For commercial aviation — the data, records, and maintenance-intelligence layer.
We build commercial-grade business and AI software for the aviation and aftermarket ecosystem: maintenance-records intelligence, predictive-maintenance analytics, fleet- and asset-data applications, and the MRO-adjacent workflow tools that off-the-shelf systems leave uncovered.
To be exact about scope: we build commercial business software only. We do not take classified work, ITAR- or EAR-controlled defense programs, flight-critical avionics, or safety-of-flight certified software. Fixed scope, one accountable lead, production in 4–8 weeks, every line of code and model assigned to you.
Because the aerospace ecosystem runs on records, and most of those records were never built to be queried. A single aircraft carries a back-to-birth paper trail — maintenance logs, airworthiness directives, component genealogy, dirty-finger-print documents — much of it scanned, hand-written, or locked in formats no system reads.
Fleet, sensor, and MRO data lives in separate silos. The result is the most expensive failure mode in the business: the information needed to keep an aircraft flying, prove compliance, or close a transaction exists somewhere, but finding and trusting it takes days instead of minutes.
That gap is widening, not closing. Production backlogs are so deep that operators are flying existing fleets longer and leaning harder on maintenance, which keeps aftermarket demand strong. Closing it isn’t a new ERP or a rip-and-replace — it’s the focused software that turns documents and signals into answers, which is what aerospace software development, scoped honestly to commercial work, delivers.
This isn’t one product. It’s a set of high-leverage applications that sit on top of the systems and records a commercial aviation operation already has. Every example below is illustrative of the technology — not a Silicon Prime client result.
Reads scanned logbooks, work orders, airworthiness directives, and component records, then extracts and structures them so the asset’s status, gaps, and compliance position can be queried in plain language instead of read by hand. Benefit — critical records surface in minutes, not days, and documentation gaps are caught before they cost you.
Example: a lessor assessing an aircraft for redelivery gets a structured back-to-birth picture and a list of missing documents from a stack of PDFs in minutes — instead of an analyst spending a week reconciling paper, the gap GE Aerospace’s own generative-AI records tool was built to close (see the measured impact below).
Learns the normal signature of a component or system from sensor, sortie, and historical data and flags the deviation that precedes a failure — turning a reactive removal into a planned one on the next scheduled visit. Benefit — fewer unscheduled removals and grounded aircraft, and better-planned MRO visits.
Example: a part trending toward an out-of-limit reading is flagged before a flight, so it is swapped during a planned overnight instead of triggering an aircraft-on-ground event the next morning — and the operation never eats the grounded-aircraft cost below.
Unifies utilization, reliability, configuration, and cost data across a fleet into one trusted view, so reliability and planning decisions run off live data instead of month-end spreadsheets. Benefit — faster, better-grounded reliability and planning decisions.
Example: a reliability engineer sees a component’s removal rate climbing across the fleet in real time and triggers a fleet-wide review before it becomes a recurring AOG driver — rather than discovering the trend in a quarterly report.
Builds the targeted application a maintenance, repair, and overhaul operation needs that off-the-shelf platforms don’t cover — a turn-time tracker, a parts-and-tooling visibility tool, a hangar-floor data-capture app — integrated with the M&E or ERP system you already run. Benefit — the workflow your operation actually runs gets purpose-built software, without a multi-year platform migration.
Example: a heavy-check turn-time bottleneck gets a focused app that surfaces the stuck task and its blocking part live, so the shift lead reroutes work instead of finding the slip at the end of the day.
Stitches existing records, parts, and inspection data into a provable, queryable genealogy so an airworthiness audit or a transaction closes against trusted data rather than a manual document hunt. Benefit — faster audits and transactions, and far lower records risk.
Example: an airworthiness review pulls a complete, traceable history for a life-limited part on demand instead of pausing while someone searches the archive — the kind of records gap that can otherwise cost millions in unbudgeted cash during an aircraft transition.
The scope below is the commercial business-software and intelligence layer. To be explicit: we do not build flight-critical or airborne avionics, we do not do DO-178C safety-of-flight certified software, and we do not take classified or ITAR/EAR-controlled defense work. This is commercial enterprise software, scoped honestly.
We build the document-AI pipelines that read scanned logbooks, work orders, and airworthiness records and turn them into structured, queryable data — including the extraction and validation that makes the output trustworthy enough to act on. Built on our data engineering work so the records are trusted before anyone relies on them.
We build the machine-learning models that learn a component’s or system’s normal behavior and flag pre-failure drift, validated against your historical removal and failure data so the alerts are trustworthy — not a false-alarm generator the line learns to ignore.
Real-time and historical analytics over utilization, reliability, configuration, and cost data — unified into one view by line, tail, and component — so reliability and planning decisions run off trusted live data.
Targeted applications integrated with the maintenance-and-engineering or ERP system you already run — turn-time tracking, parts and tooling visibility, hangar-floor data capture — plus modernization of the legacy aviation systems that are too brittle to build on, without ripping out what works.
Where it pays, a grounded assistant lets technicians and planners query manuals, SOPs, and records in plain language — every answer cited to its source, with human-in-the-loop review where a wrong answer is costly.
What you get when you hire us — all assigned to you
The same delivery model behind all our work, tuned for aviation data and records — one accountable lead, fixed scope, no handoffs to account managers.
Scope the use case and the loss it targets — records-retrieval time, unscheduled removals, audit turnaround — and confirm the data and records exist to support it. Run as our AI readiness assessment, with the honest “this is out of scope for us” or “the data isn’t ready yet” call included.
Output: a ranked plan & the metric we’ll be judged on
Pull records, fleet, sensor, and M&E/ERP data into a usable, governed store through read-only, permissioned pipelines. We read from your existing systems; we don’t replace them.
Output: a trusted data foundation
Develop the application and, where it applies, train and validate the model against your historical data, in your own cloud or on-prem environment.
Output: a working system tested on your real data, not a demo
Pilot on a contained scope — one fleet, one records type, one hangar — prove the metric moves, then scale, with your team trained to operate, retrain, and extend it.
Output: a production system & a team that owns it
Most engagements reach production in 4–8 weeks, payment is tied to the ROI we agreed at kickoff, and full work-for-hire IP assignment is signed before we start.
Software that informs whether a part is swapped, an audit closes, or an aircraft flies has to be right, and stay right. So let’s be straight about scope before any proof: Silicon Prime has no aerospace or defense engagement, no security clearances, no ITAR registration, no cleared personnel or facilities, and no classified-program experience.
This page describes commercial-grade software engineering for the aviation and aftermarket ecosystem, and we will not represent ourselves as anything more. Our case studies are in other industries; we’ll show the engineering rigor that transfers through honest, clearly-labeled adjacent examples rather than an aerospace case we don’t have.
The clearest proof of that rigor is BJ’s Restaurants — a 200+ location operation whose software is critical to daily operations. Over four years we moved their release cadence from every two weeks to twice a week while sustaining zero critical defects, through evaluations before release, staged rollout, and continuous production monitoring. That is a different industry, but it is precisely the “ships fast, never breaks the thing operations depend on” standard that any records-integrity or predictive-maintenance system has to meet.
Our closest work to the heavy-asset world is YardClub, the heavy-construction-equipment marketplace we built end to end — it processed $120M+ in transactions and was acquired by Caterpillar in 2017, so working with high-value machinery, its parts, and its data is familiar ground, even though that engagement was a marketplace, not an aviation system.
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, and personally accountable for every engagement. Aviation is a new vertical for us; we’ll say so to your face, scope a contained pilot to prove the value before you commit, and put the accountability in writing rather than overpromise capability we haven’t shipped.
Commercial scope, stated honestly. We build commercial business software for aviation — records intelligence, analytics, models, applications — not flight-critical avionics, certified safety-of-flight software, classified systems, or ITAR-controlled programs. That boundary means a faster, lower-risk engagement and zero overpromising on defense capability we don’t have.
Production discipline first. A records-integrity or predictive system is judged on trust. The same evals-before-release, staged-rollout, monitor-after discipline that holds a 200+ location operation at zero critical defects is what we bring to a system your reliability and compliance teams depend on.
Models you can trust, validated on your data. We validate predictive and document-extraction models against your historical record before they go live, with human-in-the-loop review where a wrong call is costly — because the fastest way to kill an aviation-data system is output the team learns it can’t trust.
Founder-led, built to transfer. One accountable lead, not a relay of account managers; and the code, models, and pipelines are assigned to you, with your team trained to run them when we step back.
What teams want to know before they commit to building software on their records and fleet data.
No — and we won’t pretend otherwise. Silicon Prime has no security clearances, no ITAR or EAR registration, no cleared personnel or facilities, and no classified-program or defense-contract experience. We build commercial-grade business software for the commercial aviation and aftermarket ecosystem. If your work requires clearances, ITAR/EAR controls, or CMMC-level compliance, we are not the right firm and we’ll tell you so up front rather than take the engagement.
No. We do not build airborne or flight-critical avionics, and we do not produce DO-178C safety-of-flight certified software. Our work is the commercial enterprise layer — maintenance-records intelligence, predictive-maintenance analytics, fleet- and asset-data platforms, and MRO-adjacent workflow apps that sit alongside your operation, not inside the aircraft. Keeping that boundary clear is part of why our engagements are fast and low-risk.
We’ll be straight: we have no aerospace or defense clients. Our named case studies are in restaurants (BJ’s, a 200+ location operation held at zero critical defects for four years) and a heavy-equipment marketplace (YardClub, acquired by Caterpillar) — adjacent in engineering rigor and asset-data familiarity, not aviation. For a first aviation engagement we scope a contained pilot on one records type or one fleet to prove the value before you commit, and the accountability is the founder’s, in writing. If you need a vendor with a deep aviation track record, we’ll say so.
The field results are real and public: GE Aerospace, with Microsoft and Accenture, built a generative-AI tool that surfaces critical asset records “in minutes versus days and weeks” instead of manual document review (GE Aerospace, Nov 2024). What you can achieve depends on your records — their format, quality, and volume — which is exactly what we confirm in discovery before promising anything. We build the extraction with validation so the structured output is trustworthy, and we measure accuracy against your own records before it goes live.
The software runs in your own cloud or on-prem environment under your access controls; data and document pipelines are read-only and scoped to what the use case needs; and every engagement starts with an NDA and a security review. We document every data path so your IT and security teams can verify rather than trust. Note again the hard boundary: we handle commercial data only — we do not take classified, ITAR-, or EAR-controlled data, and we’ll decline work that would require it.
You do — completely. The applications, trained models, data and document pipelines, and dashboards transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate, retrain, and extend them. Keep us on a reduced retainer or take the keys; the engagement is built around the handover, not around locking you in.
Most engagements reach production in 4–8 weeks under a fixed-scope, ROI-tied model with one accountable lead, and we typically prove the metric on a contained scope — one records type, one fleet, one hangar — before scaling. Build cost depends on scope — our AI development cost guide gives real ranges. If you want the honest version of why so many enterprise AI pilots stall before production, our analysis of why enterprise AI projects fail is worth reading first.
Thirty minutes · No pitch deck
Bring the problem — a records backlog, unscheduled removals, an audit that takes too long, a stuck MRO workflow — and we’ll tell you honestly whether it’s in our commercial scope, whether your data supports it, what it takes to build, and what it costs to run. If it needs clearances, ITAR controls, or avionics certification, we’ll say that too.