The critical context your operations and your AI need is never captured.
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?”
I don’t have any context on that asset.
Two costs, one cause. Nobody captured it. Petry is the layer that does.
Petry is the missing layer. It captures that context, structures it for your AI apps, and serves asset insights right where you work.
Messy sources in. Clean asset context out.
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.
Your daily work
The context engine
Clean asset context
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.
Nobody has to remember to capture. The engine does it for them.
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.
Microsoft Teams
Add the Petry app to a team or chat. The engine watches the stream and captures what matters, no exports, no copy-paste.
Slack
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.
Desktop agent
Sits quietly on your desktop. Drag a chart, file, or screenshot onto it and it lands on the right asset.
On demand
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.
Outside systems connect straight to it.
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.
PetryOne connection, org‑wide
MCP 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.
Your context, under your control.
Capturing the knowledge that runs your business only works if you can trust where it goes.
Traceable by default
Every answer carries its source. Nothing is a black box you have to take on faith.
Your knowledge stays yours
Your context graph is yours to keep and export, and it is never used to train public models.
Open models, your environment
Runs on open-source models hosted wherever your data must live, cloud, VPC, or fully on-prem. Nothing leaves your walls.
Ask your assets anything. Get an organized, audited answer.
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.
- Status
- Production down ~6% since the gas lift change.Spotfire review · Apr 12
- Last decision
- Switched to intermittent gas lift to cut loading.Ops call · Apr 9
- Open assumption
- Decline looks mechanical, not reservoir-driven.SME note · M. Reyes
- Evidence
- 3 charts and 1 well test, auto-linked to the asset.captured Apr 8 to 12
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.
The second brain every AI‑enabled business is missing.
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.
Context is the moat
Every team has the same models. What compounds is the connected, proprietary context only your business holds.
Agents can’t see your work
Workflows and agents can only reason about what reaches the prompt. Petry is the layer that feeds them the rest.
Proven on the hardest context
We started in oil and gas, where the operating context is messiest. The same scattered brain runs every business.