Most scholarship platforms help you collect and route applications. But when it comes to actually reading essays, reviewing transcripts, and making award decisions, the burden still falls on staff and volunteers. Sopact Sense goes further.
By combining clean-at-source data collection with AI Agents that extract insights from open-ended text and documents, it transforms the review process into a faster, more consistent, and more transparent experience.
Instead of weeks of manual reading, reviewers see clear summaries, rubric-based scoring, and evidence-linked flags in hours. This means more scholarships awarded fairly, with less volunteer fatigue and greater accountability.
Scholarship Management — Step-by-Step (Intelligent Suite)
Follow mini-steps → Copy prompts → Launch results → Explore “What else can you do?”.
| What do you want to accomplish | Design Goals / Description | Fields | Prompt | Results | What else can you do? |
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| Summary Video | Correlate qualitative + quantitative data in minutes with Intelligent Columns (Girls Code example). Mixed results; share/save report for quick insights. | N/A |
Select two fields → Write a one-line prompt → Generate instant correlation report.
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Launch ▶ |
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| Data Collection Form | Design clean, AI-ready forms. Use unique IDs to connect essays/docs to each applicant. | Quant: GPA, Financials Qual: Motivation Letter, Transcript |
Collect at source → Validate → Link artifacts to Applicant ID for AI-ready analysis. |
Launch ▶ |
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| Review Documents (Cell) AI Cell | Standardize first-pass review by extracting key summaries from essays and PDFs. | Motivation Letter, Transcript |
Motivation Letter → Extract and summarize the applicant’s financial challenges in one clear sentence.
Transcript → Summarize the applicant’s grades and overall academic performance in one clear sentence.
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Ready |
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| Review Applicant (Row) AI Row | Quick summary table: red flags (Go/No Go), 3–4 sentence overview, rubric on Motivation Letter. | Qual + Quant outputs from Cells |
Combine qualitative (motivation letter, resume, recommendation) and quantitative (transcript grades, scores). Output: quick summary table with red flags (Go/No Go), an overall summary (3–4 sentences), and rubric (Financial Need, Academic Potential, Leadership, Motivation).
Outcome: defensible per-applicant report assembled from Cells.
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Launch ▶ |
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| Summarize Results by Demographics AI Column | Aggregate fields to reveal acceptance/rejection patterns and disparities. | Gender, University |
Summarize scholarship results by demographics — show acceptance and rejection patterns across Gender and University, highlighting any disparities or trends in one clear table.
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Launch ▶ |
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| Summarize Results for Entire Batch AI Grid | Cohort-wide view: acceptance rate, rejections, waitlist, and key decision drivers. | All |
Aggregate scholarship results for the entire applicant batch — provide overall acceptance rates, rejection counts, waitlist counts, and highlight top 2–3 reasons driving decisions across the cohort.
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Launch ▶ |
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| Calculator Ops | Quantify time & cost saved by using AI for first-pass review. | Apps, Manual mins/app, AI mins/app, Hourly value |
With 400 applications at 30 min/app = 200 hours. AI at 3 min/app = 20 hours. Save ~180 hours each cycle (worth thousands), turning weeks into days.
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Time & Cost Savings Calculator
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Enter values and click “Calculate Savings.”
