Generative AI Development

Ship Production-Grade Generative AI — In Weeks, Not Quarters

We build LLM apps, RAG pipelines, AI agents, and fine-tuned models on OpenAI, Anthropic, and AWS Bedrock. 30+ GenAI apps shipped — production-ready from day one, with guardrails, evals, and cost controls built in.

Start by comparing AI platforms for generative AI workloads — then we'll match the right model and architecture to your use case.

Get My Free AI Consultation See What We Build

Free 30-min AI strategy call48-hour proposalNDA-first, data stays yours

30+

GenAI Apps Shipped

15+

Models Deployed

50+

Enterprise Integrations

98%

Client Satisfaction

What We Build

Our Generative AI Development Services

End-to-end GenAI engineering — from strategy and model selection to RAG pipelines, fine-tuning, and scalable deployment.

AI Opportunity Map · Discovery

Generative AI Consulting

Feeling overwhelmed by GenAI? Our team runs a strategic assessment — mapping your workflows, data, and revenue drivers to the places where GenAI delivers real, measurable impact. You walk away with a prioritized roadmap, not a buzzword list.

Use-case discovery & ROI scoring
Model, cost, and data-privacy strategy
Prioritized 90-day GenAI roadmap

rag_pipeline.py · retriever.ts

Generative AI Model Development

Don't settle for generic chatbots. Our engineers and data scientists build custom LLM applications, RAG pipelines, and multi-agent systems tuned to your domain data, latency budget, and accuracy bar.

RAG with vector DBs (Pinecone, pgvector, Weaviate)
Multi-agent orchestration (LangGraph, CrewAI)
Guardrails, evals, and hallucination controls

Generative AI Integration

Get the most from your AI investment with clean, secure integration. We handle the plumbing — auth, streaming, rate limits, observability — so your custom model slots into existing workflows, CRMs, and data stacks without breaking a thing.

Secure API gateways & streaming responses
CRM, ERP, Slack, and data-warehouse connectors
Token-cost observability & rate limiting

Model Monitor · Prod · LLM-v2.3

96.4%

Accuracy

842ms

P95 Latency

$0.41

Avg Cost

Upgrade & Maintenance

A GenAI model isn't a deploy-and-forget project. We monitor accuracy, latency, and cost in production — catching drift, regressions, and prompt-injection attempts before your users do.

Accuracy, latency, and cost dashboards
Drift detection and regression alerts
Prompt-injection and jailbreak monitoring

Fine-Tune Job · Llama-3-8B · Epoch 4/5

+18%

Accuracy

0.09

Loss

4/5

Epochs

AI Model Fine-Tuning

As your data and business evolve, your model should too. We fine-tune foundation models (Llama, Mistral, GPT, Claude) on your proprietary data — raising accuracy, cutting token cost, and matching your brand voice.

Supervised & instruction fine-tuning
LoRA / QLoRA for efficient training
Eval harness & A/B comparison vs. base model

Generative AI Model Replication

Need your model running in multiple regions, environments, or white-label apps? We replicate and deploy your custom GenAI solution across cloud regions, edge, and on-prem — with consistent behavior and central governance.

Multi-region & multi-tenant deployment
On-prem & private-cloud rollout
Central prompt & policy governance

Why Brilworks

Here's What Sets Us Apart

We don't run three-month PoCs that die on a whiteboard. We ship GenAI apps real users use — with guardrails, evals, and cost controls from day one.

Beyond Automation, Embrace Innovation

We don't just automate tasks — we empower your AI to generate entirely new content, concepts, and ideas that differentiate your product.

Scalable AI Solutions

Your AI shouldn't be left behind as you grow. Our architectures scale across regions, tenants, and usage spikes — without ballooning token bills.

We Speak Your Language, and AI's

Our AI experts bridge the gap between your domain knowledge and the complex world of foundation models. You stay in charge of what; we handle the how.

AI for Everyone

We're not just developers — we're your partners. Clear communication, async-first workflow, and no jargon walls between your team and ours.

Agility at the Core

We track the weekly pace of model releases, agent frameworks, and inference tech — so your project ships on current stacks, not last year's best practices.

Responsible & Secure AI

NDAs first. Your data stays in your tenancy — VPC, Bedrock, private endpoints. PII redaction, audit logs, and bias checks built in.

Client Stories

What Founders & AI Leaders Say About Us

Real outcomes from teams that trusted Brilworks to take their GenAI work from idea to production.

“Brilworks built our RAG-powered underwriting assistant on Bedrock in 9 weeks. It cut analyst review time by 60% and passed our compliance audit on the first pass. They understand finance, not just LLMs.”

James KimHead of AI, FinTech Lender

“We needed a fine-tuned clinical-summary model running inside our HIPAA-compliant VPC. Brilworks delivered — PHI never left our tenancy, and accuracy jumped 14 points over the base model.”

Dr. Anna ReyesCTO, HealthTech Platform

“Their GenAI team built our product-description generator and review summarizer. Conversions on AI-written listings are up 22%, and we cut content ops cost by two-thirds. They delivered a real business outcome, not a demo.”

Laura ChenVP Product, E-commerce Marketplace

Industries We Serve

Deep Domain Expertise Across Verticals

Not generalists. We have shipped GenAI solutions with case studies, clients, and production models in each of these verticals.

Common Questions

Frequently Asked Questions

Everything AI leaders and founders typically ask before partnering with us on a GenAI project.

It depends on complexity, team size, and ongoing maintenance. A basic LLM app typically runs $50K-$150K; feature-rich solutions with agents, RAG, and fine-tuning can reach $400K or more. We offer free consultations to scope your specific need and share a tailored quote.

Focused LLM apps with RAG can ship in 6-10 weeks. Fine-tuning and multi-agent systems typically take 3-6 months. End-to-end enterprise rollouts including integration and governance run 6-12 months. We share a detailed timeline within 48 hours of your first call.

All of the above. We match the model to the job — GPT-4 class for reasoning, Claude for long context, Llama / Mistral for on-prem or cost-sensitive use cases, Bedrock for AWS-native compliance. We also benchmark multiple options against your actual data before committing.

Your data stays in your tenancy. We deploy via private endpoints, VPC, or AWS Bedrock with no-training guarantees. We sign NDAs before any discussion and support HIPAA, SOC 2, ISO 27001, and GDPR workflows with audit logging and PII redaction.

RAG is best when your knowledge changes often (docs, policies, tickets). Fine-tuning is best for style, format, tone, or specialized reasoning patterns. Most production systems use both. We benchmark both on a sample of your data before recommending.

Layered defense: retrieval with citations, structured output parsing, eval harnesses on every deploy, guardrails (topic / PII / jailbreak filters), and production monitoring. We build evaluation in from day one — not as an afterthought.

You do — 100%. At handover you get full repo access, prompts, eval suites, fine-tuned model weights, documentation, and deployment credentials. No vendor lock-in.