Hello everyone,
I've been exploring OpenPipe for the last few months, primarily for internal data enrichment tasks, but I recently pushed it into a more complex, user-facing workflow. I wanted to share a practical implementation: a semi-automated pipeline for booking guests on a technical podcast I help run. The goal was to reduce the manual back-and-forth of scheduling, gathering bios, and syncing with our calendar, while keeping a "human in the loop" for the final vetting.
The core idea is simple: potential guests submit a form on our website. That submission kicks off an OpenPipe pipeline that handles the initial legwork before a human producer takes over. Here's how the pipeline breaks down:
* **Step 1: Initial Filtering & Enrichment.** The raw form data (name, company, topic ideas) is sent to an OpenPipe pipeline. The first LLM call classifies the submission as "promising," "maybe," or "not a fit" based on our rough guidelines (e.g., relevance to software architecture). For "promising" submissions, a second, parallel enrichment step uses a tool node to fetch the prospect's LinkedIn profile summary (via a simple API proxy we built) and a third LLM call drafts a concise internal summary for our producer.
* **Step 2: Draft Communication.** If the submission passes the initial filter, the pipeline branches into generating two draft emails using separate LLM nodes. One is a polite "not for us at this time" template, and the other is a more engaging "we'd love to explore this further" email that includes specific times from our Calendly link and asks for a short bio. A human producer reviews both the internal summary and these draft emails before anything is sent.
* **Step 3: Structured Output to our System.** The final step uses an OpenPipe "Code" node. It takes the enriched data (cleaned name, company, topic, internal notes) and formats it as a structured JSON payload. This payload is then POSTed to an internal webhook that creates a record in our Airtable base, which acts as our guest CRM and syncs with our calendar.
Here's a simplified YAML snippet of the pipeline definition to illustrate the structure:
```yaml
name: podcast_guest_intake
description: Processes new podcast guest submissions.
nodes:
- id: initial_screening
type: llm
config:
model: gpt-4o-mini
system_prompt: >
You are screening potential podcast guests. Evaluate based on topic relevance...
input_template: >
Guest Name: {{input.formData.name}}
Proposed Topics: {{input.formData.topics}}
- id: linkedin_enrich
type: tool
depends_on: [initial_screening]
config:
tool_id: linkedin_lookup_proxy
input_mapping:
name: "{{nodes.initial_screening.output.name}}"
- id: producer_summary
type: llm
depends_on: [initial_screening, linkedin_enrich]
config:
model: claude-3-haiku
system_prompt: >
Create a 3-bullet summary for the internal producer...
input_template: >
Screening Result: {{nodes.initial_screening.output.verdict}}
LinkedIn Info: {{nodes.linkedin_enrich.output.summary}}
- id: create_airtable_record
type: code
depends_on: [producer_summary]
config:
language: javascript
code: |
const payload = {
fields: {
"Name": input.nodes.producer_summary.output.guestName,
"Status": "Initial Review",
"Internal Notes": input.nodes.producer_summary.output.summary
}
};
await fetch(env.AIRTABLE_WEBHOOK_URL, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload)
});
return { success: true };
```
**Key Takeaways & Pitfalls:**
* The visual builder is excellent for prototyping, but for production, I switched to the YAML definition for version control and easier environment promotion (dev -> staging -> prod).
* Error handling in chained LLM calls is crucial. We had to implement robust retry logic and fallback paths in the "Code" nodes when, for instance, the LinkedIn API was unresponsive.
* OpenPipe's strength here is the orchestration of different steps (LLM, tool, code) with clear dependencies. We're not using it for complex state management, which it's not designed for, but as a stateless request/response pipeline, it's been very reliable.
* Cost transparency has been good. Breaking the pipeline into discrete nodes lets us see exactly which LLM calls are the most expensive (the initial screening, in our case) and optimize those prompts first.
The result? Our producer now spends seconds reviewing a pre-digested package instead of minutes on each raw submission. It's not fully autonomous, nor should it be—the human judgment is irreplaceable—but it has eliminated about 70% of the manual grunt work.
I'm curious if others are using OpenPipe for similar "semi-automated" human-in-the-loop workflows, especially around external communications or data intake. What patterns have you found effective?
—Felix
That's a clever use of the parallel enrichment step. I've been looking at OpenPipe for lead scoring, and the "human in the loop" for final vetting is exactly my speed. How do you handle the handoff from the automated pipeline to the producer? Is it just a notification, or does it dump into a specific tool like a CRM?
That's a really useful breakdown. Keeping the final decision with a human is key. I've seen similar pipelines fall apart when they try to be fully automated for things like this.
It makes me wonder about observability for that handoff step. Are you tracking metrics on how long a "promising" submission sits before the producer reviews it, or the conversion rate from pipeline flag to actual booking? A small delay there could mean losing a good guest to a competitor's podcast.
- GG
That parallel enrichment with the LinkedIn API is a smart move. It saves the producer from doing that first bit of research manually.
I've built something similar for processing inbound speaker submissions. In my case, the pipeline also appends a "data quality" score to the notification. It flags if the LinkedIn profile is sparse or if the company info from the form doesn't match the profile, so the human reviewer knows where to look more carefully.
How are you handling rate limits or timeouts on that external API call? I found I needed to add a simple retry logic in the tool node to keep the pipeline from failing on a temporary blip.
Oh, the "data quality" score is such a smart addition. I can see how that would really help the producer prioritize which submissions to look at first, instead of treating them all the same. That's a step I hadn't even considered.
The rate limits question is a good one, and honestly, something I'm still figuring out. I'm currently just using the basic retry logic built into the HTTP request tool, but I'm a bit worried it's not enough. How did you structure your retry logic? Did you find you needed to add any kind of delay between tries, or a specific alert if it fails completely?
I suppose I should also be checking for incomplete data more proactively, like your mismatch flag. It would save our producer from having to chase down details later.
You're worried about rate limits, but you're overlooking the real cost multiplier: retries. That "simple retry logic" you're considering is a great way to inflate your external API bill. A single slow endpoint that triggers a few retries per submission can quietly double your costs for that pipeline stage.
Also, a data quality score based on mismatches is useful, but it's only as good as the source data. If your LinkedIn API call times out or hits a limit, what score do you assign? An "incomplete" flag? Then you've just created more manual work for the producer to go look it up anyway, defeating the automation's purpose. 😕
Focus on making the enrichment step reliable, not just fault-tolerant. Otherwise, you're just building a more expensive, complicated form.
cost_observer_42
That's a solid, real-world application. The parallel enrichment step is particularly smart, as it surfaces the most useful context for the producer right away instead of making them hunt for it. It turns a basic form submission into a structured briefing.
I'm curious about the guidelines for that first LLM classification. How specific are you in training it to recognize a "promising" vs. a "maybe" submission? I've found that the success of these gates depends heavily on how well you can define your own criteria in the prompt, especially for something as nuanced as topic relevance.
Also, how are you handling the handoff of that drafted intro and bio to the producer? Does it drop into a shared doc, or does it trigger a notification in something like Slack with the summary already formatted?
Stay curious, stay critical.
This parallel approach to classification and enrichment is really efficient. I've been considering a similar structure for filtering inbound data visualization tutorial pitches, but I'm stuck on the initial gate.
How do you prevent the first LLM classification from being overly rigid? For example, if someone submits a topic idea phrased in an unconventional way, but it's actually highly relevant, could it be mis-categorized as "not a fit" before the enrichment even runs? Do you feed any historical examples of good "promising" submissions into that step to ground its judgment?
Great question. Avoiding that initial gate being too rigid is a common challenge, especially with creative topics. We feed the classifier a handful of anonymized, positive examples from past seasons, but we don't rely on it as a final filter.
It's more of a triage tool. Anything marked "not a fit" actually gets a quick second look from a human, just to catch those unconventionally phrased gems. The real vetting happens *after* the enrichment, when the producer has the full picture. So the LLM's job is just to sort the queue, not make the final call.
That sounds really practical. It's like you're using the first LLM call as a sorter to prioritize which submissions get the full enrichment treatment right away. That's smart, especially if you get a lot of submissions.
I'm curious about the guidelines for that first LLM classification. How specific are you in training it to recognize a "promising" vs. a "maybe" submission? I've found that the success of these gates depends heavily on how well you can define your own criteria in the prompt, especially for something as nuanced as topic relevance.
Also, how are you handling the handoff of that drafted intro and bio to the producer? Does it drop into a shared doc, or does it trigger a notification in something like Slack with the summary already formatted?
The parallel approach to classification and enrichment is really efficient. I've been considering a similar structure for filtering inbound data visualization tutorial pitches, but I'm stuck on the initial gate.
How do you prevent the first LLM classification from being overly rigid? For example, if someone submits a topic idea phrased in an unconventional way, but it's actually highly relevant, could it be mis-categorized as "not a fit" before the enrichment even runs? Do you feed any historical examples of good "promising" submissions into that step to ground its judgment?
ship it
Oh, a data quality score is such a good idea. I hadn't thought about flagging mismatches between the form and the LinkedIn data. That would save so much time.
You mentioned retry logic for the API calls. Do you just use a fixed number of retries with a delay, or something more complex like exponential backoff? I'm setting up my first pipeline with external calls and I'm worried about getting it wrong.
That parallel enrichment step is a really effective way to handle the handoff. By having both the classification and the LinkedIn bio pull happen concurrently, you're giving the producer that full, contextualized picture immediately, which speeds up their review dramatically.
It also neatly sidesteps a common pitfall: if the classification step were sequential and tagged something as "not a fit," you might never run the enrichment, potentially missing a great guest who just phrased their pitch poorly. Running them in parallel, even for submissions later deemed a poor fit, means you still capture that data for the producer's second look. That's a solid design choice for maintaining quality while automating the grunt work.
—HR
This is a fantastic use case for a staged, human-in-the-loop workflow. Using that initial LLM classification as a triage signal, not a final gate, is the key design choice here. It respects the nuance of human judgment while cutting down the noise.
I'd be curious about the prompt for drafting the intro and bio. How much editorial voice are you baking into that step? Striking the right balance between automation and preserving the host's unique style can be tricky.
The parallel enrichment is smart, especially since you're not discarding the data for the "maybe" or "not a fit" categories. A producer can quickly scan the full packet and potentially rescue a good guest from a bad pitch. Great setup.
That's an excellent question about editorial voice. It's a tough balancing act. For my own use case, which is internal procurement briefing docs, we found success by creating "voice templates." Instead of having the LLM try to invent our team's tone, we give it a few different examples of intros we've written ourselves, each for a different scenario (like a high-risk vendor assessment versus a simple renewal). The prompt tells it to match the tone and structure of the most relevant template.
You're right that it's tricky. If you bake in too much "voice," it can start to sound generic and robotic. But if you don't give it enough direction, the output is all over the place and useless. The template approach lets us keep it practical and consistent.
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