About Ainary

AI + Binary Star. Two bodies in orbit, co-evolving.

The Name

The name comes from binary stars: two bodies locked in gravitational orbit, each shaped by the other's pull. Not one dominating the other. A system that co-evolves.

That's how I see the future of work. Not AI replacing humans. Not humans using AI as a tool. Something deeper: a partnership where both sides get sharper over time. The human brings judgment, context, taste. The AI brings speed, scale, tireless execution.

AI + Binary = Ainary.

The next wave of intelligence won't come from bigger models. It'll come from better partnerships between humans and machines.

Mission & Vision

Mission

Build intelligence tools that multiply what one person or small team can achieve. Research, analysis, synthesis: done in minutes, not weeks. Decisions that compound.

Vision

A world where your output and the quality of your decisions aren't limited by the size of your team. Where a founder with the right tools outperforms a company.

"Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world.": Albert Einstein

What I Believe

AI-first beats AI-later. Companies built on AI from day one will outcompete those retrofitting it. The gap between "uses AI" and "is AI" keeps widening.

Honest confidence over false certainty. Every claim is source-grounded. Every insight includes a confidence level. When I don't know something, I say so. Trust isn't a feature: it's the whole product.

Synthesis is the bottleneck. The world doesn't need more data. It needs better synthesis. Strategic research, due diligence, competitive analysis: work that shapes companies but blocks decisions for days. Ainary compresses that.

Founder

Florian Ziesche
Florian Ziesche
Founder · Former CEO & MD at 36ZERO Vision · Based in NYC · Munich

I spent more than 10 years building algorithms, software, computer vision & AI systems. Raised capital. Led teams. Then I watched my co-founder and team outproduce large teams: and knew the game had changed.

How It Works

Every project starts the same way: define the task. What's the question? What does a good answer look like? What sources matter? Clear input, clear expected output. No ambiguity.

From there, I build specialized agents for the task: not generic chatbots, but purpose-built AI workflows. A research agent that knows which databases to query. An analyst that understands financial metrics. A critic that looks for what's missing. Each agent has a role because each role requires different reasoning.

These agents don't work alone. They're orchestrated: sequenced, parallelized, and cross-checked against each other. The architecture combines state-of-the-art LLMs with a knowledge graph, specialized research tools, and structured evaluation pipelines. Not a single prompt. A system.

01
Research first: Everything starts with evidence. SEC filings, papers, market data, patents. No opinion without a source.
02
Build standards, then improve: Every output follows a quality standard. Then it gets better with feedback: the Human + AI loop that makes each iteration sharper than the last.
03
Trust as a currency: Every claim is source-grounded. Every insight carries a confidence score. When the data is weak, I say so. Trust isn't a feature: it's the unit of exchange.
The goal isn't to produce more. It's to free you for the work that matters.