Questions about Cogzia.

Cogzia is the last-mile decision layer for upstream oil & gas teams. It connects the data and analysis already living in systems like ARIES, ComboCurve, PHDWin, Enverus, LOS, well lists, Excel, PDFs, production data, ownership records, and reserve reports — then runs governed, source-traceable, repeatable workflows across all of it. Cogzia does not replace the tools your team uses; it sits above and between them so decisions can draw on every source at once.

No. You can talk to Cogzia in plain language, but the point isn't the conversation — it's the output. Cogzia runs workflows across your data and produces reusable artifacts: reconciliations, valuations, maps, charts, audit packets, and evidence trails. The chat is the steering wheel; the artifact is what you leave with.

No. Dashboards are built to monitor metrics you already know to track. Cogzia is built for the decisions where you don't yet know the answer — reconciling sources, resolving conflicts, asking follow-ups, quantifying impact, and producing a defensible result. Cogzia can render charts and maps, but the value is the governed workflow and evidence behind them.

No. Cogzia is not a place to store data and doesn't replace a warehouse, lake, or MDM system. It reads from the infrastructure you already have and turns it into decisions — reasoning across sources, reconciling inconsistencies, preserving evidence, and producing decision-ready analysis.

Cogzia is not a replacement for ARIES, ComboCurve, PHDWin, Enverus, Excel, your GIS tools, your data warehouse, or your developer tools. It is not a generic chatbot, a BI dashboard, or a data lake. It is the governed, source-traceable layer where those tools, your data, and AI reasoning come together to produce trusted upstream decisions.

Upstream oil & gas is our focus — the platform comes pre-loaded with the data formats, calculations, and reporting that O&G work requires. It also serves mineral-rights teams, who work much of the same data. More broadly, the engine is industry-agnostic: the same approach — connecting fragmented sources, running deterministic analysis, and tying every number back to its source — applies wherever high-stakes decisions are buried in fragmented, regulated data, including energy finance and A&D / PE diligence. Oil & gas is where we start, not the limit of the architecture.

Cogzia is for upstream oil & gas organizations making high-stakes decisions from data spread across many systems, documents, and spreadsheets — reserve and reservoir engineers, A&D teams, finance and technical accounting, asset and operations teams, and the data or AI teams supporting them. Typical work includes reserve reconciliation, PV10 review, acquisition diligence, ownership analysis, well-level economics, production surveillance, audit prep, and board or lender reporting.

The cross-system decision problem. Most operators already have strong tools — ARIES, ComboCurve, PHDWin, Enverus, Excel, GIS, internal databases — but high-stakes decisions rarely live inside any one of them. Cogzia connects those pieces into governed workflows so teams can reconcile data, trace evidence, find inconsistencies, quantify economic impact, and produce decision-ready outputs.

Structured, semi-structured, and unstructured upstream data: ARIES, ComboCurve, and PHDWin outputs, Enverus data, well lists, LOS, production history, ownership and lease records, reserve reports, Excel workbooks, engineering PDFs, maps, public data, and internal databases. Cogzia is built for exactly the case where the answer requires combining several of these at once.

Workflows that need cross-system reasoning, reconciliation, evidence, and repeatable analysis — reserve reconciliation, PV10 review, acquisition diligence and divestiture support, NRI and ownership checks, P&A liability discovery, shut-in and zombie-well analysis, well-level economics, decline-curve surveillance, document intelligence, and board, lender, or audit-ready packages. The best fits are high-value, repeatable, and hard to run reliably inside a single tool.

Yes — that's the point. You don't wait for a vendor to build each workflow. Describe the decision you need in plain language, and Cogzia builds and runs it across your own data, then saves it so you can reuse and audit it. New, asset-specific analyses don't require a new software project each time.

It's designed for real upstream data — fragmented, inconsistent, duplicated, and spread across formats. Rather than assuming a clean foundation, Cogzia inspects the data, surfaces gaps and conflicts, preserves assumptions, traces sources, and prioritizes issues by business impact. The goal isn't to pretend the data is clean; it's to make its real state visible and decision-ready.

No — it's often most useful when the data is imperfect. Many high-value oil & gas decisions have to be made before every source is harmonized. Cogzia surfaces the inconsistencies, organizes them, traces them, and quantifies their potential impact. Clean data helps any system, but Cogzia is built for the reality of upstream data environments.

It means the analysis is connected to its source data, calculations are traceable, workflows can be rerun, artifacts are preserved, and outputs can be reviewed — so teams always understand where an answer came from. That's the difference between Cogzia and a generic chatbot: it's built for enterprise decisions you need to trust, inspect, reuse, and defend.

Every output connects back to the underlying data, documents, calculations, and workflow steps used to produce it. In oil & gas — where reserve values, ownership percentages, PV10, operating costs, and P&A liabilities all get reviewed by multiple stakeholders — Cogzia lets you inspect not just the answer, but the evidence behind it.

High-stakes upstream calculations need to be consistent, repeatable, and inspectable. Language models are strong at reasoning, planning, and working with documents — but they shouldn't be the only mechanism behind a critical number. Cogzia routes calculations and reconciliation through controlled, deterministic tools, then uses AI for reasoning on top. The result combines AI judgment with computation you can re-run and verify.

Yes — where they add value: reasoning, language understanding, document analysis, workflow planning, and interaction. But Cogzia is not blind language-model output. For high-stakes work it pairs AI reasoning with deterministic tools, structured data operations, and source-grounded evidence, so you get the power of AI with the controls enterprise decisions require.

No. Cogzia does not use your data to train AI models. It runs commercial models under enterprise agreements that prohibit training on your data, and your data is used only to perform the work you ask for. Specific retention and model-use terms depend on your deployment and are available on request.

Cogzia tracks where each answer came from and when its source data was last updated, so teams can tell whether an analysis reflects current data or needs to be re-run. Because workflows are repeatable, you rerun them as new data arrives instead of rebuilding the analysis from scratch.

By preserving the connection between every output and the source data, workflow steps, calculations, assumptions, and artifacts behind it. For reserve reconciliation, valuation review, diligence, and board reporting, teams need to understand not just what conclusion was reached, but how — and Cogzia is built to make that evidence easy to inspect, rerun, and defend.

No. Cogzia produces the analysis, the evidence, and the economic impact — the people accountable for the decision make the call. It's built to amplify expert judgment and speed up the work behind a decision, not to replace the engineer, the A&D lead, or the executive who owns it.

Yes, Cogzia is built to work with private customer data under governed enterprise workflows. It's hosted in our GCP environment and connects to your systems over private paths — never the public internet. How your data is handled depends on the source: for some systems Cogzia connects and queries only the data a workflow needs; for others — like a full ARIES database or a VDR financial export — it ingests the data into an isolated, per-customer workspace for analysis. Enterprise deployments add SSO, role-based access control, and project-level separation. For current security documentation and deployment options, contact us directly.