Key Takeaways
- When you log in to AnyInsight, the screen splits in two. On the left is a system panel that changes according to your permissions. Everything on the right is the Agent Storefront: your workspace.
- The Storefront has three layers, top to bottom: an LLM conversation area, a shortcut area (Get Started and Favourites), and a catalogued library of agents organised by business function.
- The conversation area gives you 20+ leading large language models plus Prompt Tools, Skills, Connectors, Extract, and MAIA multi-model collaboration.
- Whether an agent appears in your catalogue at all is decided by three permission tiers, Org / Workspace / User. If you can see it, you are cleared to use it.
- Nothing in the catalogue fits? Build your own agent with the no-code Agent Builder and share it with your team.
- The point of a one-stop entry is not convenience. When the compliant path is also the fastest one, employees have no reason to go around it — which is what actually reduces shadow AI and shadow agents. This is the direction in which the Storefront is evolving into an AI operations centre.
- HEARTBOT AI Inc. has completed an internal conformity self-assessment against the EU AI Act, and AnyInsight holds ISO 27001 certification covering the entire organisation.
Most European businesses begin their generative AI programme with the same question: which model should we use? It is rarely the question that decides the outcome. What stalls a rollout is almost never raw model capability. It is delivery: getting the right AI capability into the right hands, pointed at the right task.
Claire is a marketing specialist at a European manufacturer. She does not write code, and she has no appetite for studying prompt engineering. What she wants is straightforward: log in, and start working.
The AnyInsight Agent Storefront is built for people like Claire. It sets out to solve one problem: bringing every large language model, every built-in and self-built AI agent, and every application the company already runs into a single entry point. One stop, one screen, where an employee can get the work done safely and effectively — and entirely inside the organisation's governance and permission controls.
Figure 1: The Agent Storefront at a glance — LLM conversation area, shortcut area and agent catalogue, top to bottom.
1. The moment you log in: two zones, two roles
Claire's screen is divided left and right. The left column holds system functions. Everything on the right is the Agent Storefront, where the actual work happens.
No two people see the same left column. Claire can switch between the workspaces she has joined, revisit past conversations under Dialogues, browse the AI assets she has been granted through Inventory, and open Agent Studio to build her own agents and prompt templates.
When Marc from IT logs in, that same column carries more: Agent Controller, Prompt Controller, Automation, Workspace Admin. The detail that matters is subtle but deliberate. Claire is not clicking into those functions and being turned away. They are simply not on her screen. One fewer option is one fewer moment of hesitation.
Figure 2: The system panel on the left, shown here from an administrator's view.
Standard users do not see the Admin items.
2. Top layer: one input box, a full cabinet of tools behind it
This week Claire owes her director a competitive analysis deck. She types the brief into the conversation box, and everything that follows happens on the same screen — no new tab, no second tool, no separate login.
- Pick a model. More than 20 leading large language models (LLMs) sit in the dropdown, among them Mistral, ChatGPT, Gemini, Claude, Perplexity and Grok. Changing model no longer means changing platform.
- Prompt Tools. Not sure how to ask? The Prompt Wizard reads the task first, then drafts a prompt for Claire to adjust. Anything that works well can be saved as a Prompt Template and called up next time.
- MAIA multi-model collaboration. Open the Single Mode dropdown and the three MAIA modes appear. You pair up any two models: Parallel Mode runs them side by side, Integrative Mode consolidates their two perspectives into a single answer, and Critique Mode has them examine each other's reasoning. MAIA can also be switched on inside the agents further down the page. Claire chooses Integrative, and gets a two-sided read on the competition in a single pass.
- Skills. Web Search pulls in current market movements, Image Generation supplies the visuals, and the finished work exports to Word, PowerPoint, Excel, PDF or Markdown.
- Connectors. Over 500 external applications connect through MCP connectors — built on the open Model Context Protocol (MCP) standard — including Google Drive, Gmail, Outlook, Slack, Salesforce and Jira. Claire does not log into four systems to assemble one set of facts, and she does not need to remember how each of them works — she describes what she needs in ordinary language. The AI works where the data already lives, and the learning curve of every connected system flattens out.
- Extract. Key information scattered across a long conversation is pulled into structured, immediately usable data in one click.
Two icons sit in the top right corner. The bell carries system alerts, and its contents also follow permissions: Marc receives security alerts, while Claire is notified when a scheduled task finishes. Next to it, AI Help opens a direct line to the AnyInsight support assistant, so getting stuck does not mean raising a ticket.
Figure 3: The LLM conversation area, with Prompt Tools, Skills, Connectors and Extract built in.
3. Middle layer: two seconds back to yesterday's work
Below the conversation area sits the shortcut layer. It does only two things, and both of them matter.
Get Started is a fixed set of five guides — how to create a template, how to design an agent, how to choose an LLM, and so on — so that someone on their first morning is never left staring at an empty screen. These five cannot be removed, because new joiners keep arriving.
Favourites is the opposite: entirely Claire's to define. She has pinned the two agents she opens every day, so she never has to search the catalogue for them. Experienced users rarely want more features. They want a shorter path.
Figure 4: The shortcut area. Get Started is fixed by the system; Favourites is curated by the user.
4. Bottom layer: a catalogued library of agents
Further down is the largest part of the Storefront. The name suggests a shop, but the way it actually behaves is closer to a library: catalogued, classified, curated, and governed by lending rights.
What Claire sees is a collection of agents catalogued by business function — Education, ESG, Finance & Accounting, Image, IT, Legal, Marketing, Parental education, Procurement & Supply Chain, Productivity, Professional Counseling Assistant, RD, Sales, Talent, and Uncategorised. She opens Marketing and finds a row of agents ready to run. When she does not know what she is looking for, she browses the classifications. When she does, she searches by keyword.
The collection has three kinds of holdings: LLMs you can talk to directly, Built-in Agents created and maintained by AnyInsight together with domain experts, and Custom Agents the company has built and contributed itself. Categories can be edited, but which of them Claire sees at all depends on what her administrator has released from the AI inventory against her permissions — much as any library has open shelves alongside closed stacks you need particular credentials to request from.
The one difference from a library: here, every "book" does the work itself.
One easily missed control sits at the top right: a toggle between Agent and Prompt Template. The same catalogue switches between ready-made agents and ready-made prompts — one click to a finished result, or full control of the conversation, depending on the task.
(AnyInsight also offers Task Agents and Workflow Agents. They fall outside the Storefront and will get an article of their own.)
Figure 5: The agent catalogue, browsable by category or searchable by keyword, with a toggle between Agent and Prompt Template views.
5. Why what you cannot see matters more than what you can
Claire's colleague in Legal has an entire row of contract review agents in his catalogue. Claire sees none of them. That is not a gap in her account. That is the design.
AnyInsight's display logic rests on a zero-trust AI security architecture, narrowing visibility into three tiers:
| Tier | Who it covers | What lives there | What it means for the user |
|---|---|---|---|
| Org | Every employee | Company-wide processes and approved knowledge | Everyone works from the same source of truth |
| Workspace | A department or project team | Domain expertise and team-owned data | Teams can iterate fast without over-exposing anything |
| User | One person | Personal working habits, drafts, experiments | Room to experiment without disturbing colleagues |
The layering produces two results at once. For the user, fewer options means faster decisions. For the organisation, data and knowledge stop drifting sideways between departments. Sales agents have no business appearing in R&D. HR agents belong to a specific set of managers. Every person's Storefront looks different, and it should.
The other half of that story is governance. Strip the EU AI Act down to what it asks of a business deploying AI and three obligations remain: people must understand what they are using, the organisation must be able to say who used what, and the process must leave a record. Three permission tiers plus end-to-end logging of prompts and outputs turn those three into the default behaviour of an ordinary working day, rather than a project that starts the week before an audit.
The same instinct runs through the AI Act's expectations around AI literacy: staff should be equipped to understand and judge what an AI system gives them. The Get Started guides and the Prompt Wizard are that requirement built into the product, instead of a training deck circulated once and forgotten. And the principle of data minimisation that European organisations already know well from the GDPR is, in practice, exactly what a permission-scoped Storefront enforces: you are shown the data and capability your role requires, and nothing beyond it.
HEARTBOT AI Inc. has completed an internal conformity self-assessment against the EU AI Act, including a risk-tier classification and a mapping of the obligations that follow from it. On the security side, AnyInsight holds ISO 27001 certification covering the entire organisation, and European customer data is hosted in the AWS Frankfurt region.
For Claire, all of it reduces to a single sentence: what is on her screen is what she is cleared to use.
6. One entry point, because blocking has never worked
You cannot discuss governance for long without arriving at shadow AI.
It is no longer a fringe behaviour. Verizon's 2026 Data Breach Investigations Report found that the share of employees regularly using AI on corporate devices went from 15% to 45% in a single year, with 67% of them signing in through non-corporate accounts. Shadow AI is now the third most common non-malicious insider action in the report's data loss prevention dataset, a fourfold increase in percentage terms year on year. The data type most often pasted into external models is company source code.
Two further figures are worth sitting with. UpGuard's survey of 500 security leaders and 1,000 employees worldwide found that 45% of workers find ways around applications that have been blocked — blocking does not remove the behaviour, it removes your view of it. And Gartner's Global Labor Market Survey, conducted in the first quarter of 2026 across 12,004 employees and managers in 40 countries, is blunter still: 88% of employees who already have access to enterprise AI tools are also using personal AI tools for work.
That last number punctures a common assumption. Issuing the tool is not the same as solving the problem. What decides which route people take is which route is faster.
The reason is not complicated. Shadow AI almost never starts with bad intent. It starts with the absence of a faster sanctioned option. When the approved route costs three extra tabs and two request forms, people quietly return to their personal accounts. By 2026 the problem has moved up a level: employees are no longer only pasting text into an external chatbot, they are building their own agents on outside platforms and wiring their own data into them — shadow agents. What leaks then is not one conversation. It is an entire working process carrying company knowledge inside it.
AnyInsight's answer is not a higher wall. It is making the compliant path the fastest path. A single entry point gathers what employees would otherwise hunt for across the business: the model they want, the agent that already exists, the systems the company already runs. When the internal option is genuinely smoother than the external one, going around it stops making sense. That is the only shadow AI strategy that holds up over time, and one of the rare moments when governance and productivity pull in the same direction.
7. Where this is heading: the AI operations centre
Follow the connector story one step further and a larger picture appears.
The real cost of an enterprise application has never been the licence. It is the learning curve. How do you run that query in the ERP, raise that ticket in the CRM, update that status in the project system? Every platform has its own logic and its own interface, and every one of them has to be taught again. Once those systems can be operated from the Storefront in natural language, that curve flattens dramatically. Staff no longer learn ten systems. They learn one thing: how to say clearly what they need.
This is also where the market is moving. In its 2026 enterprise AI predictions, the analyst firm Ecosystm describes investment shifting away from embedding AI into individual products and towards the platform layer — platform architecture, AI governance, explainability and operational resilience. PwC's 2026 Digital Trends in Operations survey shows where the gap sits: among operations and supply chain leaders at technology and telecommunications companies, 94% say their organisation has implemented AI and 40% are scaling it enterprise-wide, yet only 21% say the strategy is fully embedded across business units. For most, AI still sits in pilots rather than in core processes.
Closing that gap is the long-term direction of the Storefront — and the library comparison stretches one step further here. Modern libraries stopped being places you merely borrow from a long time ago; they became shared spaces where people actually sit down and do the work. The Storefront follows the same path: from a catalogue you come to in order to fetch a tool, into the organisation's AI operations centre. Employees do the work there. Agents are shared and improved there. External systems connect there. And administrators see usage, permissions and audit trails in one place rather than five.
When the tools are scattered across ten browser tabs, governance can only ever be retrospective. When the work converges on one centre, governance can finally be live.
8. From user to creator
The story does not end there. Claire works through the whole Marketing category and finds nothing that matches her company's particular product launch process.
So she builds one. In Agent Builder she assembles the prompts she already trusts, the company's brand tone guidelines and a handful of reference documents, defines the questions the agent should ask before it starts, tests it twice and publishes — first at User level for herself, then out to the whole Marketing workspace once it holds up.
A month later, that agent sits in the Favourites of everyone on the marketing team. Claire's personal way of working has become a shared asset. This is the part of the Storefront that is easiest to underestimate: best practice stops living in one person's head or private notes and becomes a capability the organisation can draw on at will.
9. Closing: from "which model" to "who can use what, and how"
Claire's day, from login to delivery, is four steps. The system hands her a Storefront shaped by her permissions. She handles one-off questions in the conversation area and repeatable processes in the catalogue. When nothing fits, she builds it. When it works, she shares it. She never leaves the entry point, and no part of it routes around her employer's controls.
That is what a one-stop entry buys you: it resolves two things long assumed to be in tension. Employees want a shorter path. The organisation wants visible governance. When both point at the same screen, AI stops being a scattering of pilots and becomes productivity that actually runs every day.
While much of the market is still arguing over which model to standardise on, AnyInsight is focused on who can use what, and how. Because what decides whether AI actually helps was never a score on a model leaderboard. It is the first minute after logging in.
