I built it. Then I left. And I watched it die.
Not immediately — these things never die immediately. First the reports stop getting updated. Then someone builds a workaround spreadsheet. Then the workaround spreadsheet becomes the system. Then a new VP arrives, asks why everything runs through a spreadsheet, and someone in the back of the room says "yeah, there was a tool, but we're not sure how it works anymore."
I've been on both sides of that sentence. I spent years in investment banking and consulting watching operators struggle to see their own business clearly — not because they lacked the data, but because nobody could get it all in the same room at the same time. I was the person who knew how the Aries output connected to the accounting accrual. I maintained the Spotfire environment, the SQL databases behind it, and the Excel macros that staged the data before it got there. I knew which Tableau workbook fed which slide deck, which NAV model was current, and why there were seventeen copies of it on the shared drive with names like Final_v3_REVISED_USE THIS ONE.xlsx.
I was, in the language of the industry, the key man.
Key men leave. That's why we built Tauris-AI.
Your Systems Are Fine. The Space Between Them Isn't.
The tools you are running are largely the right tools. Enverus, Aries, PHDWin, ComboCurve, FieldDIRECT, Peloton, ogPumper — these are the systems of record your reserves auditors, your JIB partners, other business units, your board, and your lenders expect to see. They aren't going anywhere, nor should they.
But here is what nobody mentions when you sign those licenses: you are also paying for the problem, not a solution.
Software in this industry has gotten extraordinarily expensive. By the time you add up your engineering platforms, your market data subscriptions, your SCADA systems, your visualization tools, and your Microsoft stack, you are spending serious money every year — before a single line of code gets written or a consultant walks in the door. And yet despite all of that, the question "what is our all-in LOE per BOE on the Permian assets net of hedges this quarter?" still takes four people, two days, and a spreadsheet nobody fully trusts.
That is not a tools problem. That is an orchestration problem. We work with industry to solve this. Keep your tools, let us connect them.
Tauris-AI is agnostic to which systems you run. We are built for plug and play as your business changes — not tied to your current stack, not dependent on which platforms you were running from day one. If a better tool comes along, if an acquisition brings a different system into your environment, if your team simply outgrows something — swap it. The workflow stays intact. The logic you built doesn't walk out the door with the old vendor.
It isn't the tool. It's the workflow.
Don't Be Held Hostage to a Preference
Every executive in this industry has sat through a version of this meeting: two senior technical people, forty-five minutes, one whiteboard, and a deeply felt disagreement about whether the team should be running Tableau or Spotfire. Meanwhile the actual business problem sits unresolved.
Tool preference is a people problem dressed up as a technology decision. Operators pay for it twice — once in the fees to build around whichever tool wins the argument, and again when that person leaves and takes all the context of why it was built that way with them.
You are already paying a fortune to run best-in-class software. The last thing your business needs is to be held hostage to someone's comfort zone — or to rebuild everything from scratch because the industry shifted from one platform to another.
Change when your business requirements demand it. Not because someone prefers Tool A over Tool B. With Tauris-AI, that is finally a real option.
The Graveyard Nobody Talks About
Most firms are sitting on a graveyard of orphaned work. The Spotfire environment that cost hundreds of thousands in consulting fees and worked beautifully until the SOW ended. The SQL database an engineer built to reconcile SCADA volumes against production accounting — precise, completely undocumented. The Excel macro suite that automated the monthly close package, the AFE tracking, and the variance commentary until the analyst who wrote it moved on. The Tableau and Spotfire files, Word and Outlook automation that routed files and generated reports. The PowerPoint templates wired to live data that now throw errors every quarter-end. The NAV model with seventeen versions and no clear record of which assumptions produced which output.
These weren't failures. They were smart solutions built by good people under real business pressure. The failure was that there was never a proper home for them. No version control. No way to hand them off. No environment where someone else could pick them up and keep going.
When the key man left, IT inherited something they didn't build, don't understand and couldn't maintain. Everyone else went back to doing it manually. All that work quietly went to zero.
The Numbers Have to Hold Up
There is a second problem that sits quietly alongside the key man problem, and it surfaces at the worst possible moments — audit season, a reserve report deadline, a divestiture data room.
The numbers have to be consistent, defendable, and traceable back to their source.
Two engineers run the same LOE calculation and get different results. A buyer's team asks for a 36-month reconciliation of production volumes against accounting records during a divestiture. Your reserve auditor wants to know why this year's type curve assumptions differ from last year's. They happen every reporting cycle, and when they do, the answer is almost always the same: someone has to go find the database or spreadsheet, figure out which version is current, and manually reconstruct the logic that produced the reported number.
The major accounting and advisory firms are explicit about this:
- Deloitte states that AI-generated outputs in finance and accounting "must meet stakeholder expectations for accuracy, reliability, and trustworthiness," and that organizations must be able to demonstrate why their confidence in those outputs is justified — not just assert it. (AI in Finance and Accounting: Data Transparency and Management)
- PwC frames this as a governance requirement: AI governance must address "how explainability and data lineage are addressed — particularly when outputs feed into financial reporting or external disclosures." (Responsible AI and Audits)
That is what due diligence automation actually means in this industry. Not just getting to the answer faster. Getting to an answer you can defend.
Built on a Clean Slate
We built for the problem as it exists today — the real stack firms are running, the real workflows that break down between systems, and an orchestration layer without forcing you to change how your team works.
We also know what it costs to run this industry's software. Tauris-AI is priced competitively. The value shows up in the first month-end close where nobody has to spend three days reconciling numbers between systems.
What This Looks Like in Practice
Your accounting team pulls forward accruals from your reservoir engineering platform directly — no waiting on an engineer to run a report. Your land team sees production performance alongside lease obligations in the same view. IT has visibility into the business-built tools they are asked to support — the macros, the queries, the automated decks — in an environment where those things are actually documented and maintainable. Your NAV models have a version trail so it's always clear which assumptions drove which outcome. And when an auditor, a lender, or a buyer asks where a number came from, the answer is already there.
And when someone leaves — because someone always leaves — the work stays.
That is what we built. That is why we built it.
If any of this sounds like your organization, we should talk.
We are preparing a brief that goes deeper on workflow integration across engineering, land, accounting, and IT. Reach out here if you'd like to be notified when it's available.