On OpenAI, Anthropic & Google — built for production, owned by you.

The one your evaluation results pick — and often a mix. Across the three, OpenAI's GPT-5 family leads on breadth and agentic computer-use, Claude on long-horizon agent and large-context work, and Gemini on multimodal and BigQuery-adjacent workloads. Stage two benchmarks all three on your actual tasks, so the choice is data you own, not anyone's opinion.

No, deliberately. We hold no partnership, reseller margin, or referral arrangement with any model vendor, so our engine recommendation carries no commission. Our cloud and developer-program certifications are infrastructure credentials, separate from model-vendor commercial relationships. We implement all three platforms and switch when evals say switch — and no advice is bent by a vendor.

Everything runs in your own tenant — OpenAI/Azure, Claude via API or Bedrock, or Gemini inside your GCP project — under your SSO, logging, and retention controls. All three vendors' enterprise terms state business API traffic isn't used for model training by default. Every engagement starts with an NDA and a security review, and each data path is documented.

IP ownership is defined in each engagement's contract — and our default is that the code, prompts, evaluation suites, and design assets we build for you are assigned to you. What Silicon Prime retains is our underlying Aegis AI methodology, which is patent-pending and licensed to you for use within your organization. We put the exact assignment terms in writing at kickoff, so there's no ambiguity later.

Typical engagements reach steady state in 4–8 weeks, depending on scope and integration complexity. You get a dedicated pod — a delivery lead, AI engineers, a PM/BA, a designer, and QA — under one accountable lead as your single point of contact. Prefer to keep delivery in-house? Our AI staff augmentation model embeds the same engineers into your team instead.

Because they optimize a demo, not a system. Gartner (2024) projected at least 30% of generative AI projects would be abandoned after proof of concept by end of 2025 — usually from weak data quality, thin risk controls, escalating cost, or unclear value. We engineer for production from day one: an evaluation suite, guardrails, and run-cost modeling before code ships. It's the same patent-pending Aegis AI process behind BJ's Restaurants, where twice-weekly releases across 200+ locations ran a full 12-month window with zero critical defects.

Two budgets: build and run. Build cost depends on scope — our AI development cost guide publishes the real ranges we quote. Run cost is token economics, and it's designable: routing volume traffic to mini/Flash/Haiku tiers and batching non-urgent work typically cuts inference spend sharply. We model your run-rate before building, so the first invoice is a forecast you've already approved.

Your choice, designed in from day one. Some clients keep the pod on a reduced retainer for model upgrades, eval maintenance, and cost tuning — the same shape as our managed application services. Others take full ownership at handover: runbooks, documentation, the eval suite, and a period of overlap support. The best signal it worked is that you don't need us anymore.