> For the complete documentation index, see [llms.txt](https://docs.raiaai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raiaai.com/use-cases/case-study-postgame.md). # Case Study: Postgame #### Case Study **Postgame scales NIL-influencer operations with an AI “Agentic Workforce” powered by raia** *** **Snapshot** | | Before raia | After raia | | ---------------------------- | ----------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | | Athlete support | 3 staff members handling <3,000 monthly messages | **AI Athlete Support Agent** fields **12 k+ inquiries / month** across SMS & email, 24 / 7 | | Campaign sentiment reporting | Manual review of 40 k+ Instagram comments per campaign (≈ 5 days) | **AI Sentiment Analyst** completes same work in **<3 hrs (-95 % cycle-time)** | | Content‐compliance checks | Spot-checks only, risk of off-brand posts | **AI Content Review Agent** auto-screens every post before it goes live | | Operational capacity | Team hit scale ceiling at \~10 simultaneous brand activations | Agentic Workforce now supports **5× more concurrent campaigns** without hiring | > “raia is by far the most powerful platform for building AI Agents. We would never be able to scale our launching of AI Agents with confidence without their sophisticated tools for training and testing, along with the capability of their agents to send SMS to our athletes.”\ > **Bill Jula, CEO, Postgame** *** #### 1. Company & Challenge **Postgame** operates the **largest college-athlete influencer network—60,000 + student-athletes** nationwide([Postgame App](https://pstgm.com/PORTAL/?utm_source=chatgpt.com)) and runs campaigns for global brands such as **Adidas, Microsoft, Hollister and hundreds more**([Postgame App](https://www.pstgm.com/?utm_source=chatgpt.com)). Growth exposed three pain-points: 1. **Athlete Questions at Scale** – a single Adidas campaign can enlist 5,000 athletes, each needing timely answers on briefs, deadlines and payment. 2. **Comment-Sentiment Analysis** – tens of thousands of Instagram comments per campaign required manual reading to prove ROI to brands. 3. **Content-Guideline Enforcement** – every reel, story or tweet had to be vetted for brand safety, copyrights and NIL regulations. The existing team could not keep pace without eroding margins or service quality. *** #### 2. Why Postgame chose raia * **Zero-code Launch Pad wizard** let non-technical staff spin up bespoke agents in minutes, not sprints. * **Omnichannel skills out of the box**—SMS, email, live chat and voice—critical for reaching on-the-go student-athletes. * **CoPilot human-in-the-loop** gave campaign managers confidence they could jump in or retrain the agent instantly when nuance was required. * Built-in **sentiment & engagement scoring** streamlined analytics workflows without extra tooling. *** #### 3. Solution Design | AI Agent | Primary Role | Key Inputs / Skills | | --------------------------- | ------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------- | | **Athlete Support Agent** | 24 / 7 Q\&A on briefs, deadlines, how-to-post, payment status; pushes reminders via SMS. | Skills: SMS, Email, Live Chat; Pack: *Campaign FAQ* | | **Sentiment Analyst Agent** | Scrapes Instagram & TikTok comments, tags positive/negative/neutral, rolls up brand-safe report. | Skills: Scraper, Sentiment, Report; Pack: *Sentiment Rules* | | **Content Review Agent** | Ingests draft captions & media, checks for brand guideline compliance, NIL language, copyright issues; approves or flags. | Skills: File-parser, Webhooks; Pack: *Brand Guidelines* | Deployment timeline: **< 4 weeks** for all three agents—upload brand manuals, connect influencer CRM, iterate in CoPilot. *** #### 4. Measurable Impact (first 90 days) | KPI | Result | | ---------------------------------- | --------------------------------------------------------------------- | | Athlete interactions handled by AI | **≈ 12,000 / month** (SMS 70 %, Email 25 %, Chat 5 %) | | Average first-response time | **< 45 seconds** (was ≈ 3 hrs) | | Sentiment analysis turnaround | **3 hrs** for 55 k comments (was 5 days) | | Compliance review throughput | **100 % of posts pre-screened**; human review load cut by 80 % | | Human workload | AI capacity equals **8–10 FTEs**, freeing staff for sponsor relations | *** #### 5. Lessons for Prospective Clients * **Start with the highest-volume pain-point**—support agent success created internal appetite for analytics & compliance bots. * **Use pre-built Packs** to replicate success across brands; once Postgame built an “Adidas Campaign Pack,” a Hollister version took hours. * **Maintain HITL during ramp-up.** CoPilot let brand managers shadow early chats, accelerating trust and tweaks. *** #### 6. What’s Next Postgame plans to: * Launch a **Revenue Uplift Agent** to recommend best-fit athletes for each brief in real time. * Automate **payments reconciliation** by linking campaign metadata to banking APIs. * Orchestrate multi-agent workflows so Support, Sentiment and Compliance agents collaborate on a single campaign thread. *** #### 7. Key Takeaway Postgame’s journey shows how **raia transforms influencer-marketing ops from human-bound to AI-amplified**—rapid deployment, omnichannel reach and measurable ROI in weeks, not quarters. {% embed url="" %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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