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.
Background & Context
Publishers, educators, and moderation teams want a self-hosted signal for likely machine-generated text—ideally explainable features (perplexity, burstiness, lexical diversity) rather than an opaque SaaS score. That product direction is clear; the engineering work still has to land.
The ai-detector repository is an empty FastAPI scaffold. requirements.txt pins FastAPI, Uvicorn, Pydantic, Transformers, PyTorch, scikit-learn, NLTK, textstat, and orjson. Package markers exist at backend/app/ and backend/app/detector/, but __init__.py files are empty and there is no main.py, no /detect route, and no scoring pipeline. The README’s intended response contract (ai_score, label, feature breakdown) describes planned behavior only.
Tekvers documents this engagement honestly: it is project bootstrapping and stack intent for an AI detection API—not production ML evidence and not a live service clients can call today.
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.
Our 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.
What We Delivered
- 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 & Impact
- 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
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.
Technology Stack
Services Delivered
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