Healthcare
Clinical documentation, prior-auth and claims, patient assistants inside HIPAA tenants.
Service · LLM Development
On OpenAI, Anthropic & Google — built for production, owned by you.
Copilots, retrieval systems, AI agents, and integrations — on whichever of the three frontier engines your workload needs, benchmarked on your tasks and shipped to your own cloud in 4–8 weeks.
Fixed scope Engine-agnostic Steady state in 4–8 weeks
YOUR USE CASE
YOUR DATA
BENCHMARK · EVALS DECIDE
OPENAI CLAUDE GEMINI
What we build
From use case to a production-grade LLM system.
Calling a model API is a sprint; everything below is the actual product — the nine offerings we scope, price, and build.
01
LLM strategy & use-case discovery
A frank readiness assessment that ranks your use cases by ROI and feasibility — including the ones not to build.
02
Custom LLM application development
Full applications with an LLM at the core — built on your stack and business logic, not a generic wrapper.
03
RAG & knowledge systems
Retrieval over your documents and product data — grounding measured before launch, every answer citing its source.
04
AI agents & agentic workflows
Agents that execute multi-step work with scoped tools, staged autonomy, and human approval gates where actions have consequences.
05
Chatbot & copilot development
Customer-facing assistants and internal copilots wired into your real systems — deflection and adoption targets measured weekly.
06
LLM integration into existing products
AI features added to software you already run — structured outputs into your APIs, model abstraction so a vendor swap never forces a rebuild.
07
Workflow & document automation
Extraction, classification, and enrichment at volume — schema-validated outputs with unit economics designed before the first invoice.
08
LLM-powered data analytics
Natural-language access to your warehouse — governed query generation, row-level security respected, every answer traceable.
09
Evaluation, optimization & scaling
Golden test sets, regression evals, drift monitoring, and cost tuning — the layer that turns each model release into an upgrade, not a rebuild.
Included in every engagement
The same delivery discipline.
- ✓Evaluation suites built from your real data
- ✓Deployment in your own cloud tenant
- ✓Full documentation and runbooks
- ✓Model abstraction so a vendor swap is a config change
- ✓Your team trained to run it
- ✓Full work-for-hire IP assignment
All three frontier platforms
One team, fluent in all three engines.
Engine choice is an engineering decision — we benchmark on your workload, not on hype. The strongest architectures often route between all three.
01 / OpenAI
GPT-5.5 · GPT-5.4 · Codex
The most widely adopted platform in enterprise surveys
Frontier capability on a software-update cadence — we build so each release lands as an upgrade, not a rewrite. Customer copilots, audited agents, and high-volume pipelines with mini-tier routing designed in.
Runs in: OpenAI API · Azure OpenAI
02 / Anthropic
Opus 4.8 · Sonnet 4.6 · Haiku 4.5
The engine enterprises trust with long-running agents
A 1M-token context window at standard pricing, excelling at multi-step work that has to explain itself afterward — long-horizon agents with full audit trails, whole-archive document intelligence in a single pass.
Runs in: Claude API · Bedrock · Vertex · Foundry
03 / Google
Gemini 3 · 3.5 Flash · 3.5 Pro
Multimodal depth, built where your data already lives
Reads text, audio, images, video, PDFs, and code in one model — up to 2M tokens of context. Native video and audio intelligence, BigQuery-grounded analytics under your IAM, Flash-economics document processing at volume.
Runs in: Your GCP project · Vertex AI
Independence note. We hold no partnership, reseller, or referral relationship with OpenAI, Anthropic, or Google. The recommendation you get is the one your evaluation results earn; nobody pays us to steer it.
Same engines Engineered
Everyone has the same three engines. Outcomes still diverge wildly. The failure numbers aren't model problems — they're implementation problems. That gap is what you're hiring: the engineering that turns rentable models into systems your business runs on.
How it runs
How LLM development works here.
Four stages, the same shape every time — production steady-state in 4–8 weeks, scope varies but never the gates.
STEP 01
Discover
We inventory the workflows, data, and team readiness behind each use case, then rank by ROI and feasibility — including the ones we'd decline, with reasons.
Output: ranked use-case map · weeks 1–2
STEP 02
Design
Before the first prompt: a golden test set from your real data, numeric success metrics, and a benchmark of GPT-5, Claude, and Gemini tiers on your actual tasks. The engine choice comes out of that data.
Output: engine choice + eval harness
STEP 03
Build
Development happens inside your own tenant — under your SSO, logging, and retention controls. We document every data path, and full IP assignment is signed at kickoff.
Output: working system in your tenant
STEP 04
Ship & scale
Shadow mode, then pilot, then wide — with human-in-the-loop gates wherever the system acts on the world. Runbooks and the eval suite handed over with 30 days of overlap, or the pod stays on retainer.
Output: production launch + trained team
Track record
An LLM feature is only as good as the operation it ships into.
A demo is easy; holding a feature to a release cadence inside a live business is the hard part. Here is the production operation we've kept to that bar.
Aegis AI process · 200+ locations · 4+ yrs
BJ's Restaurants
For 4+ years our Aegis AI process has run releases for a 200+ location operation at twice a week with zero critical defects sustained — the cadence and reliability bar any LLM feature has to meet once real guests and real locations depend on it.
Adjacent evidence — a production software operation held to a release cadence, cited for that reliability, not a shipped LLM product.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. When a use case shouldn't be built, we'll tell you that too.
What you get
What you actually get when you hire us.
A Stanford-rooted Responsible AI lab, founded 2011, run by founder Kelvin Tran — the four commitments below are how the contract is written.
W / 01
Fixed scope, one accountable lead
A dedicated pod under a single point of contact — no handoffs, no scope drift invoiced as "discovery."
W / 02
Payment tied to ROI
Success metrics are defined numerically before we build, and payment is tied to hitting them.
W / 03
Engine-agnostic by design
Evals before prompts, abstraction over every model API — a vendor swap is a config change.
W / 04
Your people, more capable
Every engagement trains your team to run the system after we leave — Responsible AI in practice.
Where we build
LLM systems shaped by your industry's constraints.
The constraint set changes by industry — the compliance regime, the data shapes, the cost of a wrong answer. Twenty-eight sectors we build for.
Fintech
KYC and onboarding extraction, compliance review, fraud-investigation copilots with audit trails.
Banking
Service assistants and policy intelligence to banking security standards.
Insurance
Claims intake at volume, policy Q&A, underwriting copilots that cite the clause.
Ecommerce & retail
Catalog enrichment, shopping assistants, review intelligence, support deflection.
SaaS & technology
In-product copilots and NL features behind your SLA, token economics protecting margin.
Restaurants
Guest ordering and reservation assistants, menu ops across hundreds of locations.
Sports
Athlete and coach copilots, training-content generation with expert review.
Fitness
Member-engagement and habit-coaching assistants where retention is the business.
Education
Tutoring assistants with guardrails, curriculum content with educator review.
Travel & hospitality
Booking and itinerary assistants, multilingual guest support, review intelligence.
Real estate
Listing-content generation, lease and contract abstraction, buyer-qualification.
Construction
Bid and RFP intelligence, safety-compliance review, equipment-marketplace systems.
Logistics & supply chain
Shipment-exception triage, BOL/customs automation, carrier-communication assistants.
Industrial manufacturing
Maintenance-manual Q&A on the floor, downtime-log intelligence, supplier automation.
Energy & utilities
Field-service copilots over manuals, outage communications, regulatory-filing automation.
Oil & gas
Inspection-report intelligence, HSE-compliance review, operations-log analysis.
Petrochemical
Safety-data-sheet intelligence, batch-record review, plant-scale compliance docs.
Aerospace & defense
Technical-manual intelligence and maintenance copilots for air-gapped environments.
Telecom
Support deflection at carrier scale, network-log triage, plan-recommendation assistants.
Social media
Moderation at volume, creator copilots, trust-and-safety triage with humans on edges.
Physical AI & robotics
LLM planning layers: instruction parsing, task decomposition, human approval gates.
Legal & professional
Contract review at portfolio scale and research with citations — 1M-token data rooms.
Media & entertainment
Archive search across video and audio, metadata generation, rights-document intelligence.
Automotive & mobility
In-vehicle and dealer-support assistants, warranty-claim document processing.
Government & public sector
Citizen-service assistants, records processing, request triage — auditability first.
HR & talent
Policy Q&A, job-content generation, screening assistance, bias evals as a gate.
Marketing & advertising
On-brand content generation with review workflows, campaign summarization, research.
Questions buyers ask before they build.
Which engine should we build on?+
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.
Are you a partner or reseller of any model vendor?+
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.
How do you handle data security?+
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.
Who owns the code, prompts, and IP?+
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.
How long until production, and what team do we get?+
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.
Why do so many LLM projects stall before production — and how do you avoid it?+
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.
What does LLM development cost?+
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.
What happens after launch — do you maintain it?+
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.
Thirty minutes · no pitch deck
Ready to put an LLM to work — properly?
Bring the use case — we'll tell you honestly which engine fits, what it takes to build, and what it costs to run at your volume.