Applied AI
AI Engineering
Building AI features that survive production — evaluation harnesses, retrieval that actually retrieves, and cost per task you can defend.
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Most AI features fail after launch, not before it. The model was never the hard part: the hard part is knowing whether last week's prompt change made the product better or quietly worse for a segment nobody was watching. Everything in this topic starts from that problem.
We write about the parts of applied AI that decide whether a feature survives its first three months in front of real users. Evaluation harnesses and golden datasets. Retrieval that grounds answers in documents that actually exist. Guardrails, refusals and graceful handoff to a human. Token budgets and cost per completed task, tracked like any other unit economic.
These are field notes from shipping AI automation, generative AI features and conversational systems for clients who have to justify the spend. Where something worked, we say what it cost. Where it did not, we say why.
- AI Engineering
RAG that actually retrieves: fix the retrieval layer first
Most retrieval-augmented generation problems are retrieval problems wearing a generation costume. Fix chunking, hybrid search and reranking before you touch the prompt.
RMRavi Menon2 min read - AI Engineering
Evaluating LLM features before you ship them
Most AI features fail in production because nobody built a way to tell whether a prompt change made things better or worse. Here is the evaluation harness we build first.
RMRavi Menon2 min read - AI Engineering
What an AI agent actually costs per completed task
Token pricing is not the interesting number. Cost per successfully completed task, including retries and human escalation, is the one that decides whether an agent ships.
RMRavi Menon2 min read - AI Engineering
Chatbot guardrails that hold up in front of customers
Refusals, scope limits and escalation are product decisions, not prompt lines. Here is the guardrail stack we ship on customer-facing assistants.
RMRavi Menon1 min read
If you are scoping an AI project and want the same rigour applied to yours, our AI Automation, Generative AI and Chatbot engagements all start with an evaluation plan before a line of feature code is written.
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