The data ingestion, the calculations, the regulatory monitoring, the reporting — handled by AI agents. So your team can focus on what actually moves the needle: strategy, reductions, and impact.
The people who should be designing a company's transition to a low-carbon economy are instead buried in spreadsheets, chasing supplier data, and reconciling emission factors at 11pm.
CSRD. SEC Climate Disclosure. ISSB. GRI. CDP. The regulatory surface area keeps expanding, and headcount stays flat. Every new framework means more data to collect, more narratives to write, more edge cases to interpret. Most sustainability teams are running a Fortune 500 programme with the tooling of a startup — spreadsheets, email chains, and heroic individual effort.
We think there's a different model. Not AI as a feature bolted on to existing workflows. AI as the foundation — an operating environment where specialised agents handle the operational drudgery, and the sustainability team focuses on what only humans can do: setting strategy, driving reductions, and telling the company's sustainability story.
The core insight from projects like Mercury OS isn't aesthetic — it's structural. When your team's primary collaborators are AI agents working across dozens of domains simultaneously, the traditional SaaS model breaks down. You don't need a sidebar with eight sections. You need an adaptive environment that surfaces the right context at the right moment, scales from oversight to detail fluidly, and treats agent output as a first-class interaction pattern.
This means rethinking the basics. Navigation becomes intent-driven, not menu-driven. Status is ambient, not buried in tabs. Review and approval are native interactions, not afterthoughts. The whole thing is designed around a single question: what does a sustainability team need when they're directing a fleet of autonomous specialists?
"Checking in on the class." — Inspired by the organisational model in Gas Town: your agents have been working in the background. Here's what they did, how confident they are, and what needs the team's attention.
The platform maintains a team of specialised agents — a data ingestion agent, a calculation engine, an anomaly detector, a regulation monitor, a report drafter, a stakeholder researcher. Each has a presence the team can inspect. The interface adapts to show the right level of detail: a high-level stream when scanning, full context when drilling in.
Click any agent above to drill into their work. The key idea: the team maintains oversight without micromanagement. The agents do the work. The team checks in when it suits them.
"Insights that need a human." — Agents work autonomously, but they know what they don't know. When they hit something that needs judgement, they surface it — with full context and a recommendation.
These aren't buried in a queue you have to remember to check. The OS surfaces them based on urgency and relevance. Each one tells you: what happened, why it matters, what the agent thinks, and how confident it is. You approve, reject, or ask for more context. The interface is built for this loop — it's the core interaction between you and your agents.
"The trading floor for reductions." — Inspired by Composer.trade. Build reduction strategies, model their impact, compare scenarios, then deploy the best one and let agents monitor the results.
What if you switched your APAC shipping from air to sea freight? What's the emissions impact? What does it cost? How does it align with the IMO's decarbonisation trajectory? What about grid greening assumptions — does the maths change if Vietnam hits its renewable targets?
The team describes the initiative. The system models it. They compare, adjust, and commit. Then agents go collect real data to confirm or disprove the hypothesis. A continuous loop between strategy and evidence.
The magic is the feedback loop. The team commits to the experiment, and agents start monitoring actual shipping data against the model. If reality diverges from projection, they'll know — and they'll know why.
"The automated researcher." — Inspired by Listen Labs. CSRD double materiality assessments require qualitative input from across the business. What if an agent could gather it for you?
The research agent gets dispatched with context — "I need input from regional facility managers on physical climate risks for our CSRD double materiality assessment." It crafts contextual questions, sends them asynchronously, follows up on vague answers, and synthesises everything into themes with citations. Every claim traces back to a source.
What used to take weeks of scheduling, chasing, and manual synthesis becomes a process you dispatch and check in on. The output isn't just a summary — it's report-ready prose with citations back to every interview and document.
Reporting isn't a separate activity. It's an emergent property of the system. Because agents are continuously ingesting, calculating, and gathering — the report is always up to date.
When a regulation changes, the monitor flags it. The drafter updates the affected sections. The team reviews. The operating system keeps the whole pipeline connected — agents feed into the report continuously, and the team curates and approves.
Sarah Chen is VP of Sustainability at Meridian Consumer Goods — a Fortune 500 CPG company with global operations and upcoming CSRD obligations. Here's what her Monday looks like when her team is backed by an AI-first platform.
Sarah opens her laptop. The platform shows her what her agent team did overnight: data from 3 new suppliers was ingested and calculated, Scope 3 Category 1 is now 94% complete, and one anomaly was flagged. Everything she needs to know, surfaced by priority.
She taps the flag. APAC upstream transportation is up 47%. The agent already investigated — it was October port congestion causing a temporary air freight spike. It recommends accepting the data and noting the one-time event. Sarah approves in two taps. Three minutes, done.
The air freight spike sparks an idea: what if Meridian permanently shifted APAC shipping to sea freight? She opens the simulation space, builds the scenario, and sees the projection — 12,400 tCO₂e reduction, $2.1M net savings, 8-14 days added lead time. She overlays the IMO trajectory. Saves it for Thursday's board presentation.
She checks on the research agent gathering input from facility managers for the double materiality assessment. Eight of twelve responses are in. Three themes synthesised. Two follow-up questions queued. She approves the follow-ups and moves on.
The CSRD report is at 87%. Two sections need review. She flags them for her analyst to pick up after lunch. By 8:30, she's done with operational triage and preparing for her board presentation on science-based targets — the strategic work that actually drives reductions.
None of the components here are science fiction. Automated data ingestion, intelligent anomaly detection, simulation-based strategy, AI-conducted research, always-current reporting — all within reach of current technology.
The hard part isn't capability. It's design. It's imagining the right operating system — the right interaction model between a sustainability team and a fleet of agents. An interface built for oversight at scale. Strategic where others are operational. Adaptive where others are rigid.
This is a first sketch of that imagination.