Blog
From the engineering team.
Practical guides on building production AI — agents, RAG, LLM infrastructure and security — plus straight answers on what AI development costs and how to buy it.
How Much Does It Cost to Build an AI Agent in 2026? A Full Cost Breakdown
Quotes for the same AI agent can range from $10k to $300k. Here's what actually drives the price — build effort, integrations, model usage and ongoing operations — with realistic ranges for each tier.
AI Agents vs Chatbots vs RPA: Which Does Your Business Actually Need?
Chatbots answer questions. RPA follows scripts. AI agents decide what to do next. Here's how the three really differ, where each one breaks, and how to choose the right one for each workflow in your business.
RAG vs Fine-Tuning vs Long Context: How to Choose in 2026
Should you retrieve your data, train it into a model, or just paste it into a huge context window? Each works — for different problems. Here's how to choose, with a comparison table and the combinations that work best in production.
How to Choose an AI Development Company: 15 Questions to Ask Before You Sign
Every agency now says it builds AI. These 15 questions — on production experience, evaluation, security, ownership and pricing — reveal which ones can actually ship a system your business can depend on.
Model Context Protocol (MCP) Explained: Connecting AI Agents to Your Business Systems
MCP has become the standard way to connect AI models to tools and data. Here's a plain-English explanation of how it works, when it's the right choice for your business systems, and the security details most tutorials skip.
Intelligent Document Processing with LLMs: Beyond Traditional OCR
OCR turns pixels into text. Intelligent document processing turns documents into decisions. Here's how modern LLM-powered IDP pipelines work, how they handle messy real-world documents, and how to reach the accuracy your operations need.
Build vs Buy AI: When Custom AI Development Beats Off-the-Shelf Tools
Off-the-shelf AI tools are cheap to start and expensive to outgrow. Custom builds cost more upfront but give you control. Here's a framework for deciding which one your use case needs — and when a hybrid is smarter than either.
Buying AI5
How Much Does It Cost to Build an AI Agent in 2026? A Full Cost Breakdown
Quotes for the same AI agent can range from $10k to $300k. Here's what actually drives the price — build effort, integrations, model usage and ongoing operations — with realistic ranges for each tier.
How to Choose an AI Development Company: 15 Questions to Ask Before You Sign
Every agency now says it builds AI. These 15 questions — on production experience, evaluation, security, ownership and pricing — reveal which ones can actually ship a system your business can depend on.
Build vs Buy AI: When Custom AI Development Beats Off-the-Shelf Tools
Off-the-shelf AI tools are cheap to start and expensive to outgrow. Custom builds cost more upfront but give you control. Here's a framework for deciding which one your use case needs — and when a hybrid is smarter than either.
Why Most AI Pilots Never Reach Production (and the Checklist That Fixes It)
Most AI proofs of concept impress in a demo and then stall. The reasons are predictable — and fixable. Here are the seven failure patterns we see most often, and the checklist we use to get AI pilots into production.
AI MVP in 6 Weeks: What a Realistic AI Product Roadmap Looks Like
Six weeks is enough to ship a real AI product to real users — if you scope it ruthlessly. Here's the week-by-week roadmap we use, what goes in, what stays out, and how to know if your idea fits.
AI Agents5
AI Agents vs Chatbots vs RPA: Which Does Your Business Actually Need?
Chatbots answer questions. RPA follows scripts. AI agents decide what to do next. Here's how the three really differ, where each one breaks, and how to choose the right one for each workflow in your business.
Model Context Protocol (MCP) Explained: Connecting AI Agents to Your Business Systems
MCP has become the standard way to connect AI models to tools and data. Here's a plain-English explanation of how it works, when it's the right choice for your business systems, and the security details most tutorials skip.
Multi-Agent Systems in Production: Architecture Patterns That Hold Up
Multi-agent systems are powerful and easy to over-engineer. Here are the architecture patterns that work in production, the state and failure-handling details that make them reliable, and when a single agent is the better choice.
12 Business Workflows to Automate First with Agentic AI
Not every process is a good fit for an AI agent. These 12 workflows are — they're high-volume, judgment-heavy and measurable. For each one: what the agent does, which systems it touches and where a human stays in the loop.
AI Agent Guardrails: Human-in-the-Loop, Permissions and Failure Handling
An AI agent that can act can also act wrongly. Here's the practical guardrail stack we use — risk tiers, least-privilege tools, human approvals, validation, limits and safe failure — so agents can be trusted with real work.
RAG2
RAG vs Fine-Tuning vs Long Context: How to Choose in 2026
Should you retrieve your data, train it into a model, or just paste it into a huge context window? Each works — for different problems. Here's how to choose, with a comparison table and the combinations that work best in production.
Why Naive RAG Fails: Hybrid Search, Reranking and GraphRAG for Enterprise
Chunk, embed, retrieve, generate — and get mediocre answers. Here's why naive RAG breaks on real enterprise documents, and the techniques that fix it: structure-aware parsing, hybrid search, reranking, permission filters, GraphRAG and evaluation.
Document AI1
Voice AI1
Engineering3
How to Evaluate LLM Applications: Evals, Metrics and Tools That Work
You can't improve what you can't measure — and LLM apps are notoriously hard to measure. Here's a practical evaluation playbook: building test sets, choosing metrics, using LLM-as-judge safely, and wiring evals into every release.
Claude vs GPT vs Gemini vs Open-Source: Choosing an LLM for Your Product
Benchmarks change every month and rarely match your use case. Here's a practical way to compare Claude, GPT, Gemini and open-source models for your product — the criteria that matter, where each family tends to fit, and how to avoid lock-in.
Building Production-Grade AI: Why Engineering Depth Beats Prompt Cleverness
Every AI agency claims they can build anything. Most are stringing together API calls and calling it a system. Here's what production-grade AI actually requires — and why the engineering layer matters more than the model.
Infrastructure3
LLM Observability: How to Monitor AI Apps in Production
Traditional monitoring tells you if your server is up. It won't tell you that your AI started giving worse answers, or that one customer's prompts tripled your API bill. Here's what LLM observability covers and how to set it up.
How to Cut LLM API Costs: Caching, Model Routing and Batching
LLM bills grow quietly — longer prompts, more agent steps, bigger models than the task needs. These are the techniques that cut costs most in production, in the order we apply them, without hurting quality.
How We Handle 500+ Concurrent Users: A Deep Dive Into Our Base Infrastructure
Load testing results, architecture decisions, and the specific failure modes we engineered around. Everything we learned building the distributed foundation that all Keyved products run on.