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.
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.
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.
Building a Scalable Ingestion Pipeline with Temporal (Part 2)
Heartbeats, cancellation, approval gates, scheduling, observability, and the operational patterns that made our Temporal ingestion pipeline easier to run.
Building a Scalable Ingestion Pipeline with Temporal (Part 1)
How we built a document ingestion system that handles massive sources using Temporal's workflow orchestration, and the design decisions that made it scale.
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.
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.
Building Scalable Technical Support for Engineering Communities in Electronics Manufacturing
Learn how Rapidflare built a scalable, secure Discord integration using webhooks, strong access controls, and native UX for large developer communities.
A Practical Guide to Recall, Precision, and NDCG
Step-by-step guide to optimizing RAG retrieval - improve recall, precision, and ranking for LLMs.