CandiSift vs Counting AI
CandiSift
github.comCounting AI
counting.aiSide by side
| Figure | CandiSift | Counting AI |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | — |
| Plans published | 1 | 0 |
| Spec score · Candidate Screening Software | 8.0 (#6) | 6.9 (#33) |
| Product | CandiSift is an open-source applicant tracking system for AI resume screening, CV parsing, job description and candidate matching, and bias-audited ranking. | Counting AI provides custom AI and data engineering services for job boards, staffing and recruiting firms, recruiting software companies, and other businesses. |
| Security | The repository describes HTTP Basic authentication, security headers, rate limiting, request body limits, upload validation, and prompt-injection defenses. | The privacy policy lists encryption with the client’s keys, private networking, single sign-on, role-based access, and audit logs as access controls. |
Plans
In their own words
CandiSift
CandiSift is an open-source applicant tracking system for parsing resumes, matching candidates to job descriptions, and ranking applicants through a bias-audit workflow. Recruiters can create roles, upload PDF, DOCX, or plain-text resumes in batches, and review ranked results with accept or reject decisions. The screening sequence strips personally identifiable information, applies hard filters, ranks candidates, then sends those who remain to persona agents and human review. Token-cosine ranking, technical and risk personas, and synthesis contribute to that process. Before confirming a staged batch, users can view estimated processing costs; deterministic filters run before the paid LLM stage. CandiSift connects to Anthropic Claude through Agno persona agents, with an offline stub LLM available when there is no API key. It can run in Docker, uses SQLite by default, and supports a Postgres database URL. The project describes HTTP Basic authentication, security headers, rate limiting, upload validation, request limits, prompt-injection defenses, and an append-only audit log. The API includes a bias-audit endpoint. The project has no shipped accuracy benchmark; OCR, embeddings/FAISS, and skillNER are described as swaps rather than included defaults. The software fee is $0.00 per free; LLM processing may incur per-token costs.
Counting AI
Counting AI has no write-up on Specifiction yet; its figures are above.