What does 'smart dispatching' actually mean?

Work orders matched to crews by skills, certifications, current location, and load — and sequenced by priority and geography rather than by whoever called last. The dispatcher stops being a human router juggling texts and starts supervising a system that proposes the schedule.

Where do work orders come from in the automated flow?

Every channel you already use: operator reports, office requests, inspection findings — and, when the telemetry pipeline exists, alerts from monitoring or predictive models that open work orders automatically with the readings attached. One queue, full context, no sticky notes.

Does routing actually matter that much for distributed sites?

Distances decide it. When sites are an hour apart, a badly sequenced day burns half a shift on asphalt. Routing that respects geography and priority converts windshield time into wrench time across every crew, every day — it's one of the quietest but most compounding wins in field operations.

What do technicians see in the field?

Their day: the ordered job list, each with location, task, history, parts, and safety context — plus the ability to close out with notes, photos, and time from a phone. Close-out data flows back automatically, so the office knows job status without radio check-ins.

Does this integrate with our existing systems?

Yes — through APIs and supported export paths to the back-office, inventory, and operational systems you already run. Work order history, parts usage, and labor data land where your existing processes expect them, rather than in another silo.

Can it connect to predictive maintenance?

Directly — it's the natural pairing. A failure-forecasting model that flags a compressor is only worth what happens next: an automatically created work order, scheduled into a planned window with the right crew and parts. Prediction finds the job; dispatch automation executes it.

Scoped to your crew count, site distribution, and existing systems — which is why we start with a free build plan. Walk us through how a work order moves today, from report to close-out, and we'll map the build against the hours it recovers.

Field Service Automation for Energy Companies

Dispatch on Data, Not on Whoever Calls Loudest — Energy Field Operations

Tera Bullion builds field service automation for energy companies: smart dispatching, work order management, and technician routing optimized by AI. Work orders stop living on whiteboards and in text threads — jobs get created with context, matched to the right crew and parts, and routed in an order that respects geography, priority, and the cost of every deferred hour.

What is manual dispatch actually costing you?

Follow one work order through most field operations and you'll find the whole problem. An operator notices a problem and texts a supervisor. The supervisor calls dispatch. Dispatch writes it on the board, then sends whichever crew frees up first — forty minutes past a closer crew, without the compressor's history, to discover on arrival that the job needs a part that's back at the yard.

None of those people did anything wrong. The system they're working in has no memory, no map, and no priorities — so every dispatch decision is made from partial information under time pressure. The cost shows up as windshield hours that produce nothing, second trips for parts, urgent jobs waiting behind loud ones, and paper close-outs that mean the office learns what happened days later, if the form survives the truck.

Why don't the obvious fixes work?

More crews raise capacity without fixing allocation. If dispatch can't see load, location, and priority, extra crews inherit the same badly sequenced days — you've scaled the inefficiency and its payroll together.

A better whiteboard — or a shared spreadsheet — centralizes the chaos without automating any decision. Someone still manually converts calls and texts into rows, still guesses at assignment, still can't see where trucks actually are. The board is a picture of the backlog, not a system for working it.

Radio discipline and morning huddles improve communication about a plan that's stale by 9am. Field days don't survive contact with reality — a job runs long, an urgent call lands — and without a system that re-sequences, every disruption cascades through the day by phone call.

The common failure: all three keep a human as the router in a problem that outgrew human routing. Skills, locations, priorities, parts, and geography across distributed sites is an optimization problem — and optimization problems are what software is for.

What do we actually build?

One work order queue fed by every intake channel — operator reports, office requests, inspections, and monitoring alerts — each job created with context: history, location, parts, safety notes.
AI-optimized assignment and routing: jobs matched to crews by skill, certification, location, and load, sequenced to minimize drive time and respect priority — recomputed when the day changes.
Mobile close-out for technicians: the day's ordered list on a phone, with notes, photos, parts, and time captured at the job — no paper, no end-of-week data entry.
Live status for the office: what's open, who's where, what closed today — without radio check-ins.
Back-office integration: labor, parts usage, and job history flowing to your existing systems automatically, keeping inventory and cost data current.

What changes operationally?

Before: a problem becomes a text, then a whiteboard entry → the nearest-free (not nearest-qualified) crew rolls without context → a parts gap forces a second trip → the close-out rides in a truck for three days → and nobody can say what field labor actually costs per job.

After: the report becomes a work order with history and parts attached → the system proposes assignment and routing that respect geography and priority → the technician closes out from the site → the office sees it in real time — and every job leaves behind data that makes the next dispatch smarter.

Who is this not for?

Single-crew operations. If one crew serves one site, dispatch is a conversation, not an optimization problem. This pays off with multiple crews across distributed locations.
Operations that won't put a phone in the field. Mobile close-out is where the loop closes. If field data capture is a non-starter culturally, the office stays blind and half the value evaporates — we'd rather tell you that up front.
Shops whose real problem is workload, not allocation. If every crew runs flat-out on genuinely optimal routes already, software won't mint hours. It pays where drive time, second trips, and misassignment are eating shifts — which, in distributed operations, is nearly always.

If your crews cross real distances and your dispatch runs on texts and a whiteboard, this build converts windshield time into wrench time — and gives the office its first true picture of what the field actually does all day.