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Standpoint Labs

AI Agents Are Here. Understanding Isn’t. #

See decisions from every standpoint. Deploy AI that coordinates, not just connects.

Coordination infrastructure for perspective-aware AI.

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One Promotion. Five Blind Spots. #

Leadership promotes Sarah to VP Engineering. She's earned it. She's ready.

But Sarah was the invisible bottleneck on everything critical:

  • Avalon (Q2 launch) depends on her technical decisions
  • Acme (biggest customer) depends on her relationship
  • TechPartner integration depends on her architecture
  • Elena’s growth depends on her mentorship
  • Marcus expected the role she just got

No one mapped the implications before announcing.

  • Month 3: Marcus is interviewing elsewhere.
  • Month 5: Acme is asking hard questions.
  • Month 6: Elena blocked. TechPartner stalled. Avalon at risk.

One decision that made sense became a cascade no one saw coming.

This is the perspective problem. We solve it.

The Agent Babysitter Problem #

You have 8 AI agents. One makes a decision without understanding how it affects the others. Now multiply that by 100.

The problem isn’t wrong instructions. It’s that agents don’t understand implications. They don’t see knock-on effects. They have data. They don’t have meaning.

Are you an agent babysitter? Or do you have infrastructure that lets them coordinate?

This is the coordination problem. We solve it, too.

If examples land better than explanations, try The Flashlight Problem.

Otherwise, here’s why current AI tools can’t solve this:

The Context Graph Revolution Is Incomplete #

This is the year of context graphs. Every AI company is racing to give their systems more organizational context.

That’s necessary. But it’s not sufficient.

Context graphs give AI ACCESS to information. They don’t model how it RELATES. They don’t track IMPLICATIONS. They don’t COORDINATE actions.

Most harm comes from filling what we can’t see with what we fear. Agents do the same: they fill what they can’t see with data. Neither is meaning.

Context graphs tell AI what exists. We model what it means, to each party, from each perspective.

Greater autonomy + Limited understanding = Chaos

We're the missing layer.

Infrastructure for Perspective-Aware AI #

Standpoint Core #

Core maps the standpoints.

A relational core that maps what connections mean to each party. Not just that connections exist, but what matters, why, and how, from each perspective.

  • Maps relationships from multiple perspectives
  • Captures what’s at stake for each party
  • Models how the same connection means different things to different parties
  • Provides the foundational layer for perspective-aware AI

Standpoint Mesh #

Mesh lets systems act on them together.

A collaboration mesh that lets AI systems, humans, and human-AI teams share context and work together while respecting boundaries and preserving what matters to each participant.

  • Enables AI systems to collaborate across boundaries
  • Supports human-AI collaboration and human-human coordination
  • Preserves context and perspective during collaboration
  • Builds on Core’s perspective awareness

Seeing without acting is a museum.

Acting without seeing is a wrecking ball.

Together, they're coordination infrastructure.

Who This Serves #

Enterprise Leaders #

See how your decision lands across 12 stakeholder groups. Stop doing damage control. Start deciding with confidence.

AI/ML Teams #

Deploy 50 agents that actually coordinate. Stop babysitting. Start scaling.

Product Managers #

See what your feature change breaks before you ship. Stop fighting fires. Start shipping forward.

Individuals with AI Agents #

Your personal team of 8-15+ agents working in harmony. Stop managing chaos. Start trusting coordination.

Built by Someone Who Needed This #

Christoph Plough, Founder

20+ years building enterprise technology. Employee #13 at G-Log, designed the architecture for Oracle Transportation Management and led its integration post-acquisition. Co-founded MavenWire, grew it from zero to $14M/year revenue, 150+ employees, global offices, entirely bootstrapped. Sold 2016. Not the outcome I’d hoped for, but I learned what matters: protecting people, aligning incentives, and acting decisively when it counts.

I built this because I needed it for my own work. Couldn’t find it anywhere. Spent months looking. So we’re creating it. Why this matters to me. The origin story.

In Partnership with Oznog #

We collaborate with Oznog on building sovereign infrastructure for the fifth story: genuine human-AI interdependence. Different entities, shared values.

Seeking #

  • Operations / Integrator: Someone who thrives on systems, processes, and making trains run on time
  • Go-to-market advisor: AI infrastructure experience, enterprise relationships
  • Design partners: Two more organizations ready to shape what this becomes