Uplifted

• Pricing model: Self-serve monthly SaaS subscription, with tiered plans published on Uplifted’s own pricing page; also described in the FAQ as customized by team size and asset requirements for quote-based sales conversations.
• Starting price: $99 / month
• Typical range: $99–$499 / month
• Billing: Monthly subscription; FAQ says flexible month-to-month with no long-term commitment.
• Contract terms: Month-to-month; no long-term contract stated.
• Free trial: Free plan available at $0/month; pricing page says “Start free, no credit card needed.”

Despite the user prompt calling Uplifted an agency, uplifted.ai currently presents itself as a software platform with a public pricing page. The strongest evidence is first-party: Starter is $99/month, Core is $299/month, and Growth is $499/month, alongside a free $0/month plan. Uplifted’s FAQ also says pricing is customized based on team size and asset requirements, which likely reflects enterprise or higher-complexity deals beyond the published self-serve tiers; I did not find credible third-party directory pages clearly matching uplifted.ai with additional minimums, hourly rates, or retainers.

An engagement with Uplifted appears to center on consolidating a client’s existing ad creative and performance data into one workspace, auto-tagging and analyzing it, then using those insights to brief, review, approve, and remix new ad variations. Customer review evidence is sparse, but the available G2 reviews plus Uplifted’s own detailed workflow pages consistently point to a process built around asset ingestion, AI tagging, performance-linked analysis, collaboration, and iterative ad production.

1. 1

   Connect assets and ad data

   The first step is to bring the client’s creative into Uplifted by connecting storage and ad platforms or uploading directly. Uplifted’s materials say it pulls assets from sources like Drive/Dropbox and ad performance from platforms such as Meta, while its own workflow guidance emphasizes linking ad accounts so each asset carries performance metrics.

2. 2

   Auto-tag and organize the library

   Once assets are inside the platform, Uplifted applies AI tagging across visuals, transcript/audio, and creative structure, and organizes the work into a searchable library. Its guidance also describes custom tag schemas so teams can classify content by the concepts that matter for that brand rather than relying on manual foldering.

3. 3

   Analyze what creative elements are working

   After ingestion and tagging, the team reviews performance at the ad, clip, hook, CTA, and scene level to identify patterns in what is driving results. Uplifted repeatedly describes connecting tags to live performance data so users can find not just existing assets, but the specific creative elements that are performing.

4. 4

   Generate or assemble the next brief from evidence

   Using those findings, the next phase is to turn prior results into a new creative brief and pull relevant reference assets into it. Uplifted’s briefing workflow says it analyzes past campaigns, extracts the patterns that worked, generates a structured brief, and connects that brief to the asset library.

5. 5

   Collaborate, review, and approve creative

   Production and internal collaboration then happen inside the same system through boards and approval/review features. Uplifted’s how-to pages explicitly reference organizing assets into boards and reviewing and approving creative assets in-platform, and its briefing workflow says production review is part of the connected loop.

6. 6

   Remix winning clips into new ad variations

   With the brief and approvals in place, the team reuses top-performing moments and unused matching footage to build fresh ad variants instead of starting from scratch. Uplifted’s ad-iterations documentation describes selecting winning parts, mixing them with unused creative, and creating new versions based on proven hooks, visuals, and CTAs.

7. 7

   Launch, monitor results, and repeat

   Finally, the new creatives are pushed live, their results are pulled back into the platform, and the cycle repeats. Uplifted’s process pages describe a closed loop in which teams launch variants, watch the numbers, feed performance back into the asset view, and use that evidence for the next iteration.