How it works

The platform

Rigorously Answer the Question: "What if we did X?"

Reason about a future that looks different from the past. Weeks of modeling in hours. Critical insights at a fraction of the cost.

Our APPROACH

Reasoning Beyond LLMs

LLMs

LLMs have memorized a lot of text but don't understand how the world actually works. They are designed to predict the next token and can't reason beyond the data they were trained on. They fall apart once they do. This makes them inherently backward-facing and limited.

Neuro-Symbolic AI

Neuro-symbolic AI (LLMs plus math and logic) and causal reasoning techniques allow Anthos to codify assumptions about how the world works into equations. This allows us to reason forward, beyond where deep learning can, toward a future that looks different from the past, and rigorously ask "What if we did something that's never been done before?"

THE PROCESS

Systematize Your Deepest Thinking

STEP 1

Pathfind: Build a graph

Start with the decision you need to make, and Anthos maps the factors that shape it and how they affect one another. AI agents run deep research across published literature and data, and the domain experts on your team add what only they know.

The result is a visual map of the system your whole team can see, challenge, refine and align on.

STEP 2

Model

The map becomes a mathematical model build in Anthos's proprietary math-based language AMSL, with each relationship in the graph defined by an equation. Once converted, models are hallucination-free.

Hidden assumptions become explicit, and you can run scenarios to see how different decisions play out over time, where the risks and tradeoffs sit, and which levers change the outcome.

STEP 3

Calibrate

The model is tested against data and expert judgment. Where does it match reality? Where does it drift? Which assumptions need to change? Each round of calibration makes the model more reliable.

STEP 4

Audit

Every connection is cited and every assumption is annotated, so anyone can see why the model says what it says.

All of Antho's models are fully auditable. See a number, dig in to better understand where it came from. Experts review the structure, challenge the pathways, and sign off before the model informs a real decision.

STEP 5

Publish

Publish the model so colleagues, partners, and stakeholders can inspect it, explore scenarios, and add their own expertise.

Coming Q4 2026:‍

API suppor to integrate Anthos in your tech stack.
MCP support for Claude and Chat GPT.
Sharable models that team members or clients can quiery via the agent.

EXPLORE USE CASES

Decision Infrastructure

Models Built to Drive Decisions

Every Anthos model is built around a real decision. Once that decision is made, results from the intervention flow back into the model, so it grows sharper and more valuable with every choice your team makes.

Model

Map the system behind a decision: the drivers, the feedback loops, and the assumptions, all in one auditable causal model.

Decide

Run scenarios, weigh tradeoffs, and choose the intervention most likely to change the outcome.

Act and Measure

Put the decision into practice and capture what actually happens as the intervention plays out.

Learn and Optimize

Bring the intervention data back into the model to recalibrate its assumptions to inform the next decision.

Results feed back into the model. Every decision makes the next one better.

How is this different from an LLM and a spreadsheet?

Framing, not just filling in.

From software engineering to slide decks, language models are good at filling in the details and weak at structuring and framing a problem. The platform and its agentic harness are built for that gap. You structure the graph and make the key modeling decisions, using the agent as a thought partner rather than an oracle.

Review is the real work

AI can generate an enormous amount of content, and it is usually too much. The work that matters is the review. Anthos is built around review and iteration, so what comes out the other side is sharp, clean, and simple.

Transparent auditability

Any number can be traced back through the equation, the parameter, and the source that produced it. Nothing rests on a claim you cannot open up and check.

Governance at the organization level

Review workflows, approvals, and shared standards, so a model represents the institution's position rather than one analyst's afternoon.

A systematized path from zero to model

Not a blank canvas and good luck. A repeatable process that takes a team from an unstructured problem to a calibrated, published model.

FOR INDIVIDUALS

Get Early Access

Be among the first to build, test, and share causal models on Anthos. Join the early access list and we'll let you know when spots opens.

REQUEST EARLY ACCESS

FOR ENTERPRISE

Bring Us Your Hard Questions

Facing a high-stakes decision that are holding your organization back? Work side by side with our team to build models you can trust.

INQUIRE ABOUT A PILOT