Clema

Clema (Higher Ed Co-Pilot)

Higher Ed Co-Pilot lets you query the federal database of every US college and university using natural language. Compare institutions, track trends, and export data—no more downloading CSVs or navigating clunky interfaces. Built specifically for Institutional Research (IR) teams to streamline federal reporting and benchmarking.

Key Features

• Natural Language Querying: Ask plain English questions to query complex datasets like IPEDS, College Scorecard, and EADA (Athletics).
• Instant Benchmarking: Compare graduation rates, enrollment, or finances across peer institutions side-by-side.
• Trend Analysis: Visualize multi-year historical data automatically without manual Excel work.
• Citation & Transparency: Every answer links back to the specific federal data source table for verification.
• Data Governance: SOC 2 Type II certified and FERPA compliant, ensuring institutional data safety.
• Export Ready: Download clean charts and datasets directly for use in presentations or further analysis.

How It Works

IR teams ask questions like “Compare graduation rates of Ivy League schools vs. Big Ten over the last 5 years” or “Show me the retention trends for Pell Grant recipients at public 4-year institutions.” Clema translates this into SQL-like queries against federal databases, retrieves the exact data points, generates comparison charts, and provides exportable files, saving hours of manual data wrangling.

Use Cases

• Institutional Research (IR) Reporting: Answering ad-hoc data requests from university leadership instantly.
• Strategic Planning: Benchmarking university performance against aspirational peer groups.
• Grant Applications: Quickly gathering demographic and outcome data required for funding proposals.
• Market Analysis: Analyzing enrollment trends to identify competitive threats or opportunities.
• Compliance Verification: Checking figures against federal standards before publication.

Pros & Cons

• Pros: Massive time saver for IR teams (eliminates “CSV hell” and IPEDS navigation); Access to cleaned, normalized federal data; Natural language lowers the barrier to entry for non-technical staff; High compliance standards (SOC 2).
• Cons: Niche market focus (Higher Ed only); Accuracy is critical (hallucinations are unacceptable in IR contexts); Dependent on the update cycles of federal databases (data is only as new as the government release).

Pricing

Freemium Model.
* Free: Basic access to querying federal datasets for individual researchers.
* Team/Enterprise: Paid tiers for advanced collaboration, private institutional data integration, and enterprise-grade security controls.

How Does It Compare?

Clema competes less with other “AI chatbots” and more with the manual, legacy workflows currently used in universities.

• vs. IPEDS Data Center / NCES Tools:
  • The Difference: The government’s own tools are free but notoriously difficult to navigate, requiring deep knowledge of variable codes and manual CSV downloads.
  • Winner for you?: Use Clema for speed and ease of use. Use NCES if you need the absolute raw source file for audit purposes.
• vs. Tableau / Power BI:
  • The Difference: These are general BI tools. You have to build the dashboard, clean the data, and maintain the ETL pipeline yourself. Clema comes “pre-loaded” with the data and logic.
  • Winner for you?: Use Tableau for visualizing your own internal private data. Use Clema for benchmarking against external public data.
• vs. General AI (ChatGPT / Claude):
  • The Difference: Generic LLMs often hallucinate specific numbers or struggle with the precise column definitions of IPEDS data. Clema is grounded in the structured database, ensuring the numbers cited are real.
  • Winner for you?: Always use Clema for data accuracy. Use ChatGPT for drafting the qualitative text of the report.

Final Thoughts

Clema allows IR professionals to stop being “data mechanics” and start being “data architects.” By automating the tedious retrieval of federal data, it frees up analysts to focus on the interpretation of the numbers rather than the extraction. For any higher education institution looking to become more data-informed without hiring more data engineers, this is an essential toolkit addition.