Proving It: Evaluating Enterprise AI Without a Trusted Answer Key
What happens when a customer asks us to prove our agent is accurate, sometimes with no answer key at all, and sometimes with one that turns out to be wrong.
Institutional Knowledge: Grounding an Answer Without Citing It
Some of the best answers draw on knowledge you don't want shown as an official source. Here's the feature we built for that, and the bug it exposed.
Your LLM Judge Is Just One More Model to Test
How we tune LLM judges and the rest of the eval stack so the scores are something we can actually trust.
Loop Engineering, Applied: Syncing a Moving API into an MCP Server
Loop engineering means designing the system that prompts your agent, not the prompt. Here's what that looks like on a real production problem: keeping our MCP server synced with a moving API.
A Practical Framework for Evaluating AI Agents
Evaluating AI agents is hard to get right. This is the framework we use at Rapidflare to test our agents, catch regressions, and ship with confidence.
Evals First, Agents Second: A Field Guide for Enterprises
Why evaluation comes before the agent: how we measure what "good" looks like when it comes to AI for product intelligence, and how we meet your organization wherever you are in the transformation journey.
Four Generations of the Rapidflare Agent Harness - Spark, Flame, Blaze and Forge
How the Rapidflare agent harness has evolved across four generations — from a simple RAG pipeline in 2023 to a long-running, broader, deeper harness in 2026.
The Rapidflare Fire Shield, Part II: Beyond the LLM
Part II of the Rapidflare Fire Shield series — WAF, reCAPTCHA, edge controls, human review, monitoring, and external pen-test layers around our AI pipeline.
The Rapidflare Fire Shield, Part I: The AI Safety Filter
Part I of the Rapidflare Fire Shield series — the multi-layer AI safety filter that classifies every inbound message before retrieval and answer generation.
Agents Can Reason. They Still Can't Really Search.
Agents have a search problem across the whole stack: web search, RAG, tool discovery, skills/workflow loading, and even context compaction.
How We Built LLM-Powered Autocomplete for AI Agents
How Rapidflare engineered LLM-powered autocomplete for AI agents, delivering suggestions in under 300ms with three-layer personalization.
Introducing the Electronics Industry’s First AI Agent with Visual Reasoning
Rapidflare introduces the electronics industry’s first visual-reasoning AI agent, turning schematics, diagrams, and technical visuals into searchable knowledge with diagram-grounded answers for engineering, support, sales, and marketing teams.
Introducing Inline Citations: Traceable AI for Technical Industries
Rapidflare Agents now support inline citations, enabling traceable, verifiable AI answers grounded in datasheets, manuals, and standards documents. Built for technical industries where accuracy and compliance matter.
Rapidflare Launches Native Discord Integration for Scalable Developer Support
Rapidflare’s native Discord integration helps DevRel teams support large developer communities with accurate, AI-powered responses.
Accuracy: The Key to Effective AI-Powered Technical Sales
In technical sales, precision and clarity form the basis of trust. Realizing those values in AI agents means building on structured expertise, explainable reasoning, and continuous validation, ensuring accuracy and reliability where every detail matters.
Task-Specific AI vs Generic LLMs: Why Precision and Reliability Matter
Discover why task-specific AI outperforms generic LLMs in precision, reliability, and context for mission-critical industries.