Serspace · Built for how you work

Your agent.
Your data. Your world.

Introducing Serspace Resolve. A personal agent that gets you. An architecture that protects you.

Personal agents are everywhere. But most of them are guessing. We're building one that genuinely knows your world — and one you can actually trust with it.

Get in Touch → Read the Thesis →

What We Believe

The problem with AI isn't compute or intelligence. It's trust.

Think about what it would take for you to hand an agent the keys to your life. It would need to know your world — your people, your projects, your context — not an averaged-out version of everyone else's.

And you'd need to be sure that nothing you share with it could ever be used without your permission. Most tools today can't make either promise.

Most AI tools were designed to keep you coming back, not to help you get things done. The tab overload, the context switching, the constant noise — it's not an accident.

We started Serspace because we think there's a better way. An agent that works for you, not the platform.

"The most important AI of the next decade won't be the biggest model. It will be the one that knows your world, reasons faithfully over it, and keeps it entirely yours."

— The Serspace thesis

The Architectural Bet

The mainstream approach relies too much on the LLM. We're making a different bet.

We build a model of your world instead of turning all of your data over to an LLM. The result is private, faithful reasoning. Ask the same question twice, get the same answer. See exactly how it got there. Fix it when it's wrong.

01 · Your World Model

It knows you, not everyone else

Your agent works from a real model of your world — the people, projects, and commitments that make up your life. Built from what you tell it, not scraped from the internet. The reasoning starts from something true.

02 · Proofs, Not Predictions

Reasoning you can actually follow

Every answer comes with the chain of facts and rules that produced it. Ask the same question twice, get the same answer. When your agent has to make an assumption, it shows you and waits for you to accept or reject it. When it can't reach a conclusion, it says so and points at what's missing — instead of inventing one.

03 · Privacy That's Actually Private

Nobody else gets to look inside

Your world model is for you and your agent — not for us, not for advertisers, not for whoever buys the company in five years. We're designing every layer to minimize exposure by architecture, so the strongest privacy guarantees come from how the system is built, not from a policy you have to take on faith.

04 · Built for You Alone

Your agent works for you alone

No ads, no upsells, no third party paying to nudge the answer. Your agent has one job: serve your interests. And if you ever want to leave, you take your model with you and walk out the door. An easy exit is the truest test of whose side we're on.


What We're Building

Built for people who think,
create, and communicate for a living.

Your email, docs, and decks aren't really the work — they're the paperwork around it. Serspace Resolve looks past all of that to the work itself: the ideas you're developing, the clients you're moving forward, the decisions that need to get made.

It tracks what's important across everything, so you're not constantly reconstructing context from scratch. And when you do need to write something, it helps you shape your thinking into whatever the world needs — a reply, a brief, an update.

Less time on the paperwork.
More time on the work.


Progress

Where we are.

Here's what we've figured out, and what we're currently researching.

Proven

Faithful reasoning on open models

Our natural-language-to-logic pipeline runs end-to-end on local open models, producing answers with proof trees — or honestly saying it cannot conclude — instead of plausible-sounding guesses. The reasoning step is deterministic and reproducible.

Proven

A persistent, controllable world model

Facts persist with full provenance and are validated against the same ontology the translator uses. The system has a real, queryable model of your world that grows over time — not just a chat history — and every entry is auditable.

Investigating

Human-in-the-loop assumption review

When the system translates what you said into formal logic, it makes interpretive choices. We're working on the confirm-before-commit step so you can see and approve those choices before they change your world model. The plumbing is wired; the interaction design is the open question.

Investigating

Reasoning grounded in good judgement

When the system can't prove a conclusion, can it surface the smallest set of premises that would close the gap — and weigh them against principles of sound reasoning? We're working on abductive inference that makes hidden assumptions explicit and reviewable, rather than quietly smuggling them in.

Want to know more? Please Get in touch.


Who We Want to Hear From

We'd love to hear from you.

We're looking to connect with design partners, researchers, builders, and investors.

01 · Design Partners

You'd like a personal Agent

You write, advise, manage, build, or create — and you'd like a personal agent that understands your world. Come help us figure out what that looks like.

02 · Builders & Researchers

You're working on something related

If you're trying to solve problems in privacy and faithful reasoning, we'd like to connect.

03 · Investors

You see where this is going

If you agree personal AI will succeed on the basis of trust, accuracy, and cost, rather than on bigger LLMs, reach out.


Get in Touch

Say hello.

Whether you want to use it, build with us, or back us — we'd love to hear from you.

If something on this page made you nod, or argue with the screen, or wonder what we're building — that's enough of a reason to reach out. Tell us what you're working on, what's been bugging you, or just say hi. We read everything, and we write back.


Who We Are

Built by people who wanted it themselves.

We're engineers and builders who kept running into the same problem: AI tools that felt powerful in demos and disappointing in practice. So we decided to try a different approach.

Chris Barber

Chris Barber

CO-FOUNDER & CEO

Joe Torreggiani

Joe Torreggiani

CO-FOUNDER & CTO