Custom AI and ML, built to reach production.
We build custom AI systems that make it into production and stay there — scoped against a real business goal, grounded in your own data, evaluated before launch, run in your own cloud after.
From a proof of concept to an MVP in front of real users to a full AI product your team owns. Fixed scope, ROI-tied payment, full IP — production in 4–8 weeks, not a demo that stalls.
Because building a model that impresses in a sandbox and building a system a business can depend on are two different jobs — and most AI development services only do the first. The failures cluster on data quality, weak risk controls, escalating cost, and unclear business value.
The prize is real — McKinsey estimates generative AI could add $2.6–$4.4 trillion annually across 63 use cases (McKinsey, 2023) — but adoption alone doesn’t capture it.
The gap is never the model — today’s models are extraordinary. The gap is the engineering and governance around it: choosing the right approach, preparing the data, measuring whether the system is right before it ships, integrating it inside your security boundaries, and operating it after launch.
That surrounding system is the entire job — and it’s what decides whether AI development services return anything at all.
“AI development” isn’t one thing. It’s a spectrum of builds, each answering a different business question. For each: what it does, the benefit it produces, and how that plays out.
A focused build that tests feasibility on your own data before you commit budget to a full system. Benefit — a go/no-go answer in weeks, not a six-figure bet on a hunch.
Example: a lender runs a PoC on two years of its own application data and learns in three weeks that a fraud-scoring model clears the accuracy bar — so the full build starts with the risk already retired, instead of discovering the data won’t support it after the spend.
The smallest version of an AI product you can put in front of real users to learn what actually moves the metric. Benefit — real-world validation before full investment, and a head start on the production system.
Example: a SaaS team ships an AI MVP to a pilot cohort and finds users want one feature far more than the three they planned — so the roadmap reorders around evidence instead of opinion.
Bespoke models — classical ML, deep learning, or foundation models — chosen for the problem rather than the trend, trained and tuned on your data. Benefit — accuracy on your task that an off-the-shelf tool can’t match, because it’s fit to your data and your edge cases.
Example: a manufacturer’s defect-detection model is trained on its own line photography rather than a generic vision API — so it flags the specific flaws that matter to its product, not the ones a stock model happens to know.
End-to-end development of an AI-powered product — model, software around it, integrations, and the operations to keep it running. Benefit — a system your team can trust, operate, and improve, not a model that sits in a notebook.
Example: a logistics company gets a routing product wired to its live order and fleet data with dashboards and alerts — so dispatchers use it daily instead of exporting CSVs to a data scientist.
The deployment, monitoring, and drift detection that turn a working model into software a business can rely on. Benefit — the system keeps performing after launch, and you find out it’s drifting before your customers do.
Example: a recommendation model’s quality is tracked in production and an alert fires when accuracy slips after a catalog change — so it’s retrained on a schedule instead of quietly degrading for a quarter.
For the deeper builds, this page routes to our focused practices — LLM applications, autonomous agents, generative AI, and enterprise-scale programs. Benefit — one front door, then the right room.
Example: this page is the front door; each of those is the room — so a team that already knows it’s building a language application starts in the practice that fits, not at the lobby.
The scope below is the difference between a system that ships and a model that gets shelved.
We map where AI genuinely pays off, pressure-test feasibility against your data, and return a costed build plan with projected ROI — run as our AI readiness assessment, with the honest “don’t build this one yet” call included.
We assess what data you have, what it can support, and what’s missing — then build the preparation and pipelines the model needs. Most AI failures trace back here, so it’s where we start, not where we improvise.
We choose the approach for the problem — classical ML, deep learning, or a foundation model — rather than defaulting to whatever’s in the headlines. The cheapest reliable method that hits the metric wins.
Before anything ships, the system is tested against a task-specific evaluation suite built from your real data — accuracy, the failure cases that must never reach production, and the metric you’re paying to move. No eval, no launch.
Bias and safety review, guardrails, and human-in-the-loop oversight are designed in — the system escalates or defers to a person where the stakes or the uncertainty demand it.
We integrate with your systems through authenticated, permissioned access and explicit data boundaries — inside the controls your security team already runs, not around them.
Automated deployment, production monitoring, and drift detection, so the system keeps performing and you’re alerted when it doesn’t — the discipline that separates a live product from a one-time demo.
Documentation and a trained team, so you can own, operate, and improve the system after we step back.
What you get when you hire us — all assigned to you under full work-for-hire
One accountable lead, fixed scope, no handoffs — the delivery model behind every build, powered by our Aegis AI production discipline.
Start from your business goal, not a technology. We define requirements and the success metrics we’ll be judged on.
Output: a ranked use case & a metric set
Assess the data, choose the approach, and present an ROI-backed build plan with a costed estimate before any production build begins.
Output: a costed plan you’ve seen the economics of
Develop the system in your own cloud tenant, evaluate it against a task-specific suite, and wire it to your systems through governed, permissioned access.
Output: a working system, evals passing
Staged rollout, then production, with monitoring and drift detection live and your team trained to run it.
Output: a system in production & a team that owns it
Most engagements reach production in 4–8 weeks, full work-for-hire IP signed at kickoff, payment tied to the ROI we scoped — we’re paid when the work earns.
We don’t show you a logo wall. We show you systems that reached production and stayed there — across a traditional enterprise, a long-lived product, and a marketplace built to acquisition.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We’ll tell you plainly when an AI build is the wrong move for your problem, which a vendor paid to build one won’t.
We ship to production — that’s the whole differentiator. Anyone can demo a model. The discipline that gets a build past the proof of concept Gartner says most projects die at — evals before launch, staged rollout, monitoring after — is what we’re known for, proven across four years and a 200+ location chain.
Stanford-rooted, Responsible AI since 2011. Governance, safety, and human oversight are the founding charter, not a compliance bolt-on — which matters when the system makes decisions your business is accountable for.
Engine- and approach-agnostic. We pick classical ML, deep learning, or a foundation model — and across foundation models, OpenAI, Claude, or Gemini — on the merits of your problem. No partnership steers the recommendation.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes it answers for it, and payment is tied to the ROI we scoped.
Built to transfer. Models, code, evals, and pipelines are assigned to you under full work-for-hire; your team is trained to run and extend the system when we step back.
Clinical and operational AI inside HIPAA-compliant architectures, every decision grounded, logged, and auditable. Healthcare software →
Fraud detection and real-time decisioning where every output carries an audit trail and conservative, defensible logic. Fintech software →
Recommendation, dynamic pricing, and demand models built on your live catalog and transaction data, measured against revenue, not vanity accuracy.
What teams want to know before they commission a custom AI build.
The end-to-end work of turning a business problem into a working AI system: scoping and feasibility, data preparation, model selection and training, evaluation, secure integration, deployment, and the MLOps to keep it running. The model is a fraction of it — the engineering and governance around the model is what makes it dependable, and it’s the difference between a system in production and a proof of concept that stalls.
Both, and we’ll tell you which one you need. A PoC tests feasibility on your data before you commit; an MVP puts the smallest real product in front of users to validate it; a full build is the production system. Many engagements start small precisely because Gartner finds most projects die after the PoC stage — we’d rather prove the path before you fund the whole thing.
If an off-the-shelf tool solves your problem, we’ll say so — building custom only pays when your data, your edge cases, or your integration needs make a generic product fall short. The scoping engagement returns that recommendation honestly, including “buy this and don’t hire us for it,” because shipping a build you didn’t need helps no one.
Most builds reach production in 4–8 weeks under a fixed-scope engagement with one accountable lead, and payment is tied to the ROI we scope. Cost is driven by scope, data readiness, and how far you’re taking it — we name the drivers and give a costed plan with projected ROI before any build begins. Our AI development cost guide gives real ranges.
By treating production as the requirement, not the hope. We gate the build on an ROI-backed plan, evaluate against a task-specific suite built from your real data before launch, ship behind a staged rollout, and run monitoring and drift detection after. It’s the same discipline that holds a 200+ location business at twice-a-week releases with zero critical defects across four years (BJ’s Restaurants).
The system runs in your own cloud tenant under your access controls, integrations use authenticated, permissioned access with explicit data boundaries, and every engagement starts with an NDA and a security review. We document every data path so your team verifies rather than trusts, and we build inside your security controls rather than around them.
You do — completely. Models, code, evaluation suites, and data pipelines 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.
This is the front door for custom AI development; those are the focused practices it routes to. If you’re building a language application, see LLM development; for systems that take actions autonomously, agentic AI; for content, image, or code generation, generative AI; for organization-wide programs, enterprise AI. Not sure which? That’s what the scoping call is for.
Thirty minutes · No pitch deck
Bring the problem — we’ll tell you honestly whether AI is the right tool, which kind of build it needs, what it takes to ship, and what it costs to run.