AI Development That Ships
From Prototype to 100K Users
Production-grade AI & LLM engineering for real products
Most AI projects die in the prototype stage. Ours go to production. We engineer AI applications on top of GPT-4, Claude, open-source models, and custom ML pipelines — then make them fast, reliable, and cost-efficient at scale.
We are the team behind Genius AI, an AI chat companion that hit 100K+ users in its first three weeks. We handle everything from model selection and prompt engineering to vector search, evaluation harnesses, and the unglamorous backend work that keeps AI features affordable when they go viral.
What You Ship With NerdDevs
- AI copilots and chatbots wired into your existing product
- Retrieval-augmented generation (RAG) over your private documents and data
- AI automation for support, sales, content moderation, and internal ops
- Vision & document processing pipelines (OCR, GPT-4 Vision, image generation)
- Evaluation harnesses and guardrails so outputs stay on-brand and on-topic
What We Build
Six focus areas where we bring deep, shipped-in-production experience.
AI Copilots & Assistants
In-product copilots that understand your domain, tools, and user context — deployed on GPT-4, Claude, or fine-tuned open models.
RAG & Semantic Search
Vector pipelines over your knowledge base so users get answers grounded in your real content, not hallucinations.
AI Automation Agents
Multi-step agents that orchestrate tools, APIs, and human approvals — replacing repetitive ops work at your scale.
Vision & Document AI
OCR, layout parsing, image understanding, and GPT-4 Vision pipelines for invoices, IDs, screenshots, and creative assets.
ML Infrastructure
Model serving, caching, streaming, rate limiting, observability, and cost controls so your AI feature does not sink the P&L.
Evaluation & Guardrails
Automated eval harnesses, red-team testing, and safety filters so you can ship changes confidently.
Our Stack
- OpenAI GPT-4
- Anthropic Claude
- Llama 3 / Mistral
- LangChain
- Pinecone / pgvector
- Python
- Node.js
- AWS
- Hugging Face
Who This Is For
- Enterprises rolling out internal AI copilots for sales, support, or engineering
- SaaS products adding AI features as a competitive moat
- Startups going from prototype to first 10K-100K users
- Teams needing reliable AI infrastructure that will not break at scale
Frequently Asked Questions
Can NerdDevs build AI features on top of our existing product?
Yes. Most of our AI work integrates into an existing web or mobile app — we connect to your database, auth, and billing, then ship AI features as new endpoints or UI flows without rewriting your core.
Which model should we use — GPT-4, Claude, or open-source?
It depends on the task, latency, and cost envelope. We benchmark 2–3 options in week one so the model choice is evidence-based, not fashion-driven. For high-volume internal tools we often land on fine-tuned Llama or Mistral; for nuanced user-facing copilots, GPT-4 or Claude.
How do you control AI costs in production?
Aggressive caching, prompt compression, routing cheap traffic to smaller models, streaming tokens to shorten perceived latency, and nightly cost reports per feature. On Genius AI we cut per-user token spend by ~40% in the first quarter post-launch.
Do you handle model evaluation and safety?
Every AI feature ships with an automated eval set of 50–200 labelled examples, a regression gate in CI, and content filters. We also run red-team sessions before launch for features that touch PII or safety-sensitive domains.
Ready to talk about your ai development project?
Send a short brief. We reply within one business day with next steps, a timeline, and the right engineer for the job.