We build the precision layer between AI and the real world.

ADG Precision makes sure that what AI decides is what should actually happen: understood correctly, checked against the truth, approved by the right people and recorded.

Models · ADG Precision

We started with Google Ads.

ADG Precision began by building agentic search-term governance for Google Ads — reading the intent behind every search, checking it against each campaign’s source of truth, and applying only changes approved by a person or permitted under human-defined auto-approval rules.

The missing piece of the AI stack.

Some companies teach AI to see. Others give it memory, or check that it follows the rules and stays secure. ADG Precision is the piece that completes the path from raw data to the right real-world action.

Elorian AI

Primary role: Visual reasoning

What is the system looking at?

Builds AI that reasons visually, turning raw pixels into an understanding of objects and space.

Graphon AI

Primary role: Connected knowledge and multimodal memory

How is this information connected?

Turns scattered documents, video, audio and data into lasting, connected memory a model can reason over.

Credo AI

Primary role: Enterprise AI governance

Is this AI safe, compliant and allowed to run?

Turns an organization’s rules and regulations into AI policies, risk reviews and audit trails.

ADG Precision

Primary role: Domain intent and action control

What is intended, and which exact action should happen now?

Reads the intent behind each request, whether it comes from an agent, a tool or a person, directs it to the right action and carries it out inside the guardrails, with every step recorded.

Others help AI see, know, comply and stay secure. ADG Precision makes sure it acts, exactly as intended.

Who builds each layer.

Each layer of the AI stack has its own specialists. Here are a few, grouped by the step they handle. ADG Precision works at the final step, where a decision becomes a real-world action.

See (Perceive and reason): Helps AI see

Elorian AI

Visual reasoning. Builds models that reason about images and video: what is there, how parts fit and what happens next.

World Labs

World models. Generates and simulates interactive 3D environments.

Know (Connect and remember): Gives AI memory

Graphon AI

Connected knowledge and multimodal memory. Builds a self-updating map of how documents, video, audio and images connect, and answers with sources.

Glean

Enterprise search and knowledge. Searches across a company’s apps and data, showing each person only what they are allowed to see.

Comply & Secure (Govern and secure): Keeps AI within the rules

Credo AI

Enterprise AI governance. Keeps a register of a company’s AI systems, with policies, risk reviews and audit evidence.

Zenity

AI agent security. Watches what AI agents do and blocks or modifies risky actions before they happen.

Act (Interpret and act): Turns decisions into real-world action

ADG Precision

Domain intent and action control. Checks that each action matches what was intended, then carries it out and records it.

Physical Intelligence

Robot foundation models. Turns camera input and language instructions into robot motion.

AUI Apollo-1

Agents for customer conversations. A neuro-symbolic model that lets customer-service agents complete bookings, payments and cancellations within company policy.

Palantir AIP

Knowledge to governed action. Models a whole business as connected data and actions. Agents propose changes, people approve them, and every action carries permissions.

Company names belong to their owners and appear only to illustrate each layer of the stack. ADG Precision is not affiliated with or endorsed by these companies. Descriptions summarize each company’s public materials as of October 2026.

Interpret data’s intent, then take action.

Palantir AIP starts from knowledge. It models the whole business, and a person states plainly what they want, such as “change shipment address.” Palantir makes sure that action is allowed, approved and logged. The intent usually arrives already clear.

ADG Precision starts from intent that isn’t clear. An ambiguous search term or an agent’s request has to be worked out first. Only then can we pick the right action. That middle step is the hard part.

AUI’s Apollo-1 works inside customer conversations, such as bookings and cancellations. ADG Precision works on the live systems behind a business, starting with Google Ads, where every search term has to reach the campaign built for its intent.

Knowledge platforms turn data into understanding. ADG Precision turns understanding into action.

Governance platforms set the rules for every AI system. ADG Precision checks each action against the rules and facts of its domain.

Security tools ask whether an action is safe. ADG Precision asks whether it is the right action for the intent behind it.

Grounded in knowledge. Verified before action.

Knowledge over guesswork

Decisions are grounded in structured knowledge, rules and context — not left to an unchecked model.

Approval before execution

People stay in control of what matters. Nothing consequential happens in a single unchecked step.

Traceable by default

Every action carries its reason, its approver and its result, so teams can audit and improve.

Designed for operators

We design for the experts who run the systems, because they know where precision matters most.

Multilayer intelligence. Precision across domains.

Search & Google Ads (SEM)

Decide which search terms to block, which to scale and where monthly budget should go — with every change approved before it reaches Google Ads.

Agentic commerce

AI agents now search, compare and buy on people’s behalf. A precision layer checks each purchase against what the buyer meant, the budget and approved vendors before any money moves.

Physical AI & robotics

Robots and autonomous systems turn spoken and written instructions into motion. A precision layer checks each one against the task, the environment and the safety rules before anything moves.

Graphs map the data. Ours maps the request.

A typical knowledge graph stops at the entity. A deep graph like Graphon’s goes many levels further into the data. The ADG Intent Graph takes the next step: it puts the request itself at the core and gives every word a place before any action runs.

A typical knowledge graph

Stops at the entity. Each word gets one tag, one level deep. Anything that is not an offering, brand, place or interest falls through, including what the person wants done.

A deep knowledge graph

Goes many levels below the entity, so agents can answer harder questions about the data. The request itself is not part of the graph.

The ADG Intent Graph

Starts from the request. Using the LINZ method, ADG splits it in two, again and again, until every word has a role: offering, brand, proximity, question, intent or interest. Then it picks one action and checks it.

The Intent Graph works in front of a deep knowledge graph, not instead of one. ADG works out what was asked, the knowledge graph finds the facts, and ADG checks the action before anything runs.