Commodity-native data foundation

From fragmented data to trusted answers

Fragmented sources

  • Vendor feeds
  • Internal systems
  • Files
  • Spreadsheets
MeteorIQOne traceable hubLineage at every step

  1. Without MeteorIQ: Data scattered across feeds, systems, and spreadsheets

    With MeteorIQ: Feeds, files, and systems connected in one place

  2. Without MeteorIQ: Re-cleaning the same data before every analysis

    With MeteorIQ: Normalized once, reused across the desk

  3. Without MeteorIQ: Problems first surface at the end-of-day close

    With MeteorIQ: Caught before they reach the close

  4. Without MeteorIQ: Asking a colleague which system holds a dataset

    With MeteorIQ: One tag search away

  5. Without MeteorIQ: A pipeline ticket for every new question

    With MeteorIQ: Compose analytics from QueryBricks, no ticket

Available today

AI-ready · On the roadmap

Your deskIn the tools you use
  • Excel add-in
  • Dashboards
  • Alerts
MeteorIQ AITalk to your dataFor example: Why did the Brent–WTI spread widen?

Sources, lineage, checks

Agents & systemsAPI and agent access
  • AI agents
  • APIs & apps
  • Warehouses
  • Python SDK

The trusted data foundation for commodity teams, and for the AI agents that will work alongside them. One traceable workflow connects access, enrichment, exploration, and quality, so every answer shows where it came from.

Request a demoSee what ships today
Why this matters

The question is rarely the hard part. Getting the data ready is.

Commodity teams lose time in the handoffs between fragmented sources, manual preparation, and the systems where decisions are made, and lose confidence when no one can vouch for the data underneath.

01

Data is disconnected

Vendor feeds, internal systems, files, and spreadsheets each name, measure, and timestamp things their own way, so combining them is a project before it is an answer.

On the desk
  • The same contract carries a different code in each system
  • Vendor, internal, and spreadsheet versions of a price disagree
  • No single place shows what data the desk already has
02

Preparation is repetitive

Cleaning, joining, and reshaping consume the time meant for analysis, and most of that work is redone for every new question.

On the desk
  • Every analyst maintains a private copy of the data, with private fixes
  • Critical logic lives in workbooks only their author understands
  • New questions queue behind data-engineering work
03

Answers arrive too late

Every manual handoff adds delay. By the time a view is ready, the market it was meant to inform may have moved.

On the desk
  • Decisions get made on yesterday's numbers
  • Analysis follows what is quick to get, not what matters most
  • Results are hard to reproduce or explain after the fact
04

Models can't be trusted

Forward curves, forecasts, and risk and AI models inherit every flaw in their inputs. When data isn't vetted and monitored, a bad input flows straight through to the decision.

On the desk
  • A late or missing file goes unnoticed until a number looks wrong
  • No one can trace a result back to the data that produced it
  • AI agents act on bad inputs as confidently as good ones
How MeteorIQ helps

One connected path from question to action

The work that normally hops between tools, tickets, and teams happens in one system, from the first question to the action it drives.

All inside MeteorIQOne system from question to action, with no handoffs between steps

  1. ExampleWhy did the Brent–WTI spread widen this week?

  2. ExampleSettlements, broker curves, and FX, ready to query

  3. ExampleContract codes, units, and time zones aligned

  4. ExampleThe spread charted against stocks and freight

  5. ExampleEvery point traced to its source and checks

  6. ExampleShared to the desk's dashboard and Excel

Trust runs through every step
  • Data qualityFreshness, completeness, and validation
  • Full lineageSource and transformation history
  • Explainable contextThe reasoning behind each result
Available today

What you get on day one

Everything here is already running in production. Pick a capability to see it at work.

01 / 05

Data quality monitoring

Alerts on ingestion failures and data issues before end-of-day, so an ETRM close is not the first place a problem shows up.

Conceptual illustrations
Business impact

Less data overhead. More room to think.

The value is not another dashboard. It is giving commodity teams more time, earlier signals, and clearer confidence in the decisions they make.

Faster analytical iteration

Test an idea the morning it comes up, not after the next report cycle.

Less manual preparation

Analyst hours go into the analysis, not into rebuilding the same extract every week.

Earlier quality signals

Catch a stale feed or a failed check before it reaches a position, a report, or a client.

Analytics becomes part of the desk's daily rhythm, not a separate technical project.

Why MeteorIQ

Built by the desk, for the desk

MeteorIQ was born in the fires of the commodities trading industry. Our leadership has sat where you sit — which is why the platform is built around how trading, analysis, and risk actually work.

01

Commodity-native

The language, transformations, and workflows begin with commodity-market problems rather than a generic analytics model.

02

In production

Live in production with an oil major after a successful proof of concept.

03

Configurable, not bespoke

Configuration and composable building blocks, not custom code, let the platform keep pace as your sources and questions change.

How we build

Three principles behind the platform

Eliminate the friction between a question and its answer. Everything below follows from that.

01

Trustworthy

Without a trusted foundation, every layer above it inherits the cracks.

  • Automated validation, anomaly detection, and data reconciliation, so data meets a standard before it flows downstream
  • Trace a number back through the transformations and enrichments that produced it
  • Context alongside the answer, so you can see not just what changed but why
02

Instantaneous

Compress the whole data-to-insight lifecycle into a single step.

  • Normalized data streams available by default, with no ETL project in between
  • Reshape and refine as you go, rather than queuing a request and waiting
  • Work that used to take days of analyst effort collapses into the length of a conversation
03

Seamless

A layer across your existing stack, not another destination to log into.

  • Built for traders, risk managers, compliance, and quants — not only for data teams
  • Meets your systems where they are: ETRM and CTRM, ERP and back office, Python and R, and the AI agents you deploy
  • And Excel, because that is where a great deal of commodities analysis actually happens
Where we're headed

Build the foundation first. Add intelligence deliberately.

AI and advanced analytics are only as good as the data beneath them, so each phase builds on the one before.

  1. Available today

    Foundation

    Dependable, normalized data with quality monitoring, search, and analysis on top.

    See what ships today
  2. In developmentQ1 2027

    Acceleration

    Open the foundation to your systems, your code, and your AI agents.

    • API and agent access
    • Warehouse and streaming integrations
    • First-party Python SDK
    • Extensible QueryBricks language with user-contributed bricks
  3. ThenQ3 2027

    Intelligence

    AI that helps your team find and query the data in plain language.

    • Natural-language search
    • AI-assisted queries
    • Collaboration integrations
    • Customer-owned extensions
  4. LaterQ4 2027

    Autonomy

    A platform that tunes its own checks and surfaces insights before you ask.

    • Adaptive data-quality tuning
    • Proactive, AI-driven insights

Future-looking items describe our current direction and may change. Timelines are estimates, not commitments.

How we collaborate

Start with the problem. Build toward the value.

A focused engagement keeps the work grounded in a question your team needs to answer, then grows from what it proves.

01

Listen

Understand the decision, data, and friction that matter most to your team.

02

Focus

Choose a bounded use case with a clear path to operational value.

03

Prove

Put a working solution in front of real users and measure what changes.

04

Evolve

Extend what works as new questions, sources, and teams come into scope.

30-minute walkthrough

Bring a question you can't answer quickly today

Request a demo