How it works
Rigline runs on warehouse extracts you already produce and answers cross-system questions with governed definitions. Powered by Wisdom AI.
Warehouses, operational systems, GIS, file stores, and the documents nobody modeled. No migration, no rip-and-replace.
Peloton
Quorum
Esri
Snowflake
Databricks
Enverus
BigQuery
SharePoint
PostgreSQL- Warehouse and database extracts — Snowflake, Databricks, Oracle, SQL Server, Postgres
- Operational systems — Peloton, Quorum, Esri, Enverus
- Documents — leases, DOs, JOAs, AFE packets, DDRs, scanned records with OCR
- File stores — SharePoint, Google Drive, Box, S3
Implementation note
Cross-table KPIs — lifting cost across AFE, production, and LOE — are knowledge plus reviewed queries, not one metric object. Joins, grain, and IDs as strings are the work.
Why the answers hold up
Rigline runs on a semantic layer built from your systems: table grain and keys, effective dates, metric definitions, entity mappings between land, well and cost master data, and the documents behind each figure. Definitions are written down, reviewed by your team and version-controlled — the same question returns the same number for everyone who asks it.
Data model
Grain, join keys and effective dates mapped across land, well, AFE and revenue master data — daily cost never sums against monthly volume.
Metric definitions
HBP tests, NPT categories, cycle-time boundaries and WI/NRI treatment defined once, owned by your team, applied to every query.
Document evidence
Leases, JOAs, AFEs and DDRs indexed alongside the data, so a clause or report can be cited next to the figure it explains.
Audit trail
Every answer returns the generated SQL, row-level drill-through, source tables and the assumptions applied.
The context engine
A language model can write SQL. It cannot know that your allocation run is monthly, that Bar-M is held by a 1998 amendment, or that your team counts rig-move hours separately. Accuracy comes from the context layered on top of your data.
It knows how your tables actually join
Grain, keys, effective dates and the string-typed well IDs that never line up between systems.
- Well / lease / unit identifiers reconciled across systems
- Daily vs monthly grain declared, not guessed
- Effective-dated ownership and AFE revisions
- Nulls, re-allocations and prior-period adjustments handled
Without this context
Sums monthly volumes against daily costs and reports a lifting cost that is 30x off.
With Rigline
Aligns to the monthly allocation run and states which run it used.
Context is reviewed by your people and improves with every correction · illustrative example UI
Every role on the same system
One system · every role
Analysts, field engineers, operations, land and executives ask different questions of the same governed definitions. Nobody rebuilds the number, and nobody argues about whose version is right.
They ask
“Why did lifting cost move in the Delaware last quarter?”
- Reads and edits the query behind any figure
- Promotes a reviewed query so the rest of the org reuses it
- Stops rebuilding the same extract for four different teams
Where they work
Chat + reviewed queries
Shared underneath
One context layer — joins, grain, IDs, synonyms
Governed metric definitions with named owners
Permissions carried through to every answer
Same definitions, different surface per role · illustrative example UI
Value in as little as two weeks
Digitz brings the upstream and midstream side — the land, drilling, and production knowledge plus the data engineering behind your extracts. Wisdom AI brings the context engine and the natural-language surface. Together that removes the two things that usually stall a rollout: nobody has to explain the business, and nobody has to build the platform.
Days 1–3
Connect
Point at warehouse extracts and document stores you already produce. Read-only, no migration.
Days 4–9
Define
Digitz loads the upstream and midstream context — joins, grain, WI/NRI, AFE and LOE definitions — and reviews the answers with your team.
Days 10–14
Use it
Land, drilling, and ops start asking their own questions, with citations back to the source row.
Timeline depends on extract access and data quality · illustrative
Wisdom AI in the field
The engine underneath Rigline is already running drilling operations at global scale.
Case study · ConocoPhillips
Drilling teams answering their own questions across 14 countries
The data infrastructure was already there — WellView, Snowflake on Azure, Power BI. The problem was that every routine question about well status, event history, or job performance still needed an analyst or a SQL query, and the operational guidance lived in thousands of pages of drilling manuals nobody could query. After a six-month evaluation of leading AI vendors and an in-house build, Wisdom AI was the only one accurate enough for engineers to trust.
50%
accuracy improvement over the closest competitor
2,000+
engineers with self-serve access to drilling data
14
countries running on one context layer
Structured drilling data and unstructured manuals answered in one workflow, on a direct warehouse connection. Source: Wisdom AI customer case study.