Alright, fellow tinkerers, I've been deep in the weeds on this one and I have to say, I'm pretty pumped about the results. We've all seen those "AI agent automates your workflow!" demos, but I wanted to build something concrete that tackles a real, annoying problem: qualifying leads that come in through a Typeform.
The goal? Stop manually sifting through form submissions to figure out who's hot and who's not. I wanted an agent to read each new Typeform response, ask clarifying questions if needed, and then slap a clear label and next step into my CRM (I'm using Airtable for this test).
Here’s the step-by-step of how I wired it up in Lindy, along with some gotchas and observations:
**The Basic Flow I Built:**
1. **Trigger:** New submission on my Typeform "Contact Us / Demo Request" form.
2. **Lindy Agent Task:** "Qualify Lead."
3. **The Agent's Job:** Analyze the response. Key things I'm having it look for:
* Budget mentions (specific numbers, ranges like "5k-10k").
* Timeline words ("ASAP", "next quarter", "exploring").
* Use case fit against our features.
* Team size (is it a solo founder or a 100-person team?).
4. **Decision Point:** If critical info is missing (like no budget indicator), the agent is configured to reply via email with *one* very specific, clarifying question. Once the human replies, it re-evaluates.
5. **Output & Action:** The agent then creates/updates a record in my Airtable base with:
* A lead score (Hot/Warm/Cold, based on my own logic rules).
* A summary of the qualification reasoning.
* A recommended next action ("Schedule demo", "Send pricing", "Nurture sequence").
* All the original form data.
**What Worked Surprisingly Well:**
* The "ask a clarifying question" loop is magic. It feels like a real, attentive SDR for simple stuff. I had it ask "To make sure I route you to the right team, could you share if you have a rough budget range in mind?" and the reply rate was high.
* Configuring the lead scoring logic was straightforward. I used a simple points system in the agent's instructions, like "+1 point if timeline is 'ASAP', +2 if budget > $10k". Lindy's context handling makes referencing these extracted values easy.
* The audit trail is solid. I can always see why the agent scored a lead a certain way, which is crucial for tuning the model later.
**Pitfalls & Lessons Learned (The Good Stuff):**
* **Typeform webhook setup:** Straightforward, but remember to map the questions to clear variable names in Lindy. The raw JSON has the question IDs, which are useless. Do the mapping once, test it thoroughly.
* **Agent over-reach:** My first version tried to ask *two* questions if two data points were missing. Big mistake. It confused users. **One clarification per cycle** is the golden rule.
* **Cold lead handling:** I initially had it sending a "thanks but no thanks" email to clear mismatches. Too aggressive. Now it just scores them 'Cold' and files them in Airtable for a monthly review. Automation should facilitate human judgment, not replace it in sensitive cases.
* **Cost watch:** Each agent run + the potential email back-and-forth adds up. I'm tracking cost per qualified lead now versus my old manual time. Early ROI looks positive, but it's not zero-cost.
**The Real Test - Cohort Analysis:**
I'm running this for 30 days on incoming leads. I'll then compare a cohort of agent-qualified leads vs. my manually qualified ones from last month on:
* Time-to-first-contact (should plummet).
* Conversion rate from lead to demo (does pre-qualification improve it?).
* My own hours saved (to calculate a hard ROI).
The beauty here is the flexibility. Once this is tuned, I can clone this agent for other form sources (like a Google Form for webinar sign-ups) with slight tweaks. The core of "ingest, clarify if needed, score, and push to system of record" is so powerful.
Has anyone else built a similar qualification pipeline? I'm especially curious about how you're handling lead scoring logic or if you've connected Lindy to a proper CRM like Salesforce yet.
🔥
Try everything, keep what works.
Interesting approach using keywords like "ASAP" and "5k-10k" for the agent to parse. That's going to get brittle fast as soon as someone writes "we need it quickly" or "around ten thousand dollars."
Have you considered adding a separate classification step before the qualification logic? Something to catch and normalize that natural language variation first. Otherwise your false negative rate on hot leads will creep up.
Build once, deploy everywhere