Systems that generate real work, governed and shipped to production.
We build generative AI that produces the output your business runs on — drafted documents, on-brand content, working code, images, and synthetic data.
Each one is grounded in your own information, checked against your own standards, and shipped inside your own cloud. Fixed scope, one accountable lead, production in 4–8 weeks.
Because generating text is the easy part — and the part everyone demos. A model drafts a slick paragraph in a sandbox, the room is impressed, and then the project hits the wall that actually matters.
The draft cites a policy that doesn’t exist. The generated code looks right and silently breaks an edge case. The “personalized” content goes off-brand, and no one can prove the output is correct at scale. So the pilot stays a pilot.
The gap is never the model — today’s models generate fluently across language, code, and images. The gap is the system around the generation: grounding it in your real data so it doesn’t invent, evaluating output quality before it reaches a customer or a codebase, putting a human where a wrong answer is expensive, and monitoring drift and cost once it’s live.
Generative AI isn’t one product; it’s a capability that earns its keep in a handful of specific, high-volume production processes. For each: what it generates, the benefit it produces, and a one-line illustration.
Drafts contracts, reports, summaries, RFP responses, and policy documents from your templates and source material, with every claim traceable to a grounding source. Benefit — turnaround on document-heavy work drops from days to minutes, with fewer errors.
For example, an analyst feeds a 90-page filing in and gets a structured summary with citations in seconds instead of spending an afternoon reading and re-typing — and the citations let a reviewer verify rather than trust.
Produces first-draft marketing copy, product descriptions, and personalized variants on-brand and at volume, with human approval before anything publishes. Benefit — more content shipped per marketer, without diluting the brand voice.
For example, a merchandiser generates a hundred product descriptions in the house style in an afternoon — work that used to be a week of copywriting — then edits rather than writes from scratch.
Generates boilerplate, tests, refactors, and documentation inside your repos and review process — never merged unread. Benefit — faster delivery on routine engineering, with quality held by your existing review gates.
For example, a developer scaffolds a service and its test suite from a spec in minutes, then spends the saved time on the design decisions that actually need a human.
Generates product imagery, design variations, and media assets to brief — original output, never scraped or a real logo without rights. Benefit — creative iteration that used to cost a studio day happens in an hour.
For example, a team generates a dozen on-brand banner variants to test instead of waiting on an external shoot — so a campaign launches the same week it’s conceived.
Generates realistic, privacy-safe datasets to train models, test systems, and fill gaps where real data is scarce, sensitive, or regulated. Benefit — teams build and test on representative data without exposing real customer records.
For example, a fintech generates a synthetic transaction set that mirrors real fraud patterns to train a detector — without ever moving regulated production data into a dev environment.
Generates direct, sourced answers over your documents and data — the assistant composes a grounded reply, not just a search result. Benefit — staff get a written answer with its source instead of a list of links to read.
For example, a support rep asks a policy question and gets a composed, cited answer in seconds rather than digging through a wiki — so the customer waits seconds, not minutes.
The scope below is the difference between generated output you can ship and a pilot that never clears review.
We map where generation genuinely pays off, what “good output” means for it, and what it will cost to build and run — delivered as our AI readiness assessment, with the honest “don’t generate this one” call included.
Output is generated against your documents, data, and brand standards — not from training-data guesswork — and every generated answer can cite its source. Grounding accuracy is measured against your own content before launch.
We decide on evidence, not hype: most generation needs strong prompting and grounding; fine-tuning is used only where the task and the data justify it. The choice is benchmarked on your workload across our AI development work.
Before output reaches a customer, a codebase, or a campaign, it’s tested against a golden set built from your real material — accuracy, factual grounding, tone, brand fit, and the failure cases that must never ship — with regression checks so quality doesn’t drift release to release.
Generated output passes through content guardrails and prompt-injection defenses, and human-in-the-loop review is designed in at the points where a wrong output is costly — it routes to a person rather than publishing on a guess.
We wire generation into your stack, auth, and data boundaries through scoped, permissioned access, ship it behind a staged rollout, and instrument it for drift and token cost — then train your team to run the evals, tune the prompts, and own it.
What you get when you hire us — all assigned to you under full work-for-hire IP transfer
The same delivery model behind all our AI development services, tuned for generation — one accountable lead, fixed scope, no handoffs.
Scope the generation use case, the source data, and what “good output” means in measurable terms.
Output: a ranked, costed plan & the quality metrics we’ll be judged on
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 & a grounding architecture
Develop the generation pipeline in your own cloud tenant, wired to your data through governed access, with guardrails and human-review gates in place.
Output: a working system behind your access controls
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
Production in 4–8 weeks, full IP assignment signed at kickoff, payment tied to the ROI we agree up front — we make the case in business terms or we don’t take the build.
Generated output is only as trustworthy as the engineering discipline underneath it — and code is the highest-stakes thing a generative system produces, because it ships into a live business. That is precisely the discipline we’re known for.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20-plus years of production engineering, personally accountable for every engagement. We’ll tell you plainly when generative AI is the wrong tool for a problem, which a vendor paid to ship one won’t.
Responsible AI is the founding charter. For a system that generates content, code, or decisions in your name, governance — what it may produce, when a human must approve, how output is audited — 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 transfer; your team is trained to run and extend the system when we step back.
Clinical-documentation drafting, intake summarization, and patient-communication generation inside HIPAA-compliant architectures, every output grounded and logged. Healthcare software →
Document generation, report drafting, and synthetic data for model training where every generated output carries an audit trail and conservative, sourced grounding. Fintech software →
Product-description, content, and image generation from live catalog data, on-brand and reviewed before publish, throughput measured weekly. Ecommerce software →
What teams want to know before they build generative AI on their own data.
It’s building production systems that generate useful output — documents, content, code, images, or synthetic data — grounded in your own information, evaluated for quality before release, and governed so a wrong output never ships unchecked. The model that does the generating is the easy 5%; the grounding, evaluation, guardrails, integration, and monitoring around it are the engineering that decides whether generative AI development returns anything.
Grounding plus measurement. The system generates against your approved sources, every generated answer can cite where it came from, and we measure the factual-grounding rate against a golden set built from your real material before launch — then monitor it after. Where confidence is low or the output is high-stakes, it routes to a person rather than publishing on a guess.
Largely emphasis. A conversational assistant converses; an LLM app wires a model into a feature; generative AI development is the work where the output itself — the document, the code, the image, the dataset — is the product, and the engineering centers on making that output accurate, on-brand, and safe at scale. The grounding, evals, and review discipline are shared across all of them, which is why the same delivery model covers our broader AI development services.
We decide on evidence. Most enterprise generation is best served by strong prompting plus retrieval grounding (RAG), which keeps output current and traceable; fine-tuning is used only where the task pattern and the data genuinely justify the cost. We benchmark the options on your workload during design and tell you which wins and why.
Whichever wins your evaluation. We benchmark the candidates on your real generation tasks during design and route accordingly — and because the system sits behind a model abstraction, switching later is a config change, not a rebuild.
The system runs in your own cloud tenant under your access controls; integrations use scoped, permissioned access; and every engagement starts with an NDA and a security review. Business API traffic to the major providers isn’t used to train their models by default, generated assets are original (no scraped images or real logos without rights), and we document every data path so your team verifies rather than trusts.
You do, completely. Prompts, evaluation suites, and code transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate and extend it. Keep us on a reduced retainer or take the keys — the engagement is built around the handover.
Most generative systems reach production in 4–8 weeks under a fixed-scope engagement with one accountable lead. Build cost depends on scope — our AI development cost guide gives real ranges — and run cost is token economics we model before building, so the first invoice is a forecast you’ve already seen. Want a feasibility read first? That’s our AI consulting entry point.
Thirty minutes · No pitch deck
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.