A large language model trained on your procedures, internal notes, manuals and playbooks, incident reports, and contract terms. Ask it anything about your operation in plain language - and get answers from your actual data, not generic AI.
Every mature operation carries an enormous body of knowledge - in manuals and playbooks no one reads, in notes filed away after incident or account reviews, in the heads of the veterans who've been there 20 years and know exactly why that process behaves that way. When that knowledge lives in documents scattered across shared drives, it's effectively inaccessible. When it lives in people, it walks out the door when they leave.
TMI's Operational LLM is a large language model fine-tuned on your company's specific data. Not a general-purpose AI. Not a search engine that returns ten links. A model that has ingested your SOPs, your manuals and playbooks, your incident reports, your contract terms, your internal notes - and answers questions about your operation with the specificity those sources contain. A technician in the middle of a job, or a support rep in the middle of a ticket, can ask why a particular reading or account looks off, and get an answer sourced from your actual records. A new hire can ask how your company handles a scope change on a fixed-price contract, and get the answer from your standard operating procedure - not a generic template.
The result is institutional knowledge that persists across staff turnover, that scales to every person in your organization simultaneously, and that gets more precise as your data grows.
We ingest every relevant document in your operation: standard operating procedures, equipment manuals, maintenance logs, incident reports, inspection records, field notes, contract terms, and compliance documentation. Documents are parsed, structured, and indexed - not just stored. The model understands the relationships between your data sources, not just the contents of individual files. This is the foundation that makes the answers specific rather than generic.
The base model is fine-tuned on your operational data specifically. It learns your terminology, your equipment, your protocols, and the way your organization describes and categorizes things. The difference between a fine-tuned operational LLM and a general AI assistant is the difference between asking a 20-year veteran of your company and asking a random consultant who just read the industry Wikipedia page. The answers come from your records, attributed to your sources, with your specifics - not generalities.
The operational LLM is accessible wherever your people work. Field and frontline staff query it from mobile. Managers, analysts, and estimators access it from desktop. It can be integrated directly into existing tools - your work order or ticketing system, your CRM, your ERP. Every answer is sourced and citeable: the model shows which document, which section, which record its answer comes from. It doesn't hallucinate your procedures. It quotes them.
The operational LLM is a flagship intelligent system - the intelligence layer that makes the rest of your infrastructure queryable in plain language.
A large language model fine-tuned on your company's data: procedures, internal notes, manuals and playbooks, incident reports, and contract terms. Teams and managers ask questions in plain language and get answers sourced from your actual operation. Institutional knowledge made queryable.
"The goal is not a smarter search engine. It's an AI that has read everything your operation has ever documented, understands it the way your most experienced people do, and is available to every person in your organization at once - at 2am on a remote job site, on a support shift, or in the middle of a client negotiation."
The operational LLM has the broadest value distribution of any system we build - it benefits everyone from the newest hire to the senior operations director.
Ask about process steps, error codes, procedures, specifications, account history, or compliance requirements - and get answers from your actual manuals and records, not a generic knowledge base. A technician on site at 10pm or a support rep mid-ticket doesn't need to call someone back at the office. They query the system, get the answer from the documentation, and get the job done.
Query job history, performance data, contract terms, and cost records in plain language. "What was our average utilization on the Peterson account last quarter?" "Which team has the highest rework rate on this work type?" "What does our master agreement with that vendor say about scope change pricing?" Answers in seconds from the actual data, not a report that takes three days to pull.
Onboarding time compresses dramatically when new hires can ask the operational LLM everything they would have called a veteran to ask. Your procedures, your terminology, your equipment quirks, your client preferences - all accessible on day one. The institutional knowledge of your most experienced people becomes a resource available to your newest, rather than an invisible asset that only exists in their heads.
We'll assess your current knowledge infrastructure, identify the documents and records that matter most, and show you what an operational LLM built on your data looks like in practice.
FAQ
An operational LLM is a language model trained on and connected to a specific company's data - maintenance records, work orders, dispatch logs, financial data, compliance reports. When a manager asks 'which equipment on Site B has had more than three unplanned maintenance events in the last 90 days,' the operational LLM answers from actual operational records. ChatGPT can only answer from its training data, not from your company's data.
TMI builds the data infrastructure that connects the LLM to operational databases, document repositories, and real-time data streams. The LLM layer can query structured data (job records, maintenance logs, financial data), read documents (contracts, SOPs, compliance reports), and respond to questions that require synthesizing information across multiple data sources.
Frontline and field managers query history and troubleshooting guidance. Project and account managers query cost status and change history. Executives query operational performance and financial trends. Compliance officers query regulatory status and incident history. The same interface serves all roles - each person gets answers from the data they have access to, with access controls enforced at the data layer.
Standard reports answer predefined questions. An operational LLM answers any question that can be answered from the available data. 'Which customers have had three or more service or support calls this year without an active agreement?' 'What was the average response time for our top five team members last quarter?' 'Which jobs in the past six months had costs that exceeded estimate by more than 20%?' These require combining data from multiple sources in a way no standard report can do on demand.
A search system finds documents or records that match keywords. An operational LLM understands the question and synthesizes an answer from across all relevant data. When someone asks 'what's the full history on this account or asset,' the LLM pulls records from logs, work orders, transaction history, and inspection or review records and returns a coherent answer - not a list of documents to read.
An operational LLM implementation depends on the number and complexity of data sources being connected and the scope of the knowledge base being built. A focused implementation connecting core operational data typically takes 8-16 weeks. A comprehensive implementation covering all operational systems typically takes 4-6 months.
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