Deriving Business Value from AI in EdTech & Training

September 10, 2025

One of the things I've learned building Epistemy is that AI is only valuable when it leaves the lab and enters real classrooms, training rooms, or student study routines.

Over the last year, my team and I delivered four very different AI learning projects — spanning test prep, consulting interview coaching, executive negotiation training, and large-scale language learning. Each project started with the same big question: How do we take expert teaching methods and make them scalable, reliable, and engaging through AI?

This post walks through anonymized case studies of those projects — what the use case was, what we built, the technical backbone (in plain English), and the business results.

Project A — Adaptive Test Prep for Standardized Exams

The Use Case

Preparing for global standardized exams is grueling. Students need daily consistency, instant feedback, and motivation. A leading prep provider asked us to create a system that would feel like having a personal tutor in your pocket.

What We Built

• A daily quiz engine that adapts difficulty to the student's performance.
• Instant AI explanations that don't just say "wrong," but explain the reasoning step by step.
• Gamification features — streaks, leaderboards, progress dashboards — to keep students engaged.

Technical Backbone

• LLM-powered feedback: We designed structured prompts so the AI could map each mistake to a category (concept gap vs. careless vs. timing).
• Evaluation: We built a gold dataset of real student answers and ran the AI against it until accuracy on feedback exceeded 90%.
• Dataset annotation: Our team and the client's tutors annotated hundreds of answers with categories and explanations, creating the training ground for consistent feedback.
• Cost & latency control: To keep things affordable, we used retrieval to pull in reference solutions instead of asking the LLM to generate everything from scratch.

Business Impact

The app launched with thousands of students in the first few weeks, boosted engagement, and reduced support tickets ("why was my answer wrong?"). For the client, this meant differentiation in a highly competitive market and a new tech-driven brand identity.

Project B — AI Coaching for Case Interview Candidates

The Use Case

Tens of thousands of consulting applicants need practice in mental math and structuring business problems. Traditionally, this requires expensive 1:1 coaching. Could AI provide credible feedback at scale?

What We Built

• AI Math Drills — candidates solve problems aloud. The system transcribes, checks the logic in Python, and evaluates speed, clarity, and accuracy.
• AI Structure Drills — candidates dictate a case structure. The system scores clarity, MECE logic (mutually exclusive, collectively exhaustive), and prioritization.

Technical Backbone

• Speech-to-Text + LLM: We combined speech recognition with an LLM evaluator that parsed logic step by step.
• Dataset annotation: We worked with consulting experts to label dozens of sample structures and math solutions, defining what "good" vs. "weak" looked like.
• Rubric-driven evaluation: Instead of open-ended judgments, the AI scored along clear dimensions (clarity, efficiency, logic).
• Evaluation & calibration: We ran side-by-side tests — experts graded candidate responses, AI graded the same responses, and we tuned prompts until alignment exceeded 85%.

Business Impact

The drills now serve 10,000+ candidates each month. Students get instant, coach-like feedback. Coaches save time by focusing on higher-level practice. And the client positioned itself as the only global player offering credible AI-driven prep tools.

Project C — Negotiation Training for Executives

The Use Case

Senior executives often attend negotiation workshops, but they rarely get to practice outside the classroom. Our client wanted to create an AI simulator where executives could "rehearse" negotiations against different counterpart personalities.

What We Built

• A real-time AI roleplay simulator with different persona types (tough, cooperative, ambiguous, anchored).
• A scoring engine that measured preparation, concession strategy, and framing.
• An admin dashboard for trainers to assign scenarios and track progress.

Technical Backbone

• LLM persona design: We built controlled personas with strict constraints so the AI didn't drift or go off-script mid-conversation.
• Memory management: The system summarized conversations to keep them coherent without bloating costs.
• Evaluation: We used expert rubrics to annotate negotiation transcripts and test whether the AI flagged the same strengths/weaknesses as human trainers.
• Compliance: Since executives were using this, we implemented filters for sensitive data, profanity, and personally identifying information.

Business Impact

We delivered the proof of concept in 3 months, on time and on budget. Feedback was overwhelmingly positive: executives reported that the simulator helped them practice strategies they otherwise only "knew in theory." For the institute, this opened a new scalable product line beyond traditional workshops.

Project D — Writing Feedback for Language Learners

The Use Case

Tens of thousands of students in Asia were writing essays for English exams. The client wanted fast, personalized feedback that was both accurate and cost-effective.

What We Built

• An AI engine that evaluated essays on grammar, clarity, coherence, and argument strength.
• Tailored feedback explaining why an essay scored a certain way and how to improve.
• A cost-optimized system that kept the per-essay evaluation affordable.

Technical Backbone

• Prompt audit: We reviewed their existing system, identified inefficiencies, and rebuilt prompts for clarity and structure.
• Dataset annotation: We created a labeled dataset of essays with error categories and expert scores.
• Evaluation: Benchmarked AI outputs against expert ratings to ensure consistency.
• Optimization: Introduced caching, tiered inference (routing simple tasks to cheaper models), and batch processing.

Business Impact

• 25% lower API costs projected.
• Higher accuracy in matching expert ratings.
• A framework for the client to continue iterating prompts without external support.

Lessons Across Projects

Looking back, a few lessons stand out:

• LLMs are powerful, but fragile. Without rubrics, datasets, and evaluation, outputs vary wildly. Annotation and structured prompts were the difference between "demo" and "production."
• Business outcomes drive adoption. Clients didn't care about model architecture. They cared about engagement, retention, and credibility.
• Speed matters. Delivering working prototypes in weeks built trust, won contracts, and allowed iteration with real users.
• Scalability is about balance. Accuracy, cost, latency, and user experience have to be optimized together.

Why This Matters

For me, these projects are more than technical achievements. They show that AI can:

• Lower the cost of quality education (test prep, interview coaching).
• Scale elite training methods (executive negotiation practice).
• Make feedback accessible to thousands of learners at once (language learning).

And from a founder's perspective, they prove that with the right mix of technical rigor and business execution, AI partnerships with top-tier organizations can deliver both impact and credibility.