I spent a large part of my career at Netflix working on its mission-critical API services. At its peak, the Netflix API was the single entry point for all 200+ million customer devices worldwide. Expectations for availability and resiliency were among the highest anywhere.

Over time, I learned:

• 99.9% uptime is baseline, it’s simply expected.

• 99.99% is hard, every “9” adds significant complexity.

• 99.999% or higher borders on impossible, requiring entirely new architectures, processes, and operating discipline. This could also be prohibitively expensive. 

5 “9”s translates to only 5 minutes of downtime in a given year! Each extra “9” demands non-linearly increasing levels of investment — new fault-tolerant designs, deeper observability, better cross-team communication, relentless testing, and operational rigor. Conversely, losing a “9” was instantly felt. Even a few minutes of downtime could hit headlines. 

That culture of extreme reliability shaped our thinking when starting Rapidflare. We made a foundational bet: for AI agents, accuracy and reliability will be just as critical, and achieving high 9's of accuracy is how we'll differentiate as an AI first company.

Why Accuracy Matters More in Electronics Sales

Electronics is one of the most information-dense, detail-sensitive industries on the planet. Sales engineers face constant pressure to:

• Compare components with nearly identical specifications, where one wrong detail can mean the difference between passing and failing a customer’s qualification process

• Verify compliance with regional regulations, such as RoHS, REACH, or ITAR, where even a small oversight can jeopardize global deals.

• Answer complex BOM (Bill of Materials) queries on the fly, where customers expect precise cross-references and compatibility checks instantly.

In such a technical sales environment, especially in industries like electronics, trust is everything. AI demos can look impressive, but if a sales engineer can’t rely on an AI agent to be accurate in front of a million-dollar client, the deal is at risk. A misstatement in this context isn’t trivial, it can derail trust, delay deals, or even disqualify a vendor.

That’s why accuracy in AI agents isn’t a “nice-to-have.” It’s existential.

Why Accuracy Is Hard in AI

Generative AI excels at writing and summarizing natural language, emails, blogs, and reports. But it often struggles with the structured, high-precision world of technical documents.

Datasheets and engineering specs aren’t written for machine readability. They’re visually structured for humans, but unstructured as far as a machine goes, or even worse, have implicit relationships between entities on a page. 

That’s why models often misread context or misinterpret parameter relationships. Predicting what sounds right isn’t the same as knowing what is right — a swapped voltage range or mistyped tolerance can be catastrophic. Visuals like circuit diagrams or timing charts make the challenge even harder, since most models still can’t fully connect visual data with textual reasoning. Conventional extraction techniques must be augmented by domain and customer specific content processing and structuring capabilities.

Bigger Models Aren’t the Answer

Accuracy will not come “for free” with bigger and bigger models.

• A larger model won’t automatically understand the difference between two connectors with similar form factors but different pinouts.

• A larger model won’t know the regulatory nuance that applies differently in Germany versus the U.S.

• And a larger model won’t magically deliver the reliability a sales engineer needs when a customer is pressing for specifics.

What works instead are task-specific agents, designed with domain accuracy in mind. They don’t try to be everything to everyone. They focus relentlessly on being right in the narrow, high-stakes contexts where it matters most. Bigger models help us - like a rising tide that lifts all boats, their greater understanding of features from all domains, and improved reasoning ability in turn improve the quality of Rapidflare’s agents higher. 

How Rapidflare Engineers for Accuracy

At Rapidflare, we’ve carried forward the lessons of Netflix reliability culture into the design of our sales-focused AI agents. Accuracy isn’t a byproduct, it’s the goal. Here’s how we get there:

1. Structuring Domain Knowledge for Precision
   We don’t just dump documents into a vector store. We model electronics knowledge, product catalogs, component specs, compliance standards, in ways that align with how sales teams actually reason. For example, when a customer asks whether a capacitor series meets RoHS requirements, the agent doesn’t “guess.” It retrieves structured compliance metadata mapped directly to the component’s SKU.

2. Blending Agentic Reasoning with Explainable Workflows
   Purely generative agents can and will hallucinate. Deterministic systems are also rigid and can’t handle varying or unpredictable requirements well. Thus, we combine the two: agentic reasoning for flexibility, paired with deterministic rules that enforce correctness on sensitive data. Take a scenario where a customer asks for a BOM substitution. The agent uses reasoning to understand use cases and identify potential alternatives, but a deterministic ruleset enforces compatibility thresholds so the suggestions are always valid.

3. Continuous Monitoring, Tuning, and Guardrailing
   Reliability doesn’t stop at deployment. We instrument agents with telemetry to monitor output quality and drift, operational factors like cost and latency. We feed results back into our product development and AI tuning loops. Guardrails catch anomalies and alert us well before before the customer becomes aware. We strive to quickly act on the issue and have a solution ready by the time we engage our customers on it.

This layered approach converts accuracy from just an aspiration into a bankable, repeatable engineering practice.

The Path Forward

Our constant drive and mission is to go beyond being just "close enough" and instead become “best in class”. We seek Rapidflare to be a sales assistant that you can confidently bet your business growth. When the million-dollar question comes, we want our answers to be always accurate, high quality, and trustworthy. With those rubrics as our core foundation, our AI powered workflows seek to create natural, effective and highly impactful outcomes for electronics.

There's a lot more to share about our technical architecture and approach to achieving these challenging goals. Stay tuned as we'll be posting a series of articles in this blog on these topics!