RAG or fine-tuning — what should I build first?

Almost always RAG first. Fine-tuning is expensive, slow to iterate, and gets stale. RAG gives you fresh data, citations and lower cost — and works on top of any frontier model. We only recommend fine-tuning when you have a specific style, domain language, or latency need that RAG can't solve.

Which models do you use?

Claude 4.x (Opus / Sonnet / Haiku) and GPT-class models for frontier reasoning. Open-source Llama / Mistral / Qwen for self-hosted, cost-sensitive workloads.

How do you control AI costs?

Prompt caching, model cascading (cheap model first, expensive model only when needed), strict output schemas, and a live cost dashboard you can watch every day.

AI products that ship.

AI & Machine Learning.

RAG applications, agent workflows, custom chatbots, fine-tuned models and computer-vision systems built on the latest foundation models — engineered for production, not demos.

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What is ai & machine learning?

Ship an AI product real users can rely on — with eval, guardrails and cost control built in.

Most agencies ship slick AI demos that fall over in production. At Neticx, every model goes live with eval pipelines, guardrails and a live cost dashboard — engineered for real users, not pitch decks.

Who it's for

Built for these teams.

If one of these sounds like you, AI & Machine Learning is the right starting point.

SaaS teams adding AI features to existing products

Enterprises automating support, ops or sales workflows

Founders building AI-native products from scratch

What's included

Everything you need to ship.

Each engagement is fixed-scope. Below is exactly what we deliver.

RAG applications (Pinecone / Weaviate / pgvector)

Agent workflows (LangGraph, CrewAI, custom)

Custom chatbots with persistent memory and tools

Fine-tuning on open-source models (Llama, Mistral, Qwen)

Computer vision (object detection, OCR, ID verification)

Eval pipelines, guardrails, cost monitoring

How we deliver

From scope to production AI.

From scope to production — eval, guardrails, and live cost monitoring.

01
Scope & data audit
2–5 days. Use case defined, model selected, data quality checked. Output: architecture doc and fixed quote.
02
Prototype
RAG pipeline or agent workflow built in staging. You test accuracy before we move to production.
03
Eval & guardrails
Accuracy benchmarked, cost per query optimized. Guardrails added before go-live.
04
Production & monitor
Deployed to production with live cost and latency monitoring dashboard. 30-day hyper-care included.

FAQ

Buyer questions, answered.

If yours isn't here, WhatsApp us — we usually reply within an hour.

Ready to start

Talk to an AI engineer for ai & machine learning.

Tell us about your project. We'll come back with a fixed-scope quote within 24 working hours.

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