Positioning for robotics & industrial AI

Make your technical advantage clear to the buyers who matter.

ModelVox helps robotics and industrial AI companies choose a focused market, substantiate their differentiation, and turn it into clear websites, sales narratives and evidence for AI-assisted buyer research.

Discuss your positioning See a sample engagement

Founder-led strategy, evidence and implementation.

Before

An AI-powered1 platform2 transforming3 warehouse operations4.

  1. Describes the method, not the job
  2. No product category a buyer could compare
  3. An outcome nobody has measured
  4. No workflow, no user

After

A vision inspection system for warehouse teams checking pallet labels before dispatch. It flags unreadable or mismatched labels for operator review.

  • Names the buyer
  • Names the workflow step
  • States what it does today
  • Claims no result it cannot show
Illustrative sample — fictional product, not a client result. Open the full sample

Who the work is for

Four buying situations. One discipline underneath them.

These are focus areas, not a list of completed projects. What connects them is one commercial problem: a technically credible product that a buyer, a partner or a committee has to understand before they can choose it.

Industrial AI software Inspection, machine vision, orchestration and operational intelligence. Can an operations leader see where your system sits in the workflow, and what changes after it? Robotics and enabling technology Commercial robots, perception, simulation, control and data infrastructure. Does a buyer compare you with the right alternative — including manual work and conventional automation? Humanoid robotics A specific task, buyer and evidence base behind the demonstration. Can a pilot sponsor tell what your robot does today, and what is still roadmap? Defense and dual-use technology Clear international positioning and approved evidence for buyers and partners. Would an international partner understand your proposition from approved, public material alone?

Where it usually stalls

Four problems the work is built for.

Not every company has all four. If one of them is costing you deals, pilots or partner conversations, the sprint was designed for it.

Your technology is impressive. Its commercial fit is hard to explain.

Common when a demonstration travels faster than a use case. The sprint starts by choosing one product, one use case and one buying group, and writing for them.

The sprint

Buyers compare you with the wrong alternatives.

The real alternative is often manual work, a conventional automation cell, a rules engine, or waiting a year. We map the alternatives a buyer actually weighs and position against those.

How we choose a position

Your team has evidence, but your sales story does not use it.

Test results, pilot data and engineering notes rarely reach the website or the deck in a form a buyer can check. A claim-to-evidence register makes the hand-off explicit.

See a register

AI-assisted research describes you inaccurately or leaves important facts out.

Your website is one source among several. We sample what assistants say for a fixed set of buyer questions, compare it with your approved facts, and prioritise corrections.

The visibility audit

Proof you can inspect

See the thinking before you buy it.

Client work appears here only when the client has approved every word, and none is published yet. In the meantime this sample — built on a fictional product so it can be shown in full — has the rewrite, the buyer-question map, the evidence register and the page outline a sprint produces.

Illustrative sample — fictional product, not a client result. Illustrative industrial-AI positioning sample A fictional vision-inspection product for warehouse dispatch, taken through the sprint: the rewrite, the buyer-question map, the claim-to-evidence register, the application-page outline and the AI-discovery questions. Open the sample

The flagship engagement

Positioning & Evidence Sprint

A four-week engagement to define one market position and put the evidence behind it to work.

Fixed scope. Quoted after a fit discussion.

Discuss the sprint See the full scope

What you receive

Buyer and alternatives map
Who decides, what they compare you with — including doing nothing — and what each needs to see.
Positioning brief
The market, buyer, problem and differentiation, written so a founder, a sales lead and a website say a compatible thing.
Claim-to-evidence register
Every important claim, its status, the evidence behind it and how it may be published.
One application page
Written and implemented, so the position ships rather than waiting in a slide.
Sales narrative and buyer FAQ
The story for a first meeting and the answers to the questions that stall the second one.
AI-discovery baseline and next steps
A documented sample of what assistants say today, and a 90-day publication and measurement plan.

Scope

  • One product, one use case and one primary buying group
  • Three internal interviews; up to two buyer or customer interviews you arrange
  • Up to five alternatives compared, including the status quo
  • Two consolidated review rounds

The four-week schedule assumes timely access to people, materials and feedback. Without buyer interviews, conclusions about buyers are marked as hypotheses.

The method

Read, choose, prove, spread — in that order.

The order matters. Publicity for a claim nobody has chosen carefully, or proved, spreads the wrong sentence faster.

  1. 1

    Read

    Buyer questions, real alternatives, the evidence you already hold, and a sample of what AI assistants say about you today.

  2. 2

    Choose

    The customer, the problem and a defensible differentiation — relevant to the buyer and supported by evidence, not merely unusual.

  3. 3

    Prove

    Each claim connected to evidence, with its status stated; then the agreed page and sales material implemented.

  4. 4

    Spread

    A scoped plan for publications and partners, and the same measurement repeated so change is observed rather than asserted.

When we report on AI answers we keep three things apart: sources an assistant cited, sources we infer may have influenced it, and provenance nobody outside the provider can see. The method in detail

Taras Netrev, founder of ModelVox

Who does the work

Founder-led, from the first conversation to the last page.

Founded by Taras Netrev, a technology entrepreneur originally from Ukraine. Taras held a senior marketing role at Intel and has founded several software companies, including a product development firm with an R&D centre in Ukraine that worked with venture-backed startups in Silicon Valley and established businesses in France.

You work with him directly. ModelVox is deliberately small: where an engagement needs other expertise — engineering sign-off, legal, export or procurement advice — it comes from your team or a named specialist, and we say which.

LinkedIn profileAbout ModelVox

Where AI fits

There is a sentence about your company you have never read.

Buyers increasingly ask an AI assistant to explain a category or suggest vendors, and the answer can shape a first impression before anyone visits your site. Your website is one source buyers and AI systems use to understand you. We help make the important facts clear, accessible and supported, then measure what the systems actually say.

What we measure: a fixed set of buyer questions, run several times on agreed platforms, with answers, citations and factual errors recorded. What we do not claim: that wording guarantees a recommendation, or that a change in the answers proves what caused it.

The argument in fullThe visibility audit

Before the first call

Direct answers.

If an answer here rules us out, that is a useful outcome for both of us.

Technical companies in industrial AI, robotics and enabling technology, humanoid robotics, or defense and dual-use technology — with a product that can be demonstrated, one commercial decision to resolve in the next quarter, access to engineering and customer evidence, and someone who owns the budget. It is not a fit if the goal is a guaranteed investment, a guaranteed AI recommendation, or promotion of capabilities that have not been demonstrated.

Six things: a buyer and alternatives map; a positioning brief; a claim-to-evidence register; one implemented application or positioning page; a sales narrative with a buyer FAQ; and an AI-discovery baseline with a 90-day next-step plan. The scope is one product, one use case and one primary buying group.

Time with three people internally — usually a founder or commercial lead, someone from product or engineering, and someone close to customers — plus whatever exists: test results, pilot data, customer feedback, decks and technical documentation. If you can arrange up to two conversations with customers or prospects, conclusions get stronger; if you cannot, we mark them as hypotheses. Anything that cannot be public stays in your systems and out of public material.

No. AI answers vary between sessions, between systems and over time, and nobody outside the providers controls them. We record a documented sample of buyer questions before and after the work, so changes can be measured. They cannot be promised — and neither can funding, procurement awards or revenue.

Commercially first: whether qualified conversations, meetings and sales feedback improve, and whether your team actually uses the material. AI discoverability is measured separately, on a fixed set of buyer questions run several times on agreed platforms, scored for inclusion and factual accuracy. A before-and-after difference does not by itself prove what caused it, and the report says so.

Start with a 20-minute fit conversation.

Tell us the product, the buyer and the decision in front of you. If the sprint is the right next step, we will scope it. If it is not, we will tell you what would be.

Discuss your positioning See the sprint scope

If we can help, you leave with one specific observation about your positioning. If we cannot, we say so.