Voice Fit Finder
You spend hours scrolling feeds and pasting links into spreadsheets, only to pay for reach that never converts. Creators chosen for how their content and comments actually sound, not for bios or follower counts.
01The offer
For marketing leads at ecommerce brands and consumer startups, turn product briefs, customer profiles and campaign tone into a ranked shortlist of micro voices with plain English fit reasons. Address the recurring problem: agencies push macro names with inflated follower counts and mismatched audiences, so you pay for reach you never get.
- For
- Marketing leads at ecommerce brands and consumer startups
- Takes in
- Product brief, customer profile, campaign tone, platform scope
- Delivers
- Ranked shortlist of under 30 micro voices with plain English fit reasons
- Message
- Stop paying for reach you never get. Get a shortlist of micro voices who actually move your customers.
- Lead magnet
- A free sample shortlist for one platform with three ranked voices and fit reasons.
02How it works
- Describe product, customer and campaign feeling in a structured brief
- Crawl public social profiles across platforms
- Score each voice on audience overlap, comment intent and style alignment
- Rank voices with a one paragraph plain English fit reason
- Filter and sort by platform, score or audience size
- Export a shareable memo for team or agency
Workflow
Create a brief, set platform scope, run the scan, review ranked voices, read fit reasons, filter to a shortlist, and export the memo. Start with product brief, customer profile and campaign tone and finish with a ranked shortlist of under 30 micro voices with plain English fit reasons.
AI and people
Use language models to read months of timeline content, scoring tone, credibility and purchase signals in comments. Use vision models to assess visual style and brand fit from post images. A person checks the fit reasons and shortlist before anything is sent to a client or used in a campaign.
Screens
Key screens: Brief builder, voice discovery, fit scores, shortlist review. Use a dashboard with a brief form on the left, a results grid in the middle showing ranked voices with fit scores, and a detail panel on the right for the plain English reason. Filter by platform, audience overlap and tone. Save shortlists and export as a shareable memo. In this product, the first view is brief builder, followed by voice discovery, fit scores and shortlist review.
Admin
Brief ownership, scan history, saved shortlists, export logs, user roles, approval states for final shortlists, and an audit trail of scoring changes.
03Market gap
Alternatives buyers use today
Manual scrolling, agency lists, influencer platforms with follower counts. This differs by using AI to read real content and intent signals, not vanity metrics.
Where this wins
The scoring models improve with every brief and outcome, and the crawl history builds a proprietary map of audience quality that competitors cannot easily replicate.
04Why now
Marketing teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. The buyer already feels the problem: you spend hours scrolling feeds and pasting links into spreadsheets, only to pay for reach that never converts.
05Proof & signals
Channels where buyers gather: LinkedIn, ecommerce communities, influencer marketing forums, and direct outreach to marketing leads.. Metrics that prove it works: Shortlist to campaign conversion rate and cost per acquired customer from shortlisted voices..
Paid pilot
Run a paid pilot with 10 ecommerce brands; baseline is their current agency shortlist quality and conversion. Decision to expand if 7 of 10 report a better fit and lower cost per acquisition.
06Execution plan
MVP
One platform, one buyer type (ecommerce marketing leads), first two modules are brief builder and voice discovery with manual review of fit reasons.
First 30 days
Week 1: Build brief builder and single platform crawler. Week 2: Implement scoring models and fit reason drafting. Week 3: Build shortlist review and export. Week 4: Test with 5 brands and refine scoring.
After the pilot
Automate fit reason drafting, add cross-platform deduplication, and introduce a retainer tier with scheduled rescans for monthly campaigns.
Retention
Monthly rescans and fresh shortlists for retainer clients, plus a history of voice performance to improve future recommendations.
Integrations
Public social APIs (Instagram, TikTok, X), CSV export, and later CRM and campaign management tools.
07Investment and running costs
| Phase | Scope | Time | Budget |
|---|---|---|---|
| MVP | One buyer segment, one recurring use case. Manual review in the loop. | 4 days | $8,000 |
| Paid pilot | Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers. | 5 days | $9,000 |
| Full product | Self-serve onboarding, billing, monitoring and the wider integration set. | 9 days | $13,000 |
| Total | $30,000 | ||
| Running | Hosting | AI usage | Total a month |
|---|---|---|---|
| MVP and paid pilot (about 3 customers) | $30–$60 | $40–$90 | $70–$150 |
| Full product (about 50 customers) | $110–$210 | $280–$560 | $390–$770 |
Revenue model to test
Test pricing at $1,500 per brief for a complete shortlist, with a $3,000 monthly retainer for brands running ongoing campaigns.
Cost drivers
Cloud compute for crawling and inference, model API costs, and a small data compliance review per platform.
Safeguards
Limit crawling to public data, respect platform rate limits and terms, require human approval on all shortlists, and block any use of private or non-consented data. Must not scrape private accounts or share personal data without consent.
Take it further
Solution blueprint, rewritten from an earlier Nexibeo concept. Demand, pricing, build scope and integrations are working assumptions, not verified market findings. The MVP and the paid pilot exist to confirm them for your business before the larger build.