AI-Powered Educational Tool | Dental Crown Preparation Evaluation

Use a labeled dataset that mirrors real student submissions, test across varied lighting and resolution conditions, track rubric adherence, break down error categories, and compare cost per evaluation across model providers.

Apply hybrid routing using cheaper models first, batch requests, cache results, compress images where allowed, and regularly audit model behavior to eliminate unnecessary expensive calls.

Expect benchmark datasets, cross-provider comparisons, image-quality variance testing, regression testing after model updates, and documented accuracy thresholds.

Secure full ownership of prompts, rubric logic, evaluation workflows, architecture design, and all data pipelines to ensure long-term independence from the agency.

Look for real model comparison logs, accuracy audit reports, prompt-testing tools, vision pipeline artifacts, and screenshots of feedback overlays instead of generic marketing slides.

Start with an agency for speed and breadth of expertise. Move in-house once your architecture is stable and you need full-time ownership and iteration.

Use an existing platform if your rubrics are standard and don’t require visual overlays. Build custom if you need configurable scoring, specialized image logic, or multi-model evaluation pipelines.

Plan for API usage, storage for images and evaluation logs, compute for model testing, and ongoing re-evaluation whenever model providers push updates that may change output behavior.

Expect $150K–$400K due to precision requirements, specialized domain logic, advanced rubric structures, and potential custom model tuning.

Yes. A 3–4 week pilot costing $10K–$25K lets you validate model accuracy, rubric performance, image variability handling, and the team's ability to deliver consistent evaluation.

Ask them to walk through their prompt-testing methodology, accuracy measurement process, error classification framework, and approach to switching or comparing models. Real AI engineers can explain these clearly.

A strong team delivers an MVP in 10–16 weeks, covering model trials, rubric and prompt design, image-processing pipelines, multi-provider orchestration, and a basic instructor dashboard.

Ask for architecture diagrams, model comparison data, annotated output samples, and reproducibility testing workflows. Teams relying only on plug-and-play APIs won’t have this depth of evidence.

Agencies typically run $20K–$60K per month with immediate senior talent, while an in-house team often exceeds $35K per month for just two AI engineers—before adding recruiting time, benefits, and management overhead.

Most MVPs range from $80,000 to $250,000, driven by the complexity of image analysis, rubric flexibility, and whether your system needs multi-model comparison or visual feedback overlays.