For asset-heavy and operations-heavy companies. We take the data already sitting in TM Master, your ERP, or your maintenance system, turn it into a working database your teams can actually question, and prove in weeks whether AI clears your return bar. Fixed fee. Gated. If the answer is no, you get that answer in writing instead of finding out after the build.
Enterprise AI usually starts with a big integration and a bigger promise. We start the other way around. Send us an export from the system your operation already runs on. We come back with what your own data can already answer, and where the money is. Everything after that is staged, so you only fund what the evidence supports.
We take a raw export from your system of record and put real findings in front of your team: a queryable working database, decoded legacy codes, and the events hiding in free-text notes. You see what we can do before you commit to anything.
A fixed-fee, fixed-window phase that sizes the savings in dollars, on your volumes and your intervals. Your engineers validate the inputs. It ends in a written go or no-go with a confidence number and the reasoning behind it.
Only what cleared the gate gets built, priced firm from the proof phase instead of a ballpark. Forecasting, dashboards, and natural-language access to your own records. You can stop at any gate.
Sensor feeds, condition monitoring, cost data. Each new signal slots into the same foundation and makes everything before it sharper. Your OEM and vendor programs plug in too. We hand them clean history instead of competing with them.
A global fleet operator handed us a raw export from TM Master, their planned-maintenance system of record. No new data was collected and nothing was integrated. Everything below came out of a file they already had.
Job-history records loaded into one queryable working database
Failure, leak, replacement, and overhaul events recovered from free-text notes that had never been labelled
Components given life-used estimates and ranked by risk across the fleet
Data-quality issues surfaced, each traced to the record and the team that owns it
One generator bearing had seven scattered entries across six vessels. As the system of record shows them, they are just rows. Read together, they showed a component with a characteristic life near 11,000 running hours, vessels running it at four to seven times that, and one unit with a 99 percent probability of failure inside 90 days. That is the difference between a maintenance log and an answer.
That working session won a fixed-fee engagement to prove the savings in dollars, now underway, ending in a written go or no-go with a confidence number.
If your teams live in it, we can work with it. Decades-old records, migrated databases, and inconsistent codebooks are the normal case, not a blocker. The real story is usually in the free text nobody has read at scale.
TM Master and systems like it: CMMS, EAM, and planned-maintenance platforms holding years of job history, components, and spares.
Cost, procurement, and inventory data. It turns an engineering finding into a dollar figure a board can act on.
Pre-migration archives, retired codebooks, and the records that never made it across. We decode and reconcile them.
Work-order comments, engineer notes, manuals, and logs. The story usually sits in the typed comments, and we read all of it.
Oil analysis, vibration, temperature, and OEM monitoring feeds. When they are ready, they layer onto the history we have already built.
If you already run a lake or warehouse, we build on it. If you do not, we stand up a working set your team can query from day one.
Phased and gated. It starts with a working session on an export from your system of record, so you see real findings before committing. Then a fixed-fee proof phase sizes the return in dollars and ends in a written go or no-go with a confidence number. Build phases follow only if the evidence clears your bar, priced firm from what the proof phase found. You can stop at any gate.
No. The first phase runs on a flat export from the system you already run, whether that is TM Master, an ERP, or another operational database. No new sensors, no platform purchase, and no change to how your teams work.
Then you get that answer in writing, with the reasoning and a confidence number, for a fixed fee. A no-go still leaves you a costed answer, a validated view of your data, and a ranked data-quality register. That is a far cheaper way to find out than after a build.
We plan for your process instead of pretending it away. Read-only access, least privilege, and your infrastructure where required. Client data is never used to train models for anyone else and never shows up in anyone else's engagement. Delivery clocks start when access is confirmed, so review time never eats delivery time.
No. TM Master, your ERP, and your operational systems stay exactly where they are. We build the working layer beside them: normalized, queryable, and reproducible. If you migrate systems later, that clean history carries with you instead of being lost in the move.
Messy is the normal case. Legacy job codes, inconsistent naming, and half-empty fields are what we expect to find. Part of the engagement is measuring exactly how good the data is, publishing the gaps, and telling you what can honestly be built on it. Sometimes the answer is a smaller build than hoped. You will know before you spend.
Tell us where the work runs through you. We'll find the bottleneck, quantify the ROI, and tell you honestly whether automation will pay off.