I built an influencer finder because I had a growth problem that did not care that I was a team of one. I was building an iOS app and I needed creators in a very specific niche: faith-based recovery, men's accountability, and personal discipline. The usual advice was useless. Generic influencer databases were noisy, Instagram manual research was slow, and every hour spent digging through profiles was an hour not spent building the product. So I built a workflow with AI agents that could browse Instagram, watch their videos , follow account graphs, judge relevance, extract contact information, and hand me a shortlist of creators to review the next morning.
This post is the real version of that system: what I used, what actually worked, what kept breaking, and why the biggest insight was not the model or the prompt. It was the discovery method.
Why I Built It
The problem was never "find influencers." The problem was "find the right influencers without wasting half the day."
For the app, I was not looking for broad wellness creators or generic Christian lifestyle pages. I needed a narrow slice of the internet where people were already talking about addiction, discipline, shame, relapse, recovery, accountability, and rebuilding your life. That is a completely different search problem.
Manual research looked like this:
- open Instagram
- search for a few keywords
- click into profiles one by one
- check follower count
- watch their videos
- read the bio
- look for contact details
- decide whether the account was actually relevant
- repeat until exhausted
It was slow, repetitive, and mentally expensive. A single session would produce a handful of useful leads if I was lucky. Then I had to do it all again the next day.
That is exactly the kind of work agents should take over: structured repetition plus fuzzy judgment.
The Stack I Used
Here is the stack that emerged after a lot of trial and error:
OpenClaw -> browser automation and agent orchestration
MiniMax -> LLM Model used in OpenClaw (One of the best and cheapest model designed specifically for AI-agents)
Veedcrawl -> video metadata,transcript analysis and hook analysis
AgentMail -> email delivery and outreach handoff
Instagram -> discovery surface
Each part solved a different bottleneck.
OpenClaw gave me a persistent browser agent that could navigate Instagram the way a human would. It could open profiles, click through "Similar accounts," follow links in bios, and keep moving without me supervising every second.
MiniMax the LLM Model used inside OpenClaw.
Veedcrawl mattered because a bio does not tell the whole story. Some accounts look relevant on the surface but the actual content is off-topic. Veedcrawl let me inspect video metadata, transcripts quickly and the hooks of viral videos, which helped answer a much more important question: does this creator consistently publish content that fits the campaign?
AgentMail handled the final handoff. Once the workflow surfaced a contact and I approved it, the email could move into delivery instead of staying trapped in a spreadsheet.
None of this was especially elegant. That was fine. The goal was not elegance. The goal was leverage.
The Core Insight: Instagram Is a Graph
The most important idea in the whole system was not an LLM trick. It was recognizing that Instagram's "Similar accounts" feature is a graph traversal interface.
Every good seed account leads to a cluster. Open one relevant profile, click "Similar accounts," and Instagram gives you a set of adjacent nodes that already share audience and topical overlap. Each of those accounts can lead to more accounts. If your first node is strong, the next few layers of the graph are usually strong too.
That changes the discovery problem completely.
Instead of searching from scratch every time, the workflow became:
1. Start from one deeply relevant seed creator
2. Open "Similar accounts"
3. Inspect each surfaced account for relevance
4. Extract contact details if available
5. Recurse only on qualified accounts
6. Stop when quality drops
That is a much better search method than keyword-hunting forever inside Instagram's top-level search box.
It also explained why some sessions felt magical and others were useless. The quality of the whole run depended heavily on the starting seed. If I started with a creator who was truly embedded in the recovery/accountability niche, the graph stayed clean. If I started with a broad motivational account, the graph drifted into generic self-improvement and lifestyle content almost immediately.
The agent stack helped me scale the method. The method itself is what made the stack valuable.
What the Workflow Actually Did
At a high level, the system worked like this:
1. Choose a seed account
2. Use OpenClaw to browse the profile and open similar accounts
3. Use MiniMax to classify each account:
- relevant or not relevant
- niche fit
- likely outreach priority
4. Use the browser to check for contact details:
- bio
- linked site
- Linktree
- Beacons
5. Use Veedcrawl to inspect video content when the bio was ambiguous
6. Return a shortlist of creators with context and email availability
7. Hand approved contacts to AgentMail
The system was not trying to fully replace me. It was trying to compress the expensive part of research.
That distinction matters. Full autonomy sounds impressive, but in practice the highest-value setup was a semi-autonomous pipeline:
- the agents did discovery
- the models did triage
- I handled final approval and campaign direction
That was enough to create real leverage without pretending the system was smarter than it was.
Where Veedcrawl Helped More Than I Expected
I originally thought Veedcrawl would be useful only for deep content analysis later in the funnel. In practice, it became useful much earlier.
A creator bio is often too thin to trust. Someone might describe themselves as a faith-based mentor, but their recent videos could be generic motivation clips with no overlap with the actual campaign. Another creator might have a weak bio but publish exactly the kind of short-form recovery content I wanted.
That is where Veedcrawl helped.
With metadata, I could quickly check whether the account was actively posting, the average view-count , the saves and the comments, what kinds of clips were getting traction, and whether the content fit the intended audience. With transcripts, I could go beyond the surface and inspect the actual language used in the videos. That matters a lot in niche outreach and using extract, I can analyze the hook,the camera charisma of the creator. A creator can look similar by category while speaking to a completely different audience.
For influencer discovery, that extra layer changes the quality of the shortlist. Instead of "this person has 80K followers and seems vaguely relevant," the system could produce something closer to:
- this account posts consistently about discipline and recovery, with average views of 10K
- the video language matches the audience I want
- the video style and the creator uses good hooks , good audio and is relevant to our app.
- engagement is concentrated on the right topics
- outreach is worth attempting
That is a better output for a founder than a raw list of usernames.
What Broke
A lot broke. That is worth documenting because this kind of system sounds smoother in hindsight than it felt in reality.
Browser sessions were fragile
Long-running browser automation is still brittle. After enough navigation, the session would degrade or the CDP connection would die silently. The failure mode was annoying because the browser often looked "alive" until the next action failed.
The practical fix was not clever. I added health checks, checkpointed progress, and accepted that supervised restarts were part of the workflow.
Instagram forced a read-only posture
I was not trying to push the platform too hard. Even so, it was obvious that the safe version of this system was read-only:
- browse profiles
- inspect pages
- extract publicly visible information
- avoid actions like follow, DM, or like
That constraint turned out to be useful. It kept the workflow narrow and pushed outreach into email, which was a better channel anyway.
The "run it forever" script failed
I tried to build a background hunter that would keep traversing while I slept, completely unsupervised. That version was the wrong abstraction too early. The first sleep cycle or browser disconnect would kill the run, and debugging long unattended sessions was miserable.
The lesson was simple: do not build the daemon before you understand the working manual loop.
Email extraction stayed hard
Discovery was not the hardest part. Contact acquisition was.
Many small and mid-sized creators do not expose an email directly in the bio. Sometimes it is on a Linktree page. Sometimes it is buried in a Beacons setup. Sometimes it is nowhere public. JavaScript-heavy landing pages also made extraction more annoying than plain text bios.
That meant the workflow could identify good creators faster than it could always extract a clean contact path. The bottleneck shifted, but it did not disappear.
Seed quality determined everything
This was the biggest operational lesson. A strong seed account gave me a relevant subgraph. A weak seed polluted the whole run. Picking better starting points mattered more than adding another model or writing another prompt.
The Results
This is not a "we 10x'd revenue overnight" story. It is more useful than that.
The workflow processed far more accounts per session than I could manually. It surfaced creators I would not have found through normal keyword browsing. It reduced the amount of repetitive research I had to do personally. Most importantly, it let me wake up to a list of accounts that had already been explored, filtered, and partially validated.
That was enough.
As a solo founder, the win was not perfect automation. The win was getting back hours of attention.
The system also changed the quality of decisions. Instead of reacting to whoever looked good at first glance, I had more context:
- account fit
- content fit
- likely audience fit
- available contact path
That is the difference between random outreach and targeted outreach.
Why This Matters for Veedcrawl
This project also sharpened my view of what Veedcrawl is actually for.
AI agents are good at navigating interfaces, following routines, and handling repetitive research. But they are still blind to video unless you explicitly give them a way to inspect it. A bio, caption, or username is often not enough to decide whether a creator is relevant. The real answer is inside the content they make.
That is the gap Veedcrawl fills.
It lets an agent move from:
- "this profile looks promising"
to:
- "this creator content matches perfectly our app and the content is also top notch"
That is a much stronger primitive for any workflow involving:
- influencer discovery
- creator partnerships
- content sourcing
- niche research
- trend analysis
Your agent is only as good as the data it reasons from. A Veedcrawl API key gives it eyes on social media: the numbers behind the video, the words that were spoken, and the ability to ask any question about why it worked.