We build AI systems you won’t regret owning.
Matter & Gas designs and builds AI-powered software for founders and operators who want to ship something real — not just something that works once in a demo.
We move fast, but we don’t ship throwaways.
Most early AI products don’t fail because the model is bad.
They fail for more ordinary reasons: messy inputs, unclear ownership, surprise costs, brittle logic, or systems no one knows how to review once they’re live.
We’ve seen this enough times to be opinionated about it.
So when a product earns the right to be built, we design foundations that are as solid as they reasonably can be — and ready to scale.
That means being honest about constraints early: what the system actually needs to do, where complexity creeps in, how humans stay in the loop, what breaks first in practice, and what it costs to run — not just to build.
Cutting the wrong scope early is how things ship in weeks instead of getting rewritten later.
The tool below shows how we think.
It’s a short, structured way to describe an idea and see how it holds up when treated like a real system. In some cases, that confirms an idea is viable. In others, it surfaces risks that make it smarter to pause, narrow, or rethink.
Many of our conversations start exactly this way.
You describe what you’re trying to build — who it’s for, what decisions it supports, what constraints already exist, and what would be painful if it went wrong.
We reflect it back clearly: where the complexity really is, where AI helps and where it doesn’t, what failure modes to expect, and what an initial build might reasonably look like.
What you’ll get back
A structured report covering 7 dimensions of feasibility:
Product Description
Summary of the system, user journey, what AI handles vs. what humans review, and likely failure points.
Overall shape
A 1–10 complexity rating with the key drivers that push it higher.
What will matter most in practice
Tagged constraints (compliance, latency, cost, etc.) with severity levels and explanations.
Where automation helps — and where it shouldn’t
Which tasks automation can handle confidently and which require human judgment, with reasoning.
What this will cost to run
Estimated per-unit cost, scaling factors, non-linear cost drivers, and ongoing review workload.
How this could break over time
Named failure scenarios with likelihood, impact ratings, and specific mitigations.
A sensible first milestone
What to include and exclude in a first build, a concrete first milestone, and a timeline estimate.
Start with your idea.
We’ll reflect it back clearly — as if it were going to be built and operated for real.
This is a short, structured way to describe what you’re thinking and see how it holds up when treated like a real system.
- Technical complexity
- Delivery risk
- Operating cost realities
- Where systems tend to break in practice
Public example system — please don’t submit confidential or client-specific information.
Single pass, no follow-up questions.
The output is structured on purpose so it’s easy to review — and easy to talk through.
AI Product Analyzer
Sometimes this will suggest narrowing scope — and occasionally it will suggest not building something yet. That's part of the point.
If this raised questions, confirmed a few suspicions, or helped you articulate the problem more clearly, that’s a good outcome.
You can save it, send it to yourself, or open a workspace to continue the conversation with us.
There’s no sales script waiting on the other side — just a friendly conversation about whether something like this is worth building, and what it would take to do it responsibly.
Good fit
- Founders with real problems they plan to own
- Teams building things people will rely on
- Agencies delivering complex or high-risk systems
- Operators who want software that doesn’t quietly become a liability
Not a fit
- Pitch-deck validation
- “Just add AI” experiments
- Projects where it’s okay if things break later and no one notices
Everything here is built the same way we build client systems — with clear inputs, bounded behavior, explicit assumptions, and long-term ownership in mind.
If that resonates, you’re in the right place.