The decisions, observations, and SME judgment that keep assets running stay locked in calls, screenshots, and people’s heads. So your teams redo the same analysis, your new hires start from zero, and your AI tools guess.
“What changed on Well 12-H, and why?”
Two costs, one cause. Nobody captured it. Petry is the layer that does.
Petry is the pipeline between your day-to-day and your AI tools. Scattered context pipes in, Petry cleans, structures, and links it to the asset it belongs to, and a live stream of organized context flows out, ready for anything downstream to pull.
Pulled by ChatGPT, Claude, your agents via MCP
Structured intoDecisions · Evidence · Observations · SME insight · Assumptions · Outcomes
Docs and systems of record store what exists. Petry captures what happened, why it mattered, and who knew it, then streams it to every tool downstream.
Connect the places work already happens. Petry’s engine decides what is worth keeping, captures it, and files it to the right asset, so your team never has to think about it.
Add the Petry app to a team or chat. The engine watches the stream and captures what matters, no exports, no copy-paste.
Drop Petry into a channel. The engine picks out the decisions, evidence, and insight as messages flow.
Forward a thread once, or set a rule and forget it. Petry digests it and links it to the right asset.
Export the chat and drop it in. Petry digests the thread and links it to the right asset.
Sits quietly on your desktop. Drag a chart, file, or screenshot onto it and it lands on the right asset.
Want to be deliberate? Type /petry or drop a screenshot in, the moment something matters.
Everything lands in the same context engine, cleaned, structured, and ready for any AI tool to pull.
Give your organization access once, through MCP. The chats, agents, and IDEs your team already uses pull live asset context as they work, and your own applications query the API directly.
PetryMCP server and API, running inside your environment.
Teams ask for asset data in the chats they already work in.
Claude apps and agents pull asset context mid-task.
Builders get asset context without leaving the editor.
Internal agents and workflows query the graph as a tool.
Query the API directly and build products on the live stream.
Streamlined data stays inside your walls, so teams keep moving fast.
No integration project. Grant access once and every tool downstream gets smarter.
Capturing the knowledge that runs your business only works if you can trust where it goes.
Every answer carries its source. Nothing is a black box you have to take on faith.
Your context graph is yours to keep and export, and it is never used to train public models.
Runs on open-source models hosted wherever your data must live, cloud, VPC, or fully on-prem. Nothing leaves your walls.
Everyone is racing to put AI to work and skipping the part that matters, capturing the context. Petry captures it where your team already works, so you can ask it back and it keeps growing with the team.
Give your org access through MCP. Teams pull asset data in the chats they already use, builders ship apps on it or query the API directly, all inside your environment.
Every answer carries its source, so you can trust it, and trace it.
Models and agents are commodities. The connected context that makes them useful for your business is the moat, and it is the layer almost everyone skips.
Every team has the same models. What compounds is the connected, proprietary context only your business holds.
Workflows and agents can only reason about what reaches the prompt. Petry is the layer that feeds them the rest.
We started in oil and gas, where the operating context is messiest. The same scattered brain runs every business.
