Some firms are piloting AI. Some are chasing efficiency. Some are still deciding where it fits.
We put AI on the real problems of a trading day. The price that doesn't match, the feed that fails before the close, the upgrade that breaks something downstream. We didn't just monitor these. We built AI that resolves them, and it's changing what the trading day produces.
A frontier model knows everything, and your business in particular, not at all.
Point a general model at a trading operation and it is confident, fluent, and wrong. It has never seen a curve, a break, or a close. Give it the domain (the trade, the data beneath it, and the business workflows around it) and it stops guessing. AI built on that depth is the combination that finally moves the outcome, not just the workload. It is the one we operate in.
The model is the commodity. The domain is the moat.
Anyone can call an API. Almost no one can teach it what a mispriced curve means before the close, or which break of a thousand actually threatens the numbers. The intelligence is cheap now; the context is the rare part.
Connecting to a system is plumbing. Understanding what its data means is the work.
Plenty of firms will tell you they connected AI to your ETRM. That is a wire, not a result. Knowing that a deal booked one way and a deal booked another are the same trade (knowing which difference is noise and which one moves money) is a different order of problem. That is the line we work on.
Built where it has to work.
Not trained in a lab and piloted at you. Built inside a live trading operation, against real feeds, real breaks, and real deadlines. It arrives already knowing what production does to a clean idea.
It compounds, or it isn't AI.
Each run sharpens the next. Domain knowledge accumulates into the system (how this curve behaves, how that feed fails) while a generic tool resets to zero every time you open it.
Watch the day. Understand the trade. Act on it.
Three layers of applied AI, shown in the order we earned them: what runs in production, what we are building on live data, and what we have proven end-to-end. Each layer earns the right to the next. We run the systems, and the data beneath them, across all three.
Monitoring & anomaly detection
Domain-aware checks across feeds, models, data pipelines, and end-of-day, built to know what normal looks like for each, and to separate the break that matters from the noise that doesn't.
Intelligent triage & recovery
Classifies an incident, ranks probable cause from history, and recommends (or, under approval, runs) the fix. Every step carries its evidence and a full audit trail.
Self-healing & the automation loop
Recurring failures become automation, not repeat tickets. Each fix teaches the system, so the estate gets quieter over time, not louder.
Reconciliation intelligence
Matches on meaning, not fields. Today it validates a system before and after an upgrade, separating the 80% of differences that are expected from the 20% that matter. Tomorrow, across two different ETRM systems: one deal here, one there, and the AI knows they are the same trade.
Market-data & curve intelligence
Reads curves, marks, and feeds the way a senior desk analyst would, catching the value that looks valid but isn't, before it reaches a position or a report. The data discipline everything above it depends on.
Upgrade & configuration intelligence
Assesses what a release will break across years of custom code (readiness, impact, and configuration health) so the risk is known before the upgrade ships, not after.
Settlement, regulatory & contract intelligence
The business beyond the front office: settlement breaks classified and explained, regulatory checks run with evidence, contract terms read and understood, the workflows where errors turn into disputes and fines.
Governed agentic workflows
Intent to audited execution: an instruction moves through discovery, evaluation, and action. Every decision logged, bounded by policy, and reversible.
Autonomous procurement & negotiation
Multi-agent negotiation across counterparties to a settled deal, demonstrated end-to-end on energy procurement, from intent to ledger in seconds.
Natural-language access
Ask the trading and data estate in plain language and get an answer with its evidence, without knowing which system holds the data or how to query it.
Where this goes next: agentic operations that run a workflow end-to-end under human oversight the close, the contract lifecycle, the recurring process measured on the outcome, not the hours.
Watch, understand, act: shown in the order we earned them. None of it started in a lab.
Built inside a European energy trading environment. Anonymised by design. We go specific in conversation, under NDA.
AI you can put near the money.
AI earns its way into live trading the way a new hand does: supervised first, trusted by evidence, never handed the keys on day one. In a business where a wrong number is a real loss and a regulator is always watching, the discipline is not a disclaimer. It is the product.
It runs inside your security boundary.
We work within your perimeter, your governance, and your toolchain. Data stays in your environment. Access is least-privilege and scoped. Nothing is exfiltrated to train something elsewhere. ISO 27001 certified, and built to operate under the controls a trading business already lives by.
Your sensitive data is protected before a model sees it.
Commercial values, positions, and personal data are tokenised or masked before they reach a model. The reasoning happens on protected data, not raw secrets, so you get the intelligence without exposing what must not leave the room.
Grounded, not guessing.
A model that invents a number is worse than no model. Every answer is tied to its evidence no source, no answer. Reasoning is bounded by deterministic rules and your business logic, validated against them rather than trusted on faith. Low confidence routes to a human. The system is built so it cannot confidently assert what the data does not support.
Assistive, logged, reversible.
AI recommends; people decide. Nothing touches production without explicit human approval, or a deterministic policy rule and a full audit trail behind it. Every action is attributable and reversible.
Autonomy is earned, not assumed.
AI takes on more only after it has proven itself against a measured baseline. More trust follows more evidence, granted by results in production, never by ambition on a slide.
Built by people who know both sides.
The combination the work demands (people who know the trade and can build the AI) barely exists in the market. We grew it: engineers who spent careers inside trading systems and data operations, retrained as AI engineers, working alongside a firm being brought to the same standard. A deepening bench, not a single lab team, with capacity to take on problems well beyond the ones already running.
We do not ship vanity. That discipline is why more than fifteen capabilities run in production today, with more in build and a long line behind them, and why the ones that run, work.
What you see live is not the limit of the team. It is the proof that when a new problem arrives (in trading, in data, in the workflows around them) there are people here who can build the answer, and the judgment to know whether it earned its place.
This works because we run the systems, and the data, underneath it.
AI is the top of a ladder, not a bolt-on. It is honest because the data beneath it is trustworthy, the discipline we run on the data side, where a number is proven before anything reasons on it. It is credible because we have run the trade for years. Take away the foundation and it is just another demo. We did not start with AI. We arrived at it.