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, and open-source models — from agentic systems that complete real work end to end, to fine-tuned models that beat generic APIs on your domain — 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 LLMOps work that keeps AI features accurate and affordable when they go viral.
What You Ship With NerdDevs
- AI copilots and chatbots wired into your existing product
- Agentic AI — single and multi-agent systems that use your tools and complete work end to end
- Retrieval-augmented generation (RAG) over your private documents and data
- Fine-tuned open models (LoRA/QLoRA) when prompting alone cannot hit accuracy or cost targets
- Voice agents for support and sales — speech recognition, natural TTS, and telephony wired in
- Evaluation harnesses, guardrails, and LLMOps so outputs stay reliable in production
What We Build
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.
Agentic AI & Multi-Agent Systems
Single and multi-agent systems that reason, call your tools and APIs, and loop in humans for approval — automating real work end to end, not just chat.
RAG & Semantic Search
Vector pipelines over your knowledge base so users get answers grounded in your real content, not hallucinations.
LLM Fine-Tuning & Custom Models
LoRA/QLoRA fine-tunes of Llama, Mistral, and other open models on your domain data — evaluation-driven, raising accuracy and cutting per-request cost versus prompting alone.
Voice & Conversational AI
Voice agents with speech recognition, natural text-to-speech, and telephony integration — built on the same communication backbone that moves millions of texts a month for our clients.
Vision & Document AI
OCR, layout parsing, image understanding, and GPT-4 Vision pipelines for invoices, IDs, screenshots, and creative assets.
AI Operations (LLMOps)
Model serving, caching, streaming, versioned prompts, eval gates in CI, cost dashboards, and monitoring — the production plumbing that keeps AI features fast and affordable.
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 / LangGraph
- LoRA / QLoRA
- Pinecone / pgvector
- Whisper / TTS
- Twilio / WebRTC
- 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.
Can you build AI agents that use our internal tools and APIs?
Yes — that is what agentic AI means in practice. We build single and multi-agent systems that reason over a task, call your internal tools and APIs, and pause for human approval on sensitive steps. Agents ship with the same eval harnesses and cost controls as the rest of our AI work, because an agent that acts needs tighter guardrails than a chatbot that talks.
Can you fine-tune an LLM on our data?
Yes. We run LoRA/QLoRA fine-tunes of Llama, Mistral, and other open models on your domain data, with a held-out evaluation set to prove the lift. Fine-tuning wins when you have high request volume, a narrow domain, or strict latency/cost targets — for nuanced low-volume tasks, a well-engineered prompt on GPT-4 or Claude is often the better buy, and we will tell you which.
Do you build voice AI agents?
Yes — speech recognition, natural text-to-speech, turn-taking, and telephony integration for support and sales use cases. Voice sits naturally on our communication heritage: our team builds and operates telephony and messaging backends that move millions of texts a month, so the real-time infrastructure behind a voice agent is home ground.
What does AI operations (LLMOps) cover?
Everything that keeps an AI feature trustworthy after launch: versioned prompts and models, evaluation pipelines that gate releases, latency and cost dashboards per feature, drift monitoring, and incident runbooks. Shipping the agent is the start — keeping it accurate, fast, and affordable at scale is the ongoing job.
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.