How a K-12 STEM Provider Cut Admin Time by 70% with an AI Operations Platform

The client is a U.S.-based education technology company that delivers hands-on STEM learning experiences to K-12 students through mobile labs, project kits, teacher training, and a logistics and content management platform. Their programs reach over 500,000 students across more than 1,000 schools and 150 school districts. Approximately 90% of implementations are funded by corporate sponsors as part of social responsibility and workforce pipeline programs, making the service free for participating schools.

The company managed every school engagement manually. Service requests from educators arrived through email, phone calls, and web forms. Staff tracked them in spreadsheets and personal inboxes. As demand grew past 1,000 schools, the system broke down in two distinct ways.

First, requests were missed. Not occasionally — regularly. Schools that had secured corporate funding and scheduled student activities received no response because submissions sat in queues. The administrative team spent three to five business days processing a single request, from intake through blueprint creation to task assignment. During peak enrollment periods, that timeline stretched further.

Second, the data was there but inaccessible. The company had years of program data in Airtable — school profiles, resource inventories, engagement histories, funding allocations — but no way to query it intelligently. Matching a school's request with available resources, scheduling windows, and eligible funding programs required administrators to cross-reference multiple tabs and filter manually. The data existed. The team just couldn't get answers from it quickly enough to keep pace with demand.

The CEO had worked with other development vendors before. Those experiences were not good. This time, he ran parallel discovery phases with multiple companies to compare proposals side by side. The company needed a partner who could handle both the platform engineering and the AI layer — building not just an operational system, but an intelligent one capable of turning years of accumulated program data into usable fuel for decision-making.

Cleveroad stood out early. Our team organized a structured workshop covering every feature the platform would need, then delivered a discovery phase with detailed architecture documentation, AI capability mapping, and process diagrams. The level of specificity in artifacts distinguished us from competitors who stayed at the surface level.

Cleveroad designed and built a custom AI-powered operations platform from scratch, split into two web applications: a portal for educators and district representatives, and an admin panel for internal operations staff. On top of the core workflow engine, the team integrated an AI layer running on AWS Bedrock — Claude 3.5 Sonnet for complex query translation and resource matching, Claude 3.5 Haiku for the educator-facing assistant — to solve the second half of the client's problem: making their data accessible and actionable.

The educator-facing portal lets schools submit service applications for mobile STEM labs and project kits, review and approve tailored blueprints, and track tasks tied to their engagement. District representatives can submit applications across multiple schools at once and assign responsible educators. A conversational assistant powered by Claude 3.5 Haiku allows educators to ask questions about available STEM resources, check program eligibility, and receive recommendations tailored to their school's profile and past engagement history — replacing a process that previously required direct calls to the admin team. Haiku's low latency keeps the experience responsive, even during peak enrollment periods when hundreds of educators may be submitting requests simultaneously.

The admin panel gives operations staff a single workspace for reviewing applications, building blueprints, managing resources, assigning tasks, and administering funding programs. An AI-powered query interface lets administrators ask questions about their operational data in plain language — such as which schools in a district have pending applications, which resources are available for a given date range, or which funding programs are approaching their allocation limits. Claude 3.5 Sonnet translates these natural-language questions into structured database queries, returning results as filterable tables. Sonnet was selected for this layer because of its accuracy with multi-step reasoning over the platform's relational schema.

The platform was integrated with the client's existing Airtable database through a bidirectional sync, so the transition did not force the team to abandon their established data workflows.

The platform combines a structured operational pipeline with an AI intelligence layer:

Intake. Educators submit structured service applications through the portal. The Haiku-powered assistant helps them select resources based on school size, grade levels, and curriculum alignment.

Match. Claude 3.5 Sonnet analyzes incoming requests against resource availability, scheduling constraints, and funding program eligibility. The system surfaces recommended matches for administrators, reducing the manual cross-referencing that previously consumed hours per request.

Blueprint. Administrators prepare tailored service proposals using AI-generated draft recommendations as a starting point. Educators review, edit, and approve within the platform.

Activate. Approved blueprints generate structured task lists. Tasks are assigned to team members with deadlines and status tracking.

Query. Sonnet powers the natural-language-to-query pipeline, translating administrator questions into structured database lookups using a RAG architecture. The pipeline retrieves relevant schema context, constructs the query, and returns filterable results from the platform's operational data and the synced Airtable records.

Sync. Data flows bidirectionally between the platform and Airtable, keeping both systems consistent without manual re-entry.

The team validated AI output accuracy through a human-in-the-loop review process during the initial deployment, with administrators verifying query results and resource match recommendations before acting on them.

• Cloud: AWS Bedrock
• LLMs: Claude 3.5 Sonnet (query pipeline, resource matching, blueprint drafts), Claude 3.5 Haiku (educator-facing assistant)
• AI architecture: RAG-based natural-language-to-query pipeline for operational data retrieval
• Platform: Two separate web applications (Educator Portal + Admin Panel)
• Integration: Bidirectional Airtable sync via custom API layer
• Document management: Built-in Blueprint creation, versioning, and approval workflow
• Access control: Role-based permissions (Educator, District Representative, Admin, Super Admin)

Request processing time dropped from three to five business days to under one day. Missed school requests — previously a recurring problem during high-demand periods — dropped to zero after the platform went live. Every application now enters a tracked queue with status visibility for both the school and the operations team.

The AI query interface changed how administrators interact with their operational data. Questions that previously required exporting Airtable views, applying filters, and cross-referencing multiple sheets now return answers in seconds through a conversational interface. Resource matching — once the most time-consuming step in the process — became partially automated, with Sonnet surfacing eligible programs and available inventory based on each school's profile.

Blueprint turnaround improved as well. AI-generated draft recommendations gave administrators a starting point instead of a blank page, compressing the proposal creation cycle. The Airtable integration preserved the team's existing data practices while the platform handled synchronization automatically, removing the manual data re-entry that had consumed hours each week.

The client has moved into the second phase of development with Cleveroad. The engagement has expanded beyond the operations platform: Cleveroad's solution team reviewed the client's separate content delivery platform and prepared an estimation for AI-assisted improvement. The roadmap includes extending the Sonnet-powered query layer to additional products in the client's portfolio and exploring AI-driven recommendations for curriculum alignment and program impact measurement.