How Conscious Futures Sifts 25k+ Creators a Year into Qualified Leads

The current roster includes creators like Noémie Lenoir, Fatou N'Diaye, Gregory Pouy, and Bisila Bokoko... a mix of cultural voices, thought leaders, and content creators united by impact rather than vanity metrics.

The agency's core competitive advantage is finding the right creators before anyone else does.

That means filtering 25,000+ Instagram creators a year, scoring each against a custom set of criteria, and surfacing the small percentage actually worth a conversation.

It's a discovery problem. And on Instagram, the strongest discovery signal isn't follower count... it's reel performance.

The challenge: filtering Instagram creators with reel-level signal

It's a super complicated process. We basically take screenshots of a lot of 'Suggested accounts', then we start enriching bio, followers, etc., and through filters and a scoring system we extract only the people of interest.

Benjamin Alba,COO, Conscious Futures

Conscious Futures' COO, Benjamin Alba, built a custom screening pipeline in n8n to automate the discovery workflow.

To score an Instagram account properly, the pipeline needs two layers of data per profile.

Profile-level signals — standard Instagram metadata most scrapers can return:

1. Bio
2. Follower count
3. Niche / category
4. Verified status
5. Business account vs personal account

Reel-level signals — the engagement layer that drives the actual scoring:

1. Reel views
2. Reel likes
3. Reel comments (count and text)
4. Hashtags
5. Caption
6. Posted date
7. Sponsored flag

The profile layer is easy. Most tools handle it. The reel layer is where most tools fail.

Reel views and likes are the most reliable engagement signal Instagram exposes... and the hardest data points to scrape reliably.

Reel comments add the qualitative layer on top — a 50,000-view reel with three generic emoji comments scores very differently from a 50,000-view reel with 200 specific, on-topic replies.

Without reel-level signal, every other data point becomes guesswork. A creator with 50,000 followers and zero reel engagement isn't worth a partnership conversation.

A creator with 8,000 followers and reels averaging 200,000 views is. The pipeline is only as useful as its ability to tell those two accounts apart.

Why every other scraper was eliminated

Before settling on lobstr.io, Benjamin tested roughly ten Instagram-scraping tools across price points and architectures. The pipeline's reel-view requirement eliminated almost all of them.

We used Apify, cannot do reel views and super expensive. Phantombuster, good for reel views but asks for cookie ID and IG ends up blocking the account, also more expensive. I tried Scrape Creators too, super expensive. I really tried around 10 different ones.

Benjamin Alba,COO, Conscious Futures

The named tools and their dealbreakers:

At scale, the pricing gap against the closest functional competitor compounds quickly:

1. Apify: $1.00 per 1,000 reels, $1.60 per 1,000 profiles
2. lobstr.io: $0.50 per 1,000 results (reels or profiles)

Two times cheaper for reels. Roughly three times cheaper for profiles. Same data, lower unit cost, no cookie requirement, no account-ban exposure.

Lobstr was the best option we found specifically to get reel info and in terms of pricing. However, we noticed IG Reel views and likes seem to fluctuate a little bit from the real numbers. We are satisfied.

Benjamin Alba,COO, Conscious Futures

That's the honest read: lobstr.io's reel view counts can drift slightly from Instagram's published numbers (a known quirk of how Instagram exposes reel metrics publicly), and Conscious Futures chose lobstr.io anyway.

For a scoring system, the directional signal is what matters... a reel pulling 200k views vs. one pulling 2k reads correctly even with small fluctuations on the absolute count.

Building the screening pipeline with lobstr.io and n8n

The full pipeline runs as an n8n agent in the background. Five stages, each one feeding the next.

The flow:

1. Discover candidates. Conscious Futures pulls Instagram's "Suggested accounts" via screenshots, extracting handles to feed downstream. (This step happens outside lobstr.io.)
2. Enrich profiles with the Instagram Profile Scraper. Each handle is resolved to a structured profile: bio, follower count, niche, verified status, account type.
3. Pull reel performance with the Instagram Reels Scraper. Recent reels are pulled with full engagement signal — views, likes, comments (count and text), hashtags, captions, posted date, and sponsored flag.
4. Score every account in n8n against a custom rubric Conscious Futures developed for talent fit. Combinations of follower count, niche match, business-account flag, and reel-engagement thresholds produce a single score per profile.
5. Filter to qualified candidates. The pipeline surfaces the top-scoring accounts to a review list.

Both scrapers return rich structured data... 46 fields per profile, 39 fields per reel. Conscious Futures' scoring rubric trims that down to a focused subset:

// Profile Scraper output (trimmed to scoring fields)
{
  "username": "lena.movesdaily",
  "full_name": "Lena · Movement & Mobility",
  "biography": "NYC · Movement coach · DM for collabs ✉️",
  "followers_count": 14820,
  "category": "Health & wellness",
  "is_verified": false,
  "is_business_account": true
}
f

// Reels Scraper output (trimmed to scoring fields, most recent reel)
{
  "reel_url": "https://www.instagram.com/reel/DXa7K2pLm9F/",
  "owner_username": "lena.movesdaily",
  "timestamp": "2026-04-21T13:42:11Z",
  "views_count": 248611,
  "likes_count": 11340,
  "comments_count": 287,
  "hashtags": "mobility, holistichealth, mindfulmovement, somaticpractice",
  "caption": "Three minutes a day to undo what desk life is doing to your hips. Save this one for tomorrow morning...",
  "is_sponsored": false,
  "comment1": { "text": "Did this right after watching, my lower back already feels different", "user": "kara.flowstate", "replies_count": 2 },
  "comment2": { "text": "Saving this. I sit 10 hours a day and my hips are wrecked", "user": "marcus_lifts", "replies_count": null },
  "comment3": { "text": "Where do I sign up for your next workshop?", "user": "sara.moves.well", "replies_count": 1 },
  "comment4": { "text": "This is the kind of content I'm here for. No noise, just useful", "user": "evie.somatic", "replies_count": null },
  "comment5": { "text": "Doing this with my mom tomorrow. She has the same desk-life hips", "user": "jonah.r.health", "replies_count": null }
  // ...comment6 through comment10, plus 2 more recent reels with the same shape
}
f

Everything else gets dropped at the n8n stage.

That produces a single fit score per profile.

In this example, 14k followers paired with reels consistently pulling 190k–310k views is the asymmetry the screener is built to surface.

A mission-aligned creator the follower count alone would have hidden.

The n8n agent runs unattended and produces qualified candidates in roughly fifteen-minute cycles.

Conscious Futures hits the queue when they're ready to evaluate, instead of doing the discovery work manually.

Results

The pipeline is doing real volume.

Outcomes at Conscious Futures:

1. 25,000+ Instagram creators filtered annually down to a queue of qualified leads
2. $0.10 fully-loaded cost per qualified lead
3. 20+ qualified leads every 15 minutes of agent runtime, in the background
4. $0.50 per 1,000 results... 2.2× cheaper than Apify at Conscious Futures' usage mix
5. 125,000+ data pulls a year — profiles + reels feeding the screener at production volume
6. No account-ban risk... unlike cookie-based scrapers
7. No manual discovery work... the n8n agent runs unattended

I'm getting like 20+ relevant accounts in 15 minutes, working in the background. Cost per lead ends up being $0.10. Super cheap.

Benjamin Alba,COO, Conscious Futures

The screening pipeline is now a permanent part of how Conscious Futures finds creators.

The cost structure makes the workflow viable at a volume the agency couldn't reach with any of the alternatives, and the automation turns talent discovery from a manual research task into a queue of pre-scored candidates ready for human review.

Key takeaways

1. 25,000+ Instagram creators filtered annually into qualified leads
2. $0.10 fully-loaded cost per qualified lead
3. 20+ qualified leads every 15 minutes of agent runtime
4. $0.50 per 1,000 results... 2.2× cheaper than Apify at production mix
5. 125,000+ profile + reel pulls a year, all unattended
6. Full reel signal pulled directly... views, likes, comments, hashtags, captions... no cookies, no ban risk
7. Talent discovery as a queue, not a research project