How to Design a Paid AI and Robotics PoC That Reaches the Field

The model ran. The robot followed a path. The 3D printer produced a prototype.

None of these outcomes, by themselves, change an operation.

What a customer needs from a proof of concept is not another demo. It is enough evidence to invest, revise, or stop.

BreakAI is looking for paid PoC partners who want to combine AI, robotics, algorithms, and design around one field decision.

Start with the decision, not the technology

“Can we do something with AI?” and “Can we try a robot?” create projects whose scope keeps expanding.

The first sentence should instead define the customer decision:

Which part of the work should change, who makes the decision, and what evidence do they need?

Only then do we choose the necessary technical components:

• sensors and robotics;
• perception, inference, or generative AI;
• routing, assignment, scheduling, or optimization; and
• interface design, CAD, or 3D-printed physical prototypes.

Using all four is not the goal. We combine only what the customer decision requires.

NTT DATA argues that manufacturing AI PoCs often fail because the use case and the available data structure do not match, not merely because model accuracy is low. McKinsey similarly links the move from manufacturing pilots to performance with clear accountability for business outcomes. The common lesson is that a PoC cannot remain an isolated technical experiment.

Separate the demo, PoC, pilot, and production stages

Before the PoC starts, define what happens after the result:

• advance to a production integration;
• improve the data or equipment conditions and test again;
• reduce the scope; or
• stop because the economics or fit are weak.

A no-go conclusion is valid. The expensive result is an ambiguous success that nobody can use.

Four disciplines, one implementation

Robotics

We work with real-world behavior, including sensors, controls, communications, and mechanisms.

A useful evaluation considers existing equipment, emergency stops, safety functions, communication loss, and ownership between people and machines. We do not send commands to production equipment without confirming authority and safety conditions.

AI

We integrate perception, inference, and generative models according to the purpose and constraints.

Accuracy is only one part of the design. We also identify the cost of errors, where a person should review a result, when the system should abstain, and whether the required data exists. If a deterministic system is simpler and safer, that can be the right answer.

Algorithms

Path planning, assignment, optimization, distributed processing, and performance measurement make complex decisions explicit and testable.

BreakAI’s published work includes the multi-agent path planning projects Rovnou and rt-lacam, and isutools, which compares SQL, HTTP, host metrics, profiles, and benchmark results.

Design

Design includes more than screens. It includes information structure, operations, physical form, and prototypes people can touch.

CAD and 3D printing can test enclosures, fixtures, controls, and sensor placement in parallel with software. We also publish draw-mcp, a tool for structured diagram authoring and validation.

Problems that fit a paid PoC

A project is a stronger fit when several of these conditions are present:

• the problem recurs in a real operation and an owner cares about it;
• people currently inspect, decide, coordinate, or recover manually;
• logs, images, sensor data, drawings, or a test device are accessible;
• one operational metric or acceptance condition can be agreed;
• the operational owner and data or system owner can participate; and
• a budget, deployment, or revalidation decision follows the result.

The work is not limited to manufacturing and logistics. Equipment, infrastructure, inspection, maintenance, worker assistance, and operational software can all be relevant where physical constraints meet digital decisions.

We do not begin the following requests as written:

• an undefined request to “do something with AI”;
• unlimited customer-specific work presented as a free trial;
• an evaluation that assumes access to unauthorized data or confidential material;
• control of production equipment without clear safety ownership; or
• a demo whose only success criterion is that it looks impressive.

Focus on one decision

A good PoC is not only small. It is focused.

Examples of focused questions include:

• Can the system surface anomaly candidates with evidence that an operator can review?
• Can operational AGV or AMR logs identify waiting causes well enough to choose the next investment?
• Can a new assignment policy be replayed against the same inputs as the current policy?
• Can operators use a new control layout tested through a CAD and 3D-printed mockup without confusion?
• Can a performance problem be separated across SQL, HTTP, host, and profile evidence?

Features that do not contribute to that question stay outside the first PoC.

Why customer-specific PoCs are paid

The initial fit conversation is free. Customer-specific data analysis, prototypes, field or device evaluation, and decision reports are normally paid after the scope, deliverables, fee, and stop conditions are agreed.

Payment is not a mechanism for manufacturing success. It creates a shared commitment:

• the customer identifies the decision that will use the result;
• the required data, equipment, and stakeholder time are made available;
• BreakAI is accountable for a reproducible implementation and evaluation; and
• a poor fit is reported as a poor fit.

Price and timing are proposed after reviewing the problem, access requirements, prototype, safety, and security conditions. We do not promise a fixed improvement rate in advance.

Define the deliverables before implementation

Depending on the project, a PoC may deliver:

1. A one-page evaluation design covering the current state, constraints, and ownership
2. Working software, an algorithm, or a physical prototype
3. A repeatable evaluation procedure using the same inputs
4. Results against agreed success and failure conditions
5. Unresolved data, safety, and operational risks
6. A recommendation to proceed, run a bounded pilot, revise, or stop

We focus on the level of completeness required for the decision instead of building every dashboard, authentication flow, and operating feature in advance.

Companies we want to work with

BreakAI is looking for paid PoC partners such as:

• manufacturers and logistics operators connecting robots, equipment, and human decisions;
• teams evaluating image, sensor, or log-based AI in an operational workflow;
• companies addressing routing, assignment, scheduling, or performance bottlenecks;
• teams using CAD and 3D printing to test software and physical interaction together;
• manufacturers and integrators embedding AI, optimization, or robotics into their products; and
• research or venture teams turning technical results into repeatable prototypes and evaluations.

For AGV and AMR congestion, waiting, and manual recovery, our Rovnou article on designing a paid AGV/AMR PoC describes the required logs and validation stages in detail.

From the first conversation to a start

1. Spend 20 to 30 minutes reviewing the field event, user, data, and next decision
2. Narrow the hypothesis, including whether the problem can be solved without advanced technology
3. Confirm authority, safety, confidentiality, deliverables, and stop conditions
4. Propose the paid PoC scope, fee, and timing
5. After agreement, implement and evaluate, then decide what follows

You do not need a complete requirements document for the first conversation. One recent problem, the current response, and the data or equipment that may be available are enough to assess fit together.

Discuss an AI or robotics PoC

Select the closest topic and describe one recent field problem. After a free fit review, we will propose a paid PoC only when the work is suitable.
Contact BreakAI

References

• NTT DATA: Structural reasons manufacturing AI PoCs fail
• McKinsey: From pilots to performance—how COOs can scale AI in manufacturing
• NX Logistics Research Institute: Joint delivery-robot verification with METI
• MHLW: Safety guidelines for autonomous transport robots