Guides
Practical, engineer-written answers to the questions teams actually ask about production AI — reducing LLM costs, the Model Context Protocol, building evals, and shipping with agents. No fluff, no vendor spin.
LLM cost
How to reduce LLM API costs
Most production LLM spend is avoidable: it comes from sending the wrong model too many tokens, too many times. Cutting it is an engineering problem — routing, caching, batching, and context control — not a matter of waiting for prices to drop.
Read the guide →Why are LLMs so expensive?
LLMs are expensive because you pay per token on every call, output usually costs more than input, and agents re-read a growing context on every step — so cost compounds far faster than the work appears to grow.
Read the guide →How to reduce LLM latency
LLM latency is two separate problems — the wait for the first token and the time to stream the rest — and each has its own set of fixes.
Read the guide →Self-hosted LLMs: when it pays off, and how
Self-hosting an LLM means running open-weight models on GPUs you own or rent instead of calling a hosted API — it wins on high, steady volume or hard compliance, and loses almost everywhere else.
Read the guide →MCP
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in late 2024, that defines one uniform way for an LLM application to connect to external tools and data through a client-server protocol built on JSON-RPC.
Read the guide →How to build an MCP server
An MCP server is a small program that exposes your tools, data, and prompts to an AI client over a standard protocol — the fastest way to give Claude or Cursor safe, structured access to your systems.
Read the guide →LLM evals
How to build LLM evals
Evals turn 'the model feels better' into a number you can gate a deploy on — they are the test suite that makes every prompt, model, and pipeline change safe to ship.
Read the guide →What is RAG (retrieval-augmented generation)?
RAG retrieves the passages relevant to a question and puts them in the prompt, so the model answers from your data with citations instead of relying on what it happened to memorise during training.
Read the guide →Agentic coding
What is agentic coding?
Agentic coding is letting an AI coding agent plan, edit across files, run tools and tests, and iterate in a loop toward a goal — powerful for the first 80% of a change, and quietly expensive on the last 20% if you skip standards and review.
Read the guide →Is your codebase ready for AI agents?
Agents don't fail randomly on a repo — they fail predictably on the same things that slow down human engineers: fuzzy boundaries, missing tests, undocumented conventions. Readiness is just those weaknesses paid down deliberately.
Read the guide →Hiring & partners
How to hire an LLM cost optimization consultant
Hire an LLM cost consultant when your bill is large enough to matter but your team is too busy to instrument, route, and eval their way out of it themselves — and only hire one who leaves you owning the result.
Read the guide →How to choose an AI engineering consultancy
Three very different businesses all call themselves an 'AI consultancy'; only one of them writes production code you own, and telling them apart is most of the buying decision.
Read the guide →How to choose an MCP server development company
The MCP SDK is a weekend's work; the job you're actually hiring for is least-privilege auth, scoping, audit logging, and production hardening on servers that reach straight into your internal systems.
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