Silicon Prime delivers AI development services for the enterprise — from a first proof of concept or AI MVP through to a production AI product, with the ML engineering and MLOps to keep it running.
Engineered for reliability, governed for risk, and shipped on a production cadence — not a model that never leaves the notebook.
Custom AI and ML built around your problem and your data — scoped to learn fast, engineered to enterprise standards, and designed so a working prototype has a direct path to production.
The smallest version of an AI product you can put in front of real users — scoped to learn fast, and built so a working MVP has a direct path to production, not a rewrite.
AI proof of concept development that tests feasibility on your own data — answering whether an idea is worth building before you commit a full budget to it.
Bespoke AI and machine learning built around your problem and your data — from model selection through to a system your team can operate, with the trade-offs made explicit.
End-to-end AI product development services — from use-case selection to a shipped, monitored product — so the first thing you launch is the thing that actually moves the business.
The engineering that turns a model into software — data pipelines, training, evaluation, and inference built to enterprise standards of reliability and observability.
Automated deployment, monitoring, and drift detection — so models ship on a cadence, stay reliable in production, and never quietly degrade after launch.
AI is easy to prototype and hard to run. Every engagement includes the parts that make a model survive contact with real users and real data.
Every AI development engagement runs on the same hands-free lifecycle. You set direction; we carry the build — and the loop keeps improving the model long after launch.
You share the business goal or use case. That's your only required input.
We translate it into clear requirements, success metrics, and the evaluation criteria the model will be judged on.
We assess your data, design the model approach, and present a costed plan with projected ROI before any build begins.
We build, evaluate, and ship faster than you've experienced — with the defect-reduction edge that lets us move at speed safely.
Post-launch we track real usage, measure ROI and model quality in real time, and catch drift before your users do.
We A/B test in-market to find what works, propose the next improvements, and lay out the forward roadmap.
↺ A continuous loop — insights from steps 5–6 feed straight back into strategy ● Model compounds in value
Shipping AI to production is where speed and safety usually fight. BJ's Restaurants, a 200+ location enterprise, runs a demanding production environment — and with Aegis AI the team sustained twice-weekly production releases with zero critical defects for the past year. See the full Aegis AI proof.
We are an AI lab born out of Stanford, building Responsible AI for the enterprise since 2011. Custom AI development is our core discipline, and generative AI development is a specialization of it — so whether your build is a classical ML model or an LLM-powered product, the same production rigor behind Aegis AI, our enterprise production suite, goes into every model we ship: eval-driven delivery, governance by design, and a cadence measured in releases, not slide decks.
The result: AI systems your team can trust, operate, and improve — built to back your people, not replace them. More than an AI software development company, we ship governed systems your team can own. If your build leans on large language models, start with our generative AI development services; either way, see how we think about human-led AI, or talk to us about your use case.
AI systems your team can trust, operate, and improve — built to back your people, not replace them.
The questions product and engineering leaders ask before committing a budget to a custom AI build.
AI development services design, build, and deploy custom AI and machine learning systems — from an early proof of concept or MVP through to a production AI product. We cover the full path: use-case scoping, data and model work, ML engineering, and the MLOps that keeps a model reliable in production, not just accurate in a notebook.
Yes. An AI proof of concept tests whether an idea is feasible on your real data; an AI MVP is the smallest version you can put in front of users to learn from. We build both, scope them so they answer a clear question, and design them so a successful prototype has a direct path to production rather than a rewrite.
It depends on whether AI is core to your product and whether you can hire and retain the specialists to run it. Partnering with an AI lab gives you senior engineering and Responsible AI governance from day one, with knowledge transfer so your team can own the system over time. We make the trade-off explicit rather than pushing you toward a permanent dependency.
Cost is driven by scope, data readiness, and how far you are going — a focused proof of concept is a fraction of a full production product with monitoring and governance. The biggest cost factors are the state of your data, the accuracy and latency bar you need, and integration complexity. We scope each engagement, name the drivers, and give you a costed plan with projected ROI before any build begins.
We treat a model as a system, not a one-off result. Every build ships with an evaluation suite, guardrails, secure integration, and MLOps — automated deployment, monitoring, and drift detection. As a Responsible AI lab, we build in human oversight and governance so you can measure quality in production and catch regressions before your users do.
Tell us what you're trying to build. We'll scope it, name the trade-offs, and give you a costed path to production.
Thirty minutes. No pitch deck. We reply within 48 hours.