You provide direction
You share the business goal or use case. That's your only required input — owned by your business team.
AI-Powered · Generative AI & LLM RAG · Agents · Fine-tuning / Ship to production
Generative AI development services that ship to production.
Enterprise generative AI and large language model development — RAG applications, AI agents, fine-tuned models, and ChatGPT and OpenAI integrations. Engineered for reliability, governed for risk, and shipped on a production cadence — not a demo that never leaves the lab.
From use-case selection and AI readiness to a costed build plan, with evaluation, guardrails, and production monitoring in every engagement — so the first thing you ship is the thing that actually moves the business.
01 / What we build
Generative AI, built
to survive production.
Generative AI is easy to demo and hard to run. We build the systems that move the business — each one shipped with the evaluation, guardrails, and monitoring real users demand.
RAG
RAG application development
Retrieval-augmented generation grounded in your own documents and data — accurate, traceable answers with the retrieval pipeline and evaluation to keep them reliable.
Agents
AI agent development
Agentic systems that plan, call tools, and complete multi-step work — built with the guardrails and observability enterprise workflows require.
Models
Custom LLM development & fine-tuning
Fine-tuning and model adaptation when it improves accuracy, cost, or latency — with the prompting-vs-RAG-vs-fine-tuning trade-off made explicit and evidence-backed.
Integration
ChatGPT & OpenAI API integration
Secure generative AI integrations into existing products and internal tools — auth, rate limiting, data handling, and evaluation included.
Strategy
Generative AI consulting
From use-case selection and AI readiness to a costed build plan — so the first thing you ship is the thing that actually moves the business.
Quality
Evaluation & guardrails
Every system ships with an evaluation suite, guardrails, and production monitoring — so you can measure quality and catch drift before your users do.
02 / What's included
A production system,
not a prototype.
Generative AI is easy to demo and hard to run. Every engagement includes the parts that make it survive contact with real users.
- Use-case scoping and feasibility, with a costed build plan
- RAG pipeline: ingestion, embeddings, retrieval, and grounding
- Model selection — prompting, RAG, or fine-tuning, decided on evidence
- Evaluation suite with task-specific metrics and regression checks
- Guardrails, prompt-injection defense, and human-in-the-loop review
- Secure integration with your stack, auth, and data boundaries
- Production monitoring, drift detection, and cost controls
- Documentation and handover so your team can own it
03 / How we work
From idea to production,
owned end to end.
Every generative AI engagement runs on the same hands-free lifecycle. You set direction; we carry the build — and the loop keeps improving the system long after launch.
We define success
We translate it into clear requirements, success metrics, and the evaluation criteria the system will be judged on.
ROI-backed plan
We design the approach — prompting, RAG, or fine-tuning — and present a costed plan with projected ROI before any build begins.
Ship via Aegis AI
We build, evaluate, and ship faster than you've experienced — with the defect-reduction edge that lets us move at speed safely.
Measure & catch drift
Post-launch we track real usage, measure ROI and model quality in real time, and catch drift before your users do.
A/B test & roadmap
We A/B test in-market to find what works, propose the next improvements, and lay out the forward roadmap.
A continuous loop — insights from steps 5–6 feed straight back into strategy ● The system compounds in value
04 / Where AI pays off
Where generative AI pays off.
The highest-ROI places we deploy generative AI inside the enterprise — chosen for impact, not novelty.
Support
Support automation
AI agents and assistants that resolve routine tickets automatically and escalate the rest with full context.
Knowledge
Document intelligence
Turn contracts, policies, and knowledge bases into accurate, traceable answers with retrieval-augmented generation.
Copilots
Internal copilots
Give your teams copilots that draft, summarize, and search across your own tools and data.
Workflows
Process automation
Automate the document-heavy, judgment-heavy workflows that rules engines never could.
05 / Proof · BJ's Restaurants
Headline case · 12-month live data
Production rigor, proven — on a live enterprise system.
Shipping AI to production is exactly where speed and safety usually fight. BJ's Restaurants, a 200+ location enterprise, runs a demanding production environment — and with Aegis AI the team sustained twice-weekly production releases with zero critical defects for the past year. The same eval-driven edge goes into every model we ship. See the full Aegis AI proof.
2×/wkRelease cadence sustained
0Critical defects · 12 months
200+Locations supported
06 / Why Silicon Prime
A Responsible AI lab that
ships to production.
We are an AI lab born out of Stanford, building Responsible AI for the enterprise since 2011. Generative AI is our core discipline — and the same production rigor behind Aegis AI, our enterprise production suite, goes into every model we ship: eval-driven delivery, governance by design, and a cadence measured in releases, not slide decks.
The result: generative AI systems your team can trust, operate, and improve — built to back your people, not replace them. See how we think about human-led AI, or talk to us about your use case.
Generative AI that actually reaches production — governed, measured, and owned by your team.
07 / Frequently asked
Generative AI,
answered.
The questions enterprise teams ask before they trust a generative AI system in production.
Generative AI development services design, build, and deploy systems powered by large language models — including RAG applications, AI agents, fine-tuned models, and ChatGPT or OpenAI integrations. We deliver these as production systems with evaluation, guardrails, and monitoring, not one-off demos.
RAG (retrieval-augmented generation) grounds a language model in your own documents and data so answers are accurate and traceable. We build the retrieval pipeline, embeddings, and evaluation needed to keep responses reliable as your content changes.
Yes. We fine-tune and adapt open and commercial models on your data when it improves accuracy, cost, or latency, and we make the trade-offs explicit so you choose between prompting, RAG, and fine-tuning with clear evidence.
Every build ships with an evaluation suite, guardrails, and production monitoring. As a Responsible AI lab, we treat governance and human oversight as part of the system, so you can measure quality and catch drift before users do.
Yes. We build secure ChatGPT and OpenAI API integrations into existing products and internal tools, including authentication, rate limiting, data handling, and the evaluation needed to ship with confidence.
08 / Scope your build
Have a generative AI use case
worth shipping?
Tell us what you're trying to build. We'll scope it, name the trade-offs, and give you a costed path to production.
Guided by human-led AI · Stanford-rooted · Founded 2011