Service · AI
Systems that generate real work — governed and shipped.
We build generative AI that produces the work your business runs on — documents, content, code, images, and synthetic data — grounded in your data, evaluated before output ships, and deployed in your own cloud.
Grounded, then evaluated
The real problem
Why so much generative AI never leaves the pilot.
Generating text is the easy part everyone demos. A model drafts a slick paragraph in a sandbox, the room is impressed — then the draft cites a policy that doesn't exist, the generated code silently breaks an edge case, and no one can prove the output is correct at scale.
The gap is never the model; it's the system around the generation — grounding it in your real data so it doesn't invent, evaluating quality before output ships, and putting a human where a wrong answer is expensive. Without that, the pilot stays a pilot.
Where it works
Where enterprises put generative AI to work — and what each delivers.
A capability that earns its keep in a handful of high-volume processes.
Document & knowledge generation
Drafts contracts, reports, summaries, and RFP responses from your templates, every claim traceable to a source. Turnaround drops from days to minutes.
Content & marketing generation
Produces on-brand marketing copy, product descriptions, and personalized variants, with human approval before publishing. More content per marketer, brand voice intact.
Code generation & engineering acceleration
Generates boilerplate, tests, refactors, and docs inside your repos — never merged unread. Faster routine engineering, quality held by your review gates.
Image, design & media generation
Generates product imagery, design variations, and media to brief — original output, never scraped. A studio day of iteration happens in an hour.
Synthetic data generation
Generates realistic, privacy-safe datasets where real data is scarce, sensitive, or regulated. Build and test on representative data without exposing real records.
Retrieval-grounded answers (RAG)
Generates direct, sourced answers over your documents — a composed reply, not a search result. Staff get an answer with its source, not a list of links.
Generating text is the easy part everyone demos. We ground it in your data, gate it on evals built from your real material, and put a human where a wrong output is expensive — so the pilot actually ships.
As of June 2026 · revisit quarterly
What generative AI actually moves — the measured impact.
Independent industry findings — never Silicon Prime's own client results.
In annual value. Across 63 use cases — roughly 75% of it in customer ops, marketing/sales, software engineering, and R&D.
Less time to write new code. For developers using gen-AI tools — refactoring in ~two-thirds, documenting in half.
Of AI/analytics data, synthetic. Projected share generated synthetically — privacy-safe data where real records are scarce or regulated.
What's included
What generative AI development covers.
The difference between output you can ship and a pilot that never clears review.
Use-case scoping & feasibility
We map where generation pays off and what it costs to build and run — with the honest "don't generate this one" call included.
Grounding & retrieval (RAG)
Output is generated against your documents and brand standards, not training-data guesswork, and every answer can cite its source. Grounding accuracy is measured before launch.
Model selection — prompt, RAG, or fine-tune
We decide on evidence, not hype: most generation needs strong prompting and grounding; fine-tuning only where the data justifies it — benchmarked on your workload.
Evaluation suites & quality gates
Before output ships, it's tested against a golden set built from your real material — accuracy, grounding, tone, brand fit, and the failures that must never ship — with regression checks against drift.
Guardrails, injection defense & review
Output passes through guardrails and prompt-injection defenses, and human-in-the-loop review is built in where a wrong output is costly — it routes to a person rather than guessing.
Secure integration & deployment
We wire generation into your stack and data boundaries through scoped access, ship behind a staged rollout, instrument it for drift and token cost — then train your team to own it.
What you get — all assigned to you under full work-for-hire IP transfer
How it runs
How a generative AI engagement runs.
The same delivery model behind all our AI development services — one accountable lead, fixed scope, no handoffs.
STEP 01
Discover
Scope the generation use case, the source data, and what "good output" means in measurable terms.
Output: a ranked, costed plan & the quality metrics
STEP 02
Design
Build the evaluation set from your real material and decide prompting vs. RAG vs. fine-tuning on evidence, not fashion.
Output: a golden test set & grounding architecture
STEP 03
Build
Build the pipeline in your own cloud tenant, with governed data access, guardrails, and human-review gates.
Output: a working system behind your access controls
STEP 04
Deploy & enable
Shadow mode, then a pilot, then wide — output quality, acceptance, and cost measured weekly, your team trained to operate it.
Output: a production system & a team that owns it
Track record
The production discipline behind generated output you can trust.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — personally accountable for every engagement. We'll tell you plainly when generative AI is the wrong tool.
Aegis AI generation · 200+ locations · 4 years
Output is only as trustworthy as the engineering underneath it — and code is the highest-stakes thing a generative system produces. Through our Aegis AI engine we've run AI-augmented software generation for BJ's Restaurants, a 200+ location enterprise, for four years — from releasing every two weeks to twice a week with zero critical defects sustained.
Why build it with us.
Responsible AI is the founding charter. For a system that generates in your name, governance is the product, not an afterthought — built to back your people, not replace them.
Engine-agnostic. We benchmark OpenAI, Claude, and Gemini on your actual generation tasks and route to whichever wins. No partnership steers the recommendation.
Eval-driven, not demo-driven. Output quality is measured against a golden set before launch and monitored after — the opposite of a slick demo that breaks in production.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes it answers for it.
Built to transfer. Prompts, evals, and code are assigned to you under full work-for-hire IP; your team is trained to run and extend the system when we step back.
Where it earns its keep first
Where generative AI earns its keep first.
Healthcare
Clinical-documentation drafting, intake summarization, and patient-communication generation inside HIPAA-compliant architectures, every output grounded and logged.
Healthcare software →Fintech
Document generation, report drafting, and synthetic data for model training, every output carrying an audit trail and conservative, sourced grounding.
Fintech software →Ecommerce
Product-description, content, and image generation from live catalog data, on-brand and reviewed before publish, throughput measured weekly.
Ecommerce software →Questions buyers ask before they build.
Thirty minutes · no pitch deck
Ready to build generative AI you can actually ship?
Bring the use case — we'll tell you honestly whether generation fits it, whether to prompt, ground, or fine-tune, what it takes to build, and what it costs to run.