For field operations, grid and asset management, and regulatory reporting.
We build the operational software and applied AI that energy and utility businesses run on — field-service and work-management tools, asset and grid-management applications, outage and telemetry analytics, and the regulatory-reporting pipelines that have to be right every time.
Built in your own cloud, integrated with the systems of record you already run, and assigned to you in full. Fixed scope, one accountable lead, production in 4–8 weeks.
The work is unforgiving in ways generic enterprise software isn’t. A scheduling bug strands a crew; a telemetry pipeline that drops messages hides a developing fault; a reporting error in front of a regulator is a finding, not a ticket. The operational systems carry safety, reliability, and compliance weight at the same time — and they have to keep running while the grid does.
On top of that, the data is messy and physical. SCADA, AMI/smart-meter streams, GIS, historian, and work-management systems each speak their own format and rarely line up cleanly; field conditions are intermittent; assets outlive three generations of the software meant to track them.
Most teams aren’t short on data — they’re short on the engineering that turns it into something operators, planners, and compliance staff can act on, reliably enough to trust when it matters. That gap is exactly what energy software development has to close.
These are the specific applications we engineer for the sector. For each: what it does, the benefit it produces, and a one-line illustration of how that plays out. The examples are illustrative of the technology’s effect, not Silicon Prime client results.
Schedules and routes crews against live work orders, asset locations, and access constraints, and gives technicians an offline-capable mobile view that syncs when connectivity returns. Benefit — more jobs completed per crew, less windshield time, and accurate records from the field.
Example: a routine inspection and a nearby emergency repair are sequenced into one trip instead of two truck rolls — the second drive never happens, and the closeout is captured on-site rather than re-keyed that night.
Tracks transformers, feeders, meters, pipes, and generation assets across their full lifecycle — condition, maintenance history, and risk — and turns sensor and inspection data into a prioritized maintenance and replacement plan. Benefit — fewer unplanned failures and capital spent where it actually reduces risk.
Example: a transformer trending toward failure is flagged and serviced on a planned window instead of failing on a peak afternoon — the outage, the overtime, and the emergency replacement cost are all avoided.
Ingests SCADA, smart-meter, and sensor telemetry to detect, locate, and characterize outages and abnormal conditions, and feeds the operators and downstream systems that coordinate restoration. Benefit — faster fault location and shorter restoration, which moves the reliability indices regulators watch (SAIDI/SAIFI/CAIDI).
Example: a fault is located to a specific line segment from meter “last-gasp” signals in seconds instead of waiting on customer calls — the crew is dispatched to the right span the first time, not after a sweep.
Assembles the recurring filings the business owes — reliability metrics, environmental and emissions data, rate-case and commission reporting — from source systems, with validation and a full audit trail behind every number. Benefit — filings that are accurate, repeatable, and defensible, with far less manual assembly.
Example: a monthly reliability report is generated from validated source data with every figure traceable to its origin, instead of being stitched together in spreadsheets the week it’s due.
Powers billing-adjacent workflows, usage and rate-plan questions, and outage communications — including conversational assistants grounded in the customer’s own account and outage data. Benefit — lower contact volume on routine questions and clearer communication during events.
Example: a customer asking “why is my bill higher this month?” gets a grounded, usage-specific answer instead of waiting in a call queue — and during a storm, status updates go out from the same outage data the operators see.
The scope below is where the safety, reliability, and compliance weight actually lives — and it’s why generic delivery doesn’t survive contact with this sector.
We connect SCADA, AMI/smart-meter streams, GIS, historian, ERP, and work-management systems into pipelines that decode each format, validate it, and keep it flowing — the energy-specific application of our IoT and telemetry engineering and data analytics engineering, not a re-explanation of it.
Crew scheduling, routing, mobile work execution, and asset-data capture built to work in the field — offline-tolerant, fast, and accurate when connectivity is intermittent.
Condition monitoring, risk and maintenance prioritization, and outage detection and location — applied machine learning on your own operational data, with every model output explainable to the operator who has to act on it.
Reporting pipelines that assemble required filings from validated source data, with the audit trail, lineage, and repeatability that hold up in front of a commission or auditor.
Energy systems are critical infrastructure. We build to least-privilege access, segmentation, and auditable controls as a default — see our cybersecurity services — and we ship behind staged rollouts with production monitoring, because a bad release here has operational consequences.
Where off-the-shelf doesn’t fit the regulatory or operational reality, we build the custom software that does — in your stack, owned by you.
What you get when you hire us — all assigned to you
The same delivery model behind all our work, tuned for a regulated, operations-critical environment — one accountable lead, fixed scope, no handoffs.
Scope the operational problem, the systems of record involved, the field and compliance constraints, and the metric we’ll be judged on.
Output: a ranked plan & the success measures we own
Design the data integration, the validation and audit approach, and (where models are involved) the evaluation set built from your real operational data.
Output: an architecture & the test plan it has to pass
Develop in your own cloud tenant, wired to your systems through governed, permissioned access, with validation, auditability, and security built in rather than bolted on.
Output: a working system behind your access controls
Staged rollout (shadow, then pilot, then wide), the target metric measured against baseline, and your team trained to operate and extend it.
Output: a production system & a team that owns it
Most engagements reach production in 4–8 weeks, with full work-for-hire IP assignment signed at kickoff.
We don’t yet hold a public energy or utilities case study, and we won’t attach someone else’s logo to imply experience we don’t have. Here is the honest record we can stand behind — and the production discipline that carries into this work.
We’ll be direct: energy and utilities is not where Silicon Prime has its deepest public case studies, and we won’t pretend otherwise or attach someone else’s logo to imply experience we don’t have. What we bring is a 15-year engineering record, a delivery discipline built for software-critical operations, and a founder personally accountable for the engagement.
The closest analog in our work is adjacent, and we label it as exactly that: we run software-critical operations for BJ’s Restaurants, a 200+ location business, holding it at twice-a-week releases with zero critical defects across four years — the same evals-before-release, staged-rollout, monitor-after discipline that an outage-analytics pipeline or a regulatory-reporting system demands. It is not an energy result, and we don’t present it as one; it is evidence of how we operate where reliability is non-negotiable.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, including multimillion-dollar systems for one of the world’s largest automobile manufacturers, personally accountable for every engagement. If a problem isn’t a fit for us, we’ll tell you in the scoping call rather than after the contract.
Delivery discipline built for critical operations. Evals before release, staged rollout, production monitoring — the practices that keep software-critical, multi-site operations stable are the ones a regulated energy environment needs most.
We build for auditability and security by default. In critical infrastructure, the audit trail, the access controls, and the explainability of a model’s output aren’t features you add later — they’re part of the build from day one.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes the work answers for it. And we’ll decline work that isn’t a fit rather than learn on your dime.
Built to transfer. Code, data pipelines, models, and audit logic are assigned to you in full; your team is trained to run and extend the system when we step back.
What teams want to know before they commit to an energy or utilities build.
We’ll be straight with you: our deepest public case studies are in other sectors, not energy. What transfers is the engineering — 15 years of production work and a delivery discipline proven on software-critical, multi-site operations (BJ’s Restaurants: twice-a-week releases, zero critical defects across four years), which we cite as an adjacent reliability analog, not an energy result. We scope honestly and decline work that isn’t a fit, so the project is judged on the engineering and the accountability — not a borrowed logo.
Field-operations and work-management tools, asset and grid-management applications, outage and telemetry analytics over SCADA/AMI/sensor data, regulatory and compliance reporting pipelines, and the customer-facing workflows around billing, usage, and outage communication. We build the operational software and applied AI, integrated with your systems of record — not the field hardware or metering devices themselves.
Yes — that integration is most of the work. We build pipelines that decode each source format, validate the data, and keep it flowing into the application or analytics that needs it, through governed, permissioned access. The energy-specific application of our IoT/telemetry and data-engineering practice is connecting these operational systems reliably.
We build to least-privilege access, network segmentation, and auditable controls by default, ship behind staged rollouts with production monitoring, and start every engagement with an NDA and a security review (our cybersecurity practice). For regulated reporting, every figure carries lineage and an audit trail so it’s defensible in front of a commission or auditor. We’re conservative by design here — overclaiming in critical infrastructure is how projects fail.
By measuring it before it ships and keeping a person in the loop. Any model is evaluated against a test set built from your real operational data, its outputs are made explainable to the operator who has to act on them, and low-confidence cases surface for human judgment rather than auto-acting. Then we monitor it in production for drift — the same evals-before-release, monitor-after discipline we apply across every engagement.
You do — completely. Code, data pipelines, models, and reporting/audit logic transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate and extend the system. Keep us on a reduced retainer or take the keys; the engagement is built around the handover.
Most engagements reach production in 4–8 weeks under a fixed-scope arrangement with one accountable lead and payment tied to ROI. Build cost depends on scope — our AI development cost guide gives real ranges — and we model run cost before building, so the first invoice is a forecast you’ve already seen.
Thirty minutes · No pitch deck
Bring the operational problem — field operations, asset or grid management, outage analytics, or regulatory reporting — and we’ll tell you honestly whether it’s a fit for us, what it takes to build, and what it costs to run.