We build Vyera, an AI co-founder platform with 26 agents, because the most reliable way to learn what keeps AI working in production is to own an AI product outright. A client project ends at handover. A product never does. Every cost, fault and awkward edge case stays with us, and every lesson it teaches goes straight into the systems we build for clients.
What is Vyera?
Vyera is an AI co-founder for founders and small teams. It works across four pillars: Findability, Marketing, Strategy and Execution. Under those pillars sit 26 agents, each with a defined job, from search and AI visibility to content planning, positioning, pricing and an action plan that turns findings into tasks.
At the top sits the Vyera Agent, the conversational co-founder a founder actually talks to. It reads what every module has produced, including the scores and the reasons behind them, so it can answer as someone who knows the whole business rather than one corner of it.
Vyera is not a competitive intelligence tool. Tracking competitors is one capability among many, and it comes last when we describe what the product does. The job is to act as the co-founder a small team does not have.
Vyera is in pre-launch. The work described below is what stands between it and launch, and you can follow it at vyera.ai.
Why build a product when clients pay for projects?
Because projects hide the hardest problems. A project is judged at handover, when the system has seen a few hundred test cases and nobody has paid its running costs for a year. A product is judged every day, by users who never behave like the demo.
Owning Vyera puts us on the receiving end of every decision we make. If a module is expensive to run, we pay for it. If an agent produces something vague, it is our name on it. If one module cannot see what another has learned, the product feels like 26 separate tools rather than one co-founder. Those consequences are the reason we build it.
What did an audit of our own product teach us?
We audited Vyera's generative layer module by module, the same way we would audit a client's system. It found four kinds of fault, and each one changed how we work.
A score is only as good as what feeds it. Some scoring engines were built and tested but not wired to the inputs they were meant to score. The numbers looked plausible, which is exactly what made them dangerous. We now trace every score back to the data behind it before anyone sees it.
Agents only help each other if their work flows between them. Twenty-six agents add up to a co-founder only when one agent's output becomes another's input. In Vyera, the Brand Voice, Product Information and Marketing Strategy modules form a foundation that the writing agents draw on, so a post or a page sounds like the business that publishes it. Where that flow was missing, the output read as generic. We now treat the flow between modules as part of the design, not something added at the end.
Placeholder data has to be hunted down. Sample data is useful while building and harmful once real users arrive. We found placeholder values still live in places they should not have been. Finding and removing every one of them became part of the work before launch.
Quality needs a written definition. "Better outputs" is not a standard anyone can test. For each module we write golden outputs: worked examples of what a good result looks like for a real business. Real outputs are then checked against them, module by module, before a module is considered done.
A fifth lesson came from a closer look at one module. Its faults traced back to a single cause: the model was writing fields it had no authority over. When a model is given a narrow, defined job and the surrounding code owns everything else, most of those problems disappear. We now give every model call in a system one clear job.
How do you build AI that works for every customer, not just the first one?
Vyera reads a business's website to understand what it sells and how it sells it. Early on, it is tempting to tune that extraction until it works well on sites that look like your own. That is a trap. A product with many tenants has to hold its quality on any site a customer brings, whatever its structure.
So Vyera's extraction is built to be fully multi-tenant, and it is judged on sites that look nothing like ours. The same principle applies to client systems: an AI feature has to work for the tenth customer, the awkward document and the unusual case, not only the example used in the sales demo.
How do you know an AI system is still working next month?
You test it as if it will change, because it will. Models are updated, prompts evolve and new data arrives. The only defence is a body of automated tests that runs every time something moves.
Vyera's AI Search module shows the scale this takes. It tracks what ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews say about a brand, scores visibility and citations, audits how ready a site is to be cited, analyses AI crawler activity and generates content through seven dedicated writers. The build of that one module carries more than 2,500 passing automated tests, and it does not ship until a live run confirms the result.
What does this mean if you work with Linvex?
It means the team building your AI system has already met the problems your system will meet, on a product we own and pay for ourselves. In practice, these are the questions we bring to a client build:
- Where does every number come from? Scores, ratings and summaries are traced to their inputs before launch.
- What does each model own? Every model call gets one defined job, and code handles everything else.
- How does information move? If a system has several agents or modules, we design how their outputs feed each other at the start.
- What does good look like? We agree golden outputs with you and test against them, rather than judging quality by feel.
- Will it hold for the next customer? We test on the cases your business actually sees, including the messy ones.
- What happens when something changes? Automated tests run on every change, so a model update or a new prompt cannot quietly break what already works.
None of this is theory for us. It is the daily work of building Vyera, and it is the same discipline we apply to the systems we build for clients.
Written by
Rana Daniyal
Chief Executive Officer, Linvex Solutions
