own requirements, rules, and stakeholders.

Traditionally, meeting those needs means:

• Long development cycles for every new internal tool or customer-facing portal

• Fragmented systems and inconsistent workflows across teams

• Technical debt that accumulates as features get shipped under pressure

• High engineering load for work that is necessary, but not differentiating

  
  

MakerBot needed a way to build and evolve multiple digital products in parallel, each with its own constraints, without spinning up a brand-new development effort every time.

The Solution: Multi-Engine Outcomes, Not a Single Engine

MakerBot is taking a multi-engine approach with Intellectible. Instead of forcing every workflow through one generalized system, MakerBot is deploying specialized Engines that solve specific process problems end-to-end, then connecting them where needed.

Examples of the outcomes this multi-engine approach supports:

1) Digital Experiences

Used to rapidly generate and iterate on portals, dashboards, and internal tools based on defined requirements, UI patterns, and security constraints.

2) Knowledge Engine

Used to turn scattered documentation, policies, product specs, and internal know-how into a living, queryable system that powers support, onboarding, and internal decision-making.

3) Operations

Used to automate repeatable operational processes like triage, routing, escalation, reporting, and standard work instructions, with clear stop conditions and human review where needed.

4) Learning and Content

Used to accelerate creation, organization, and updating of learning materials and program content, keeping outputs aligned to MakerBot standards and structured for real-world delivery.

The point is not “AI in general.” It is AI that is bounded, engineered, and accountable to the workflow. Each Engine has defined inputs, defined outputs, and rules that match MakerBot’s non-negotiables.

What This Unlocks for MakerBot

By operationalizing multiple Engines together, MakerBot can:

• Ship new digital capabilities faster across teams, not just in one department

• Keep workflows consistent and governed, even as requirements change

• Reduce engineering load on repeatable internal build work

• Scale support, content, and operations without scaling headcount at the same rate

• Improve reliability by anchoring automation to structured knowledge and rules, not “best guess” outputs

  
  

Printing the Future

MakerBot helped democratize how people create physical objects. Now they are applying the same mindset to digital execution: repeatable, scalable creation that turns requirements into working systems.

We are proud to support MakerBot with a multi-engine approach that meets unique requirements across product, operations, knowledge, and learning.

If your company is slowed down by the time it takes to build internal tools, maintain operational workflows, or scale customer-facing experiences, we should talk. Intellectible helps teams deploy outcome-specific AI Engines that run real work end-to-end.