How do you tell the story of an AI that does more than just generate content?

askee is not just another chatbot that generates the same output regardless of input. It’s an entire system supporting decisions and action within organizations at multiple levels: from HR to sales, IT, marketing, production, and customer service.

Client:

Askee

Completion date:

since December 2023

Scope of work:

What was missing at the communication level – the things that bring a brand into the game

a consistent language layer (tone of voice + semantics)

clear positioning

a presence that builds trust

reinforcing the message about the key role of humans in working with AI.

And that's exactly what we set out to do.

The challenge: a solution that doesn’t fit current definitions of AI

askee operates at the intersection of AI, management, and decision-making processes in companies. These are areas where oversimplification comes easily and precision is hard to achieve. From a communication standpoint, for us this meant:

no ready-made language model for a new category

a market dominated by the narrative that AI = content generation

the need to build expertise from scratch

scattered touchpoints with the audience: LinkedIn, media, community

responding to the negative narrative about AI's impact and the diminishing of the human role

In short: a lot of output, but no single model tying it all together… and actually working once it goes into production.

Because in AI, a prompt isn’t enough – what matters is architecture and the model

We didn’t start in the standard way, with content production. We knew that architecture is what matters most here, so we started by building a communication framework that would be scalable across all channels.

We designed it as three collaborating layers

O1. Narrative

We defined askee as a system that supports decisions within companies and integrates all the necessary data in one place. A real work assistant, not just another content-generation tool.

In the end, we defined a shared semantics – a model that ties the whole communication together:

what askee is
what askee is not
what change it brings to the organization
who it can provide real support for
what can change in companies through its implementation, and at what level

O2. Expertise

We built a communication line based on real problems:

decisions in organizations and how they’re made
managers’ accountability to their teams and effective task delivery
errors and inconsistencies in how companies operate – regardless of industry or size
the role of AI in business processes and its impact on building organizational culture

askee started talking about what actually interests business – not about the technology itself.

O3. Presence (multi-channel communication)

We designed a consistent presence across key touchpoints, including LinkedIn, industry media and publications, expert articles, community moderation (Human AI-Lab), and we laid the groundwork for the “Manifesto for organizations with AI at the center of action.”

Each channel serves a different function here, but they’re all tied together by one shared message.

A communication layer that works like a well-trained model

In this case, LinkedIn became not just a publishing channel, but an environment where the communication model keeps learning from the market and responding to its real challenges.

We create communication that:

builds expert positioning through repeatable narrative patterns (not single posts)
operates on real business problems
instead of generic AI threads
initiates discussions that increase reach and deepen brand perception
is built on messaging from the product, not about the product

How did we achieve this? We simply gave askee the floor. askee communicates with the world and drives its own narrative.

This turns it into a communication interface that shows a way of thinking.
And that leads to one thing: askee becomes the potential user’s experience.

Every post is an instance of a larger communication model, because it:

reinforces a specific way of thinking about the product, adding another building block to the narrative model
builds consistency over time, not just reach in the “here and now”
guides the user through a specific context and situation
addresses managers’ real pain points
explains decisions and shows their long-term effects, drawing on company knowledge.

In other words: it acts as an inference layer for the user. LinkedIn is the runtime for our communication model – the key place where askee is constantly fine-tuned based on market data and real user needs.

Media relations and expert publications as an influence layer

For this client, we treat the media not as channels for distributing content, but as an environment where the communication model scales and builds real influence, strengthening askee’s recognition. We’re responsible for:

creating articles and expert commentary that ground askee in real business problems, not an abstract narrative about AI
designing editorial materials tailored to the logic of specific media outlets
distribution and presence in quality outlets that build credibility, not just reach

In this case, media act as an external validation layer – the place where communication moves from the level of declaration to the level of market recognition.

The effect? We move askee from the level of a brand to the level of “a voice in the discussion.”

A layer of learning and co-creation: Human AI-Lab

As part of the collaboration, we created and moderate a space for conversation. askee wants to connect – and that includes people too. This lets us engage experts, exchange experience, gather real cases from organizations, and spark threads for future narratives.

This think tank is a living dataset… and a training layer for the narrative model, letting us test, observe, and calibrate the narrative based on feedback from the market.

At the same time, it’s the environment in which we’re co-creating the “Manifesto for organizations with AI at the center of action.”

Why do we do this?

Because growing the story around askee is a good pretext for setting a framework. We want to bring order to the way people think about using AI in companies, define the boundaries of responsibility, and establish a shared model of interpretation. This way, askee doesn’t just communicate – it genuinely helps shape the discussion.

From publications to a model that calibrates itself

The specificity of this project wasn’t just about increasing the volume of content — it was about something more: presence and consistency in the message, and defusing the objections building up around the development of AI. Output after iteration:

unambiguous brand positioning
growing visibility in the media and expert space
consistent thought leadership
an active community around the brand
content that builds value over time

Not a bug: the system works exactly as architected

In communication about AI, what matters isn’t writing as much as possible. This race is won by whoever can define the category, ground the technology in a real business context, and consistently carry the narrative forward.

What matters is whether the product itself teaches the market how to understand it.