Refined Completions transformed a stewardship report that once required 8–10 person-hours and multiple contributors into a repeatable workflow completed in approximately 30 minutes, including human review.

The result is not simply a faster report. It is a new operating model that gives technical experts more time to interpret results, advise customers, and identify the next opportunity for value.

For BloomifAI, the engagement also demonstrates what AI orchestration looks like in practice. Rather than adding another isolated AI tool, the project connected approved data, organizational rules, a defined reporting process, Agentic AI, and human review inside one controlled workflow. The technology supports the work from beginning to end while the customer’s experts remain responsible for the final judgment and customer-facing output.

The Customer

Refined Completions is an oil and gas completions chemistry company. Its team designs and supports frac chemical programs for operators, then remains engaged with those wells after the completion job is finished.

Its patented Pathfinder Prediction Process™ guides how chemistry is designed for each well, with an emphasis on long-term well performance. Stewardship reporting is a critical part of that commitment: each report helps customers understand what their wells are showing, what Refined Completions is monitoring, and what follow-up may be recommended.

These reports are more than internal documentation. They are customer-facing evidence that Refined Completions treated each well intentionally, remained engaged after treatment, and continues to monitor performance. A strong report can also begin the next account conversation by translating technical observations into clear follow-up actions.

Every stewardship report is designed to answer three practical customer questions: What are the wells telling us now? What is Refined Completions watching next? What follow-up or treatment should be considered? Producing those answers consistently is central to the company’s stewardship-centered approach.

The Challenge

Before BloomifAI, producing one stewardship report required a fragmented, multi-person process. Three to five contributors could spend a combined 8–10 person-hours gathering data, running calculations, building charts and tables, interpreting technical results, writing the narrative, formatting the report, and routing drafts through review.

The process relied on manual handoffs and repeated assembly. Every new well or pad restarted much of the same production effort, pulling highly skilled employees away from higher-value work with customers.

The old workflow moved through five broad steps. Team members first gathered laboratory, operational, and production data from separate sources. They then ran calculations and built charts and tables. Technical specialists interpreted the results, another contributor assembled the narrative and formatted the report, and the draft moved across the team for review, revision, and rework.

The effort was spread across the entire production process. An illustrative breakdown attributed roughly one-third of the work to data preparation and charts, one-third to narrative development and formatting, and one-third to coordination and review. Because every stage changed hands, the total cycle time reflected not only the work itself but also the friction of multiple contributors, handoffs, and review cycles.

The core business problem was therefore larger than document production. Highly skilled employees were spending hours assembling reports instead of interpreting the results, advising customers, and using the findings to drive account conversations. As reporting demand grew, the manual process created a choice between limiting output or allocating more expert capacity to repeatable production work.

The Solution

BloomifAI worked with Refined Completions to turn the stewardship report into a repeatable operating workflow inside a secure workspace dedicated to the Refined team. The solution combines integrated data, Agentic AI, a controlled report template, and human validation.

The workflow follows five connected stages:

• Data: Bring together approved inputs from lab systems, FracTrak, and Enverus.
• Agentic AI: Reason across the integrated sources and prepare the report components.
• Template: Draft the stewardship narrative, tables, charts, and comparisons in a consistent format.
• Human review: Let Refined’s experts validate, comment, and refine the draft before anything is finalized.
• Delivery: Produce a customer-ready output while retaining the connected data and workflow for future runs.

One person can now start the workflow by selecting a well or pad and seeding the available data. The system creates an initial draft in roughly 2–2.5 minutes. Review and collaboration remain in context, and edits change the report rather than the underlying source data.

Several operating changes make the workflow repeatable. One person can initiate the process instead of coordinating three to five contributors at the outset. The AI performs much of the initial assembly across the approved data sources. Refined’s experts still review, comment, and edit the draft before customer delivery. Comments and changes remain connected to the same well or pad rather than being scattered across separate email threads, and the workspace retains the data and prior reporting context for future runs and comparisons.

This distinction is important: BloomifAI did not replace Refined Completions’ expertise. It reorganized the work so that AI handles the repetitive production burden and the customer’s specialists focus on validation, interpretation, and judgment.

Quality and Control Built Into the Workflow

Speed matters only when the team trusts the output. Refined’s reporting rules are built into the workflow so the system can check consistency before an expert opens the draft.

• Evidence boundaries: The workflow identifies what data is present—and what is missing—before drawing conclusions.
• Supported comparisons: Claims are made only when the available data supports them.
• Consistent source logic: Well names, formations, counts, tables, charts, and narrative use the same source logic.
• Visible validation: Anomalies are flagged with the supporting math and routed for human verification.

During validation, these controls helped the team account for missing bacteria data, avoid unsupported comparisons when only a limited production window was available, reconcile chart and narrative differences, and flag a chemical delivery variance for stage-by-stage source verification.

For example, the workflow can hold back microbial-control language when bacteria data is not present for the relevant wells. It can recognize when only three months of production history is available and avoid presenting an unsupported six-month comparison. It can also reconcile formation assignments so tables, production views, and written analysis remain aligned.

In another validation example, the workflow identified a chemical delivery rate that appeared 84% below its design target. Rather than presenting that variance as a definitive conclusion, it showed the calculation, flagged the issue, and held it for stage-by-stage source verification. That behavior reflects the objective of the system: improve confidence and quality by surfacing what requires expert attention, not merely generate text faster.

The Impact

The new process reduced estimated report cycle time by 93–96%. A report that previously required 8–10 person-hours can now be generated by AI in approximately 2–2.5 minutes and completed with team review in about 30 minutes.

Before	With BloomifAI	Estimated savings
8–10 person-hours per report	Approximately 30 minutes including review	Approximately 7.5–9.5 person-hours per report

Using an illustrative direct-wage rate of $50 per hour, this change represents an estimated $375–$475 in direct-wage savings per report. At 20 reports per month, that equates to approximately $7,500–$9,500 per month, or $90,000–$114,000 per year, while returning roughly 150–190 hours of expert capacity each month.

The model begins with the customer-reported baseline of 8–10 person-hours per report. Subtracting an approximately 30-minute reviewed workflow leaves an estimated 7.5–9.5 person-hours shifted away from manual report production. At 20 reports per month, that represents approximately 150–190 hours of returned capacity—roughly the working time of one full-time employee each month.

Important context: These figures are an illustrative direct-wage model based on the customer-reported baseline and observed launch workflow. They exclude benefits, overhead, and opportunity cost. Results vary based on data complexity and review requirements.

From Hours Saved to Customer Value

The reclaimed capacity allows Refined Completions to move technical experts out of report assembly and into customer-facing work. Stewardship reports can be delivered more consistently across more wells, findings can reach customers while they are still actionable, and recommendations can lead directly into treatment and follow-up conversations.

This creates room for more customer touchpoints, faster follow-up, clearer demonstration of value, stronger retention and expansion opportunities, and greater report volume without adding people solely for report assembly.

More consistent reporting allows findings to reach customers while the information is still actionable. Each report provides a clearer record of what Refined delivered, what changed, and what the team continues to watch. Recommendations can surface the next treatment or follow-up conversation, while regular evidence-based reporting keeps Refined Completions connected to customer decisions over the life of the well.

The larger outcome is an operating-model change: experts who previously assembled reports can spend more time advising customers. The workflow creates leverage for those experts by increasing the volume and consistency of reporting without requiring an equivalent increase in report-production headcount.

What the Customer Said

“I think our whole team would agree that you and your team have done an amazing job getting this working for us. It was exciting to see everything come together and to actually use the Stewardship workflow live. This is going to be very valuable for our team.” – Holly Artho, Chief Integration Officer

“This looks really impressive… you guys have really taken a huge leap.” – Roch Romanson

“You guys knocked it out of the park. You exceeded my expectation for what we were going to get today.” – Lloyd Brown, CEO

A Repeatable Model for High-Friction Work

Refined Completions is one example of what happens when a repetitive, data-heavy workflow is redesigned around secure orchestration, Agentic AI, integrated data, and human validation.

BloomifAI begins by identifying the friction: repetitive work, handoffs, data pulls, formatting, calculations, and review loops. The team then defines the use case, connects the appropriate data and knowledge, builds and validates the workflow with customer subject-matter experts, and launches a process designed for repeated use.

For a focused use case, the typical target is an approximately two-week delivery sprint, although timing depends on data readiness, integration needs, and scope.

The delivery model begins by defining the workflow, users, required data, and desired output. BloomifAI then connects the relevant knowledge and translates the customer’s process into Agentic AI instructions and a controlled template. A working version is validated against real data with customer subject-matter experts, refined through a shared feedback loop, and launched with training and continued improvement.

In this model, both parties have clear roles. The customer’s subject-matter experts define success and validate the output. The BloomifAI team builds the logic, template, integrations, and orchestration. Iteration continues until the workflow is ready to support production use. The objective is not a one-time AI demonstration, but a customer-specific operating process that can be used repeatedly.

That approach reflects BloomifAI’s broader AI Orchestration as a Service positioning: secure orchestration, integrated data, Agentic AI, organizational context, and human review working together around a defined business outcome.

Turn One High-Friction Workflow Into Leverage

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