Service · AI
Custom AI and ML, built to reach production.
Custom AI that reaches production and stays there — grounded in your data, run in your cloud, in 4–8 weeks. Not a demo that stalls between the proof of concept and the launch.
No eval, no launch
The real problem
Why so much AI work dies between the demo and production.
The gap is never the model — today's models are extraordinary. It's the engineering and governance around them: choosing the approach, preparing the data, measuring whether the system is right before it ships, integrating it inside your security boundaries, and operating it after launch.
Get that wrong and projects die on data quality, weak controls, cost, and unclear value. The prize is real — but that surrounding system is what decides whether AI work returns anything.
Of generative AI projects abandoned after proof of concept by end of 2025.
Of organizations can link any EBIT impact to AI — 80%+ report none.
What you build
What AI development delivers — by what you're building.
"AI development" isn't one thing — it's a spectrum of builds, each answering a different business question.
AI proof of concept
Tests feasibility on your own data before you commit budget.
A go/no-go answer in weeks, not a six-figure bet on a hunch.
AI MVP
The smallest AI product you can put in front of real users to learn what moves the metric.
Real-world validation before full investment.
Custom AI & ML models
Bespoke models — classical ML, deep learning, or foundation models — chosen for the problem and trained on your data.
Accuracy an off-the-shelf tool can't match.
Full AI product development
End-to-end build of an AI product — model, software, integrations, and operations.
A system your team can trust and operate, not a model in a notebook.
ML engineering & MLOps
Deployment, monitoring, and drift detection that turn a working model into reliable software.
You catch drift before your customers do.
The focused practices
For deeper builds, this page routes to LLM applications, autonomous agents, generative AI, and enterprise-scale programs.
One front door, then the right room.
Most AI dies after the proof of concept. 30%+ of GenAI projects are abandoned after the PoC. We gate every build on evals from your real data and ship to production — no eval, no launch.
As of June 2026 · revisit quarterly
What disciplined AI development changes — the measured impact.
Independent industry findings — cited as third-party evidence, not Silicon Prime's own client results.
Abandoned after PoC. Of generative AI projects, by end of 2025 — the gap disciplined delivery closes.
Annual value. Generative AI could add across 63 use cases — for the organizations that ship it.
Can link EBIT to AI. Of organizations — 80%+ report none, because the work never reached production.
What's included
What AI development services cover end to end.
The difference between a system that ships and a model that gets shelved.
Use-case scoping & feasibility
We map where AI pays off and return a costed build plan with projected ROI — the honest "don't build this yet" call included.
Data assessment & pipelines
We assess your data and build the preparation and pipelines the model needs. Most AI failures trace back here.
Model approach selection
Classical ML, deep learning, or a foundation model — chosen for the problem, not the headlines. The cheapest reliable method that hits the metric wins.
Evaluation suite & metrics
Before launch, the system is tested against a task-specific suite built from your real data. No eval, no launch.
Guardrails, safety & oversight
Bias review, guardrails, and human-in-the-loop oversight designed in — the system defers to a person when stakes or uncertainty demand it.
Secure integration
Authenticated, permissioned access with explicit data boundaries — inside the controls your security team runs, not around them.
MLOps, deployment & monitoring
Automated deployment, production monitoring, and drift detection — so you're alerted the moment the system slips.
Documentation & handover
Documentation and a trained team, so you can own and operate the system after we step back.
What you get — all assigned to you under full work-for-hire IP
How it runs
How an AI development engagement runs.
One accountable lead, fixed scope, no handoffs — powered by our Aegis AI production discipline.
STEP 01
Scope
Start from your business goal and define the success metrics we'll be judged on.
Output: a ranked use case & a metric set
STEP 02
Plan
Assess the data, choose the approach, and present an ROI-backed plan before any build begins.
Output: a costed plan, economics seen
STEP 03
Build
Develop in your own cloud tenant, evaluate against a task-specific suite, and wire it to your systems securely.
Output: a working system, evals passing
STEP 04
Ship & operate
Staged rollout to production, with monitoring live and your team trained to run it.
Output: a system in production & a team that owns it
Track record
Production is the requirement, not the hope.
Plenty of teams can get a demo working — we build past the proof-of-concept to a system that keeps running once real load arrives.
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.
Sustained release cadence · 200+ locations · 4+ years
BJ's Restaurants — for a 200+ location chain we didn't stop at "it works." We took delivery itself into production: release cadence went from every two weeks to twice a week, and a zero-critical-defect record held for four-plus years. That sustained record — not a one-off launch — is what production-grade means.
Why build it with us.
We ship to production — the whole differentiator. Evals before launch, staged rollout, monitoring after — 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.
Engine- and approach-agnostic. Classical ML, deep learning, or a foundation model — OpenAI, Claude, or Gemini — chosen on the merits. No partnership steers it.
Founder-led, one accountable lead. 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 assigned to you under full work-for-hire; your team trained to run it.
Where we build first
Where we build first.
Healthcare
Clinical and operational AI inside HIPAA-compliant architectures — every decision logged and auditable.
Healthcare software →Fintech
Fraud detection and real-time decisioning where every output carries an audit trail and conservative, defensible logic.
Fintech software →Ecommerce
Recommendation, dynamic pricing, and demand models on your live catalog and transaction data — measured against revenue, not vanity accuracy.
Ecommerce software →Questions buyers ask before commissioning.
Thirty minutes · no pitch deck
Ready to build AI that reaches production?
Bring the problem — we'll tell you honestly whether AI is the right tool, which kind of build it needs, and what it takes to ship and run.