Industrial & Energy · Exploration workflows
AI ENGINEERING
An expert-only capability, now in every scientist's hands
6–8 hours
returned to each scientist every week.
Industrial & Energy · Exploration workflows
AI ENGINEERING
An expert-only capability, now in every scientist's hands
6–8 hours
returned to each scientist every week.
Industry
Fortune 500 energy-services company, publicly traded.
Scale
45,000+ employees, $22B revenue, 70+ countries. Users: exploration scientists who build specialized data-processing workflows that turn raw field recordings into subsurface images, on terabyte-scale survey data.
Engagement
Implementation: a self-hosted natural-language copilot in front of the client's processing engine.
Industry
Fortune 500 energy-services company, publicly traded.
Scale
45,000+ employees, $22B revenue, 70+ countries. Users: exploration scientists who build specialized data-processing workflows that turn raw field recordings into subsurface images, on terabyte-scale survey data.
Engagement
Implementation: a self-hosted natural-language copilot in front of the client's processing engine.
Industry
Customer-experience technology company, venture backed.
Scale
45,000+ employees, $22B revenue, 70+ countries. Users: exploration scientists who build specialized data-processing workflows that turn raw field recordings into subsurface images, on terabyte-scale survey data.
Engagement
Implementation: a 6-month embedded build, core build of roughly 12 weeks with 8 to 10 engineers, plus onboarding and standby support.
Industry
Fortune 500 energy-services company, publicly traded.
Scale
45,000+ employees, $22B revenue, 70+ countries. Users: exploration scientists who build specialized data-processing workflows that turn raw field recordings into subsurface images, on terabyte-scale survey data.
Engagement
Implementation: a self-hosted natural-language copilot in front of the client's processing engine.
// THE PROBLEM
Where time went
Buying was a maze of forms, catalogs, contracts, approvers and budget codes, and most requesters could not navigate it. A standard buy took 5 to 7 business days of routing, catalog hunting and serial approvals, and people who could not navigate it bought off contract, eroding the pricing the company had negotiated. Buyers spent much of the week on routing and data entry, and purchases were often miscoded by commodity.
// THE BUILD
What we built with them
With the procurement team, LevelUp built a single plain-language front door for buying, orchestrated as a stateful, auditable graph. Describe the need in plain language; the copilot classifies it, surfaces the on-contract supplier and catalog item first, checks spend thresholds and budget as hard gates the model cannot talk past, resolves the approval chain, and assembles the requisition and purchase order through the ERP APIs. It proposes, a human signs off the spend, and every action is logged and replayable for audit.
// THE PROBLEM
Where time went
Buying was a maze of forms, catalogs, contracts, approvers and budget codes, and most requesters could not navigate it. A standard buy took 5 to 7 business days of routing, catalog hunting and serial approvals, and people who could not navigate it bought off contract, eroding the pricing the company had negotiated. Buyers spent much of the week on routing and data entry, and purchases were often miscoded by commodity.
// THE BUILD
What we built with them
With the procurement team, LevelUp built a single plain-language front door for buying, orchestrated as a stateful, auditable graph. Describe the need in plain language; the copilot classifies it, surfaces the on-contract supplier and catalog item first, checks spend thresholds and budget as hard gates the model cannot talk past, resolves the approval chain, and assembles the requisition and purchase order through the ERP APIs. It proposes, a human signs off the spend, and every action is logged and replayable for audit.
// THE PROBLEM
Where time went
Buying was a maze of forms, catalogs, contracts, approvers and budget codes, and most requesters could not navigate it. A standard buy took 5 to 7 business days of routing, catalog hunting and serial approvals, and people who could not navigate it bought off contract, eroding the pricing the company had negotiated. Buyers spent much of the week on routing and data entry, and purchases were often miscoded by commodity.
// THE BUILD
What we built with them
With the procurement team, LevelUp built a single plain-language front door for buying, orchestrated as a stateful, auditable graph. Describe the need in plain language; the copilot classifies it, surfaces the on-contract supplier and catalog item first, checks spend thresholds and budget as hard gates the model cannot talk past, resolves the approval chain, and assembles the requisition and purchase order through the ERP APIs. It proposes, a human signs off the spend, and every action is logged and replayable for audit.
// PROOF POINTS
5 to 7 business days to under 30 minutes
intake to requisition, for a standard catalog buy
5 to 7 business days to under 30 minutes
intake to requisition, for a standard catalog buy
An estimated 15 to 20 percentage points
on-contract spend up, several million dollars a year in recovered negotiated savings on a procurement base this size
An estimated 15 to 20 percentage points
on-contract spend up, several million dollars a year in recovered negotiated savings on a procurement base this size
Roughly 30 to 40%
of buyers' week reclaimed from routing and data entry for sourcing strategy and negotiation (estimated)
Above 90%
of requests correctly commodity-coded at intake, up from an estimated 60 to 70%
Roughly 6 to 8 hours a week
returned to each scientist, redirected into exploration and interpretation (estimated with the team)
Under a minute
workflow setup, where hand assembly took up to 15 to 20 minutes
Roughly 90%
of generated workflows ran correctly on the first try; a large share of hand-built ones had needed rework
Roughly 90%
of generated workflows ran correctly on the first try; a large share of hand-built ones had needed rework
Zero
proprietary data left their infrastructure
Zero
proprietary data left their infrastructure
STACK
Self-Hosted Open-Weight Models
Intent Routing
Grounded Retrieval Over The Client's Manuals
Validation Gate
Delivered as a self-hosted copilot inside their environment; the capability stopped being gated on a handful of experts, so more exploration runs in parallel.
// How we measured
Generation time from system logs against a timed manual baseline; first-run success across a benchmark set; hours estimated with the client's team.
// Build with confidence
Talk to us about testing your agents before launch.
// Build with confidence
Talk to us about testing your agents before launch.
// Build with confidence
Talk to us about testing your agents before launch.
// Build with confidence