Machine learning API scaffold
AI Content Detection API
FastAPI/ML dependency scaffold for a future explainable AI-text detection API—baseline only, not a shipped detector.
The challenge
Teams evaluating AI-text detection need a transparent, self-hostable API direction—but shipping a credible detector requires implemented models and HTTP surfaces, not dependency pins alone. Without a coherent machine learning api scaffold foundation, AI Content Detection API stakeholders faced fragmented tools, slow handoffs, and limited visibility—classic failure modes Tekvers designs against.
The solution
AI Content Detection API: FastAPI/ML dependency scaffold for a future explainable AI-text detection API—baseline only, not a shipped detector. Shared understanding of target stack and API shape for an explainable detector. Tekvers delivered a maintainable machine learning api scaffold system for AI Content Detection API using Python, FastAPI, Transformers, PyTorch, with phased rollout, operator workflows, and documentation suited to long-term ownership.
Approach
Stack baseline
Pinned FastAPI/Uvicorn for serving and Transformers/PyTorch plus stylistic libraries (NLTK, textstat) for the intended linguistic + model signal stack.
Package skeleton
Created `backend.app` and `backend.app.detector` package markers so detector modules have a clear home when implementation starts.
Contract-first documentation
Captured the intended `POST /detect` JSON shape (score, label, feature contributions) in project docs so product and ML work can align before code lands—without claiming the endpoint exists.
Deliverables
- ▸Python project scaffold with pinned API and ML dependencies
- ▸Empty `backend/app` and `backend/app/detector` package layout
- ▸Documented intended detection contract (README / portfolio docs)
- ▸Honest portfolio framing distinguishing planned vs implemented scope
- ▸Clear next-step path: FastAPI app entry, detector modules, tests, deploy config
Outcomes
- ▸Shared understanding of target stack and API shape for an explainable detector
- ▸Repository ready for detector implementation without inventing a fake product story
- ▸Portfolio transparency: scaffold maturity called out explicitly
Impact
Stakeholders get a truthful baseline for an AI content detection API—dependency and package structure in place, ambitions documented—so subsequent ML engineering starts from a clean scaffold rather than a misleading “shipped detector” claim.
Tech stack
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