How do we ensure GDPR compliance when AI screens candidates?

Under GDPR Article 22, automated decisions producing legal or similarly significant effects require human intervention. For candidate screening, this means the AI scores and ranks, but a human reviewer must approve or reject before any adverse action. Forfis implements this as a mandatory approval gate in the workflow: the model outputs a score and rationale, the system flags it for review, and no offer or rejection is sent until a human clicks approve. The audit log records who reviewed, when, and what they changed, satisfying Article 30 accountability requirements.

What is pgvector and why use it for candidate screening?

pgvector is a PostgreSQL extension that stores and queries vector embeddings natively. For candidate screening, you embed job descriptions and candidate profiles into 768- or 1536-dimensional vectors (depending on the embedding model), store them in a pgvector table, and use cosine similarity search to rank candidates against role requirements. This replaces brittle keyword matching with semantic matching, so a candidate who wrote "managed a $2M budget" matches a job posting requiring "financial oversight" even without identical terms. It runs inside your existing Postgres instance, so no new infrastructure is required.

What does a 6-month timeline look like for this rollout?

A 6-month timeline is realistic for a 201-500 person B2B SaaS company running isolated pilots. Months 1-2: process audit and baseline measurement. Months 3-4: pilot build with one screening workflow, including pgvector setup, REST API integration, and human-in-the-loop approval gates. Months 5-6: pilot validation against baseline metrics, then scoped rollout to additional roles or departments. The dedicated AI team handles all technical work; your team provides domain input, approval workflows, and compliance sign-off. Delays typically come from slow access to historical data or unclear approval authority, not from the AI build itself.

What does a dedicated AI team deliver versus a fractional consultant?

A dedicated AI team owns the full delivery: technical planning, model selection, pipeline build, integration, and managed operation. For a 201-500 person company, this means 2-4 engineers and a product lead embedded in your workflow for the engagement duration. The team handles the pgvector schema design, embedding model selection, REST API endpoints, webhook handlers, and the human-in-the-loop approval interface. Your internal team provides access to ATS data, defines scoring criteria, and staffs the approval queue. The dedicated model avoids the context-switching cost of a shared resource and keeps institutional knowledge on the specific screening logic.

How does predictive scoring work in candidate screening?

Predictive scoring in candidate screening means the model assigns a numerical probability that a candidate will succeed in the role, based on features extracted from their application: skills match, experience depth, education, and semantic similarity to successful past hires. The score is not a pass/fail binary; it ranks candidates and flags those in the top decile for fast-track review. The model is trained on your historical hiring data (who you hired, who performed well, who left within 12 months). GDPR requires that the scoring criteria be documented, that candidates can request an explanation, and that the score alone cannot trigger rejection without human review.

How do the custom REST API and webhooks integrate with our ATS?

The REST API exposes endpoints for submitting candidate data, retrieving screening scores, and triggering approval workflows. Webhooks push events back to your ATS or HR system when a candidate is scored, when a human approves or rejects, or when the pipeline status changes. For example, POST /api/v1/candidates/screen accepts a JSON payload with the candidate's resume text and the job description ID; the system embeds both, runs the scoring model, and returns a score with a confidence interval. A webhook to your ATS fires when the score exceeds a threshold, creating a task for the hiring manager. All payloads are logged for GDPR audit trails.

How do we handle multilingual candidate applications in the screening pipeline?

Multilingual support requires the embedding model to handle multiple languages natively. Models like multilingual-e5 or BGE-M3 produce consistent vector spaces across English, Spanish, French, German, and other languages, so a Spanish-language resume can be matched against an English job description. The extraction pipeline must also handle multilingual document formats: PDFs with mixed-language content, resumes with non-Latin scripts, and cover letters in the candidate's native language. For a USA-based B2B SaaS company hiring internationally, this means the screening pipeline processes applications in 5-10 languages without degrading accuracy, and the human reviewer sees the original text alongside the extracted structured data.

What are the most common pitfalls in a compliance-safe AI screening rollout?

The most common failure is skipping the baseline measurement. Without a documented before/after on cycle time and error rate, you cannot prove the AI is performing better than the manual process, and you cannot detect degradation. The second pitfall is treating the AI score as final: if the model's output goes directly to rejection without human review, you violate GDPR Article 22 and create legal exposure. The third is under-scoping the pilot: trying to automate all screening workflows at once instead of proving value on one role family first. Each of these is detectable: missing baseline docs, absence of an approval gate in the workflow, or a pilot scope document listing more than two workflows.

GDPR-Compliant AI Candidate Screening for B2B SaaS: A 6-Month Rollout Plan

The Problem: Manual Candidate Screening at Scale

A 201-500 person B2B SaaS company in the USA runs candidate screening as a manual, multilingual back-office function: recruiters read resumes, score them against job descriptions, and flag top candidates for interview. The process is slow (median 14 days from application to first review), inconsistent across hiring managers, and non-compliant with GDPR Article 22 if any automated decision triggers rejection without human oversight. The company is at the “Running Isolated Pilots” stage of AI maturity: it has tested a chatbot for customer support but has not yet automated a core HR workflow. The goal is a compliance-safe AI rollout that replaces manual screening with predictive scoring, uses pgvector embeddings for semantic matching, integrates via custom REST API and webhooks into the existing ATS, and supports multilingual applications across 5-10 languages. The delivery model is a dedicated AI team working over 6 months, with human-in-the-loop approval on every screening decision.

Prerequisites Before You Start

Before step 1, confirm the following are in place:

Access to historical hiring data: at least 12 months of application records, including resume text, job description, hiring outcome (hired/not hired), and 12-month retention status. This is the training set for the predictive scoring model.
ATS API credentials: your applicant tracking system (Greenhouse, Lever, Workable, or equivalent) must expose a REST API with read/write access to candidate records and job postings. Document the endpoint URLs, authentication method (API key or OAuth 2.0), and rate limits.
Legal sign-off on GDPR compliance: your DPO or outside counsel must confirm that the screening workflow will include a mandatory human approval gate, that data subjects can request an explanation of the scoring criteria, and that all processing is logged under Article 30.
A named human reviewer for each role family: the person who will approve or reject AI-scored candidates. This is not optional under GDPR Article 22.
A Postgres 15+ instance with the pgvector extension installed, or a managed Postgres service (RDS, Cloud SQL, Supabase) that supports pgvector. The embeddings table will live here.
A dedicated AI team with at least 2 engineers and 1 product lead, engaged for the full 6-month timeline.

Step 1: Audit the Current Screening Workflow

Run a 2-week process audit on your current screening workflow. Map every step from application receipt to first interview scheduling: who touches the resume, how long each step takes, where candidates drop off, and which languages appear in the application pool. Export 200 recent applications across 3 role families (e.g., engineering, sales, customer success) and manually score them using your existing rubric. Record the median cycle time (target baseline: under 14 days), the error rate (how often a manually scored candidate was later found to be a poor fit), and the language distribution. This baseline is your before/after measurement. Without it, you cannot prove the AI outperforms the manual process, and you cannot detect degradation after rollout. The audit also identifies which role families have enough historical data to train a reliable scoring model and which do not.

Step 2: Scope the Pilot on One Role Family

Select one role family for the pilot. The criteria: at least 50 historical hires with 12-month retention data, a clear scoring rubric that hiring managers already use, and a multilingual application volume that justifies the embedding pipeline. For a B2B SaaS company, “Senior Software Engineer” or “Account Executive” are typical first pilots because they have high application volume and well-defined skill requirements. Define the pilot scope in a one-page document: the role family, the ATS endpoints you will use, the scoring criteria (skills match, experience depth, education, semantic similarity to past successful hires), the human reviewer’s name, and the success metrics (target: reduce cycle time from 14 days to under 5 days, reduce error rate by 30%). The pilot ships with a measured before/after baseline on both metrics. Do not expand the scope during the pilot; adding a second role family or a new scoring criterion mid-pilot invalidates the baseline comparison.

Step 3: Build the Document Extraction and pgvector Pipeline

Build the extraction and embedding pipeline. Ingest resumes and job descriptions from the ATS via its REST API. Parse the document text (PDF, DOCX, plain text) using a library like pdfplumber or unstructured to extract structured fields: name, email, skills, work history, education. Store the raw text and extracted fields in Postgres. Embed both the candidate profile and the job description using a multilingual embedding model (e.g., multilingual-e5-large-instruct or BGE-M3) into 1024-dimensional vectors. Store the vectors in a pgvector table: CREATE TABLE candidate_embeddings (id UUID PRIMARY KEY, candidate_id UUID, job_id UUID, embedding vector(1024), created_at TIMESTAMP). Use cosine similarity search to rank candidates: SELECT candidate_id, 1 - (embedding <=> $1) AS similarity FROM candidate_embeddings WHERE job_id = $2 ORDER BY similarity DESC LIMIT 50. This replaces keyword matching with semantic matching, so “managed a $2M budget” matches “financial oversight” without identical terms.

Step 4: Train the Predictive Scoring Model

Train the predictive scoring model on your historical hiring data. The features: skills match score (from the extraction pipeline), experience depth (years in relevant roles), education level, semantic similarity to past successful hires (from the pgvector search), and application completeness. The target variable: 12-month retention (1 if the candidate was still employed after 12 months, 0 otherwise). Use a gradient-boosted classifier (XGBoost or LightGBM) for interpretability; the model outputs a probability score between 0 and 1. Calibrate the score so that the top decile corresponds to candidates with a 70%+ probability of 12-month retention. Document the scoring criteria in a one-page summary that you can share with candidates under GDPR Article 13 (right to information about automated decision-making). The model is retrained quarterly as new hiring data accumulates. Store the model version, training data hash, and feature weights in a metadata table for audit purposes.

Step 5: Integrate via REST API and Webhooks

Build the REST API and webhook integration. Expose three endpoints: POST /api/v1/candidates/screen (accepts candidate ID and job ID, returns score and rationale), GET /api/v1/candidates/{id}/score (retrieves the score and feature breakdown), and POST /api/v1/candidates/{id}/approve (human reviewer approves or rejects, with a comment field). The approval endpoint is the GDPR Article 22 gate: no rejection is sent to the candidate until a human clicks approve. Webhooks push events to your ATS: candidate.scored (when the model outputs a score), candidate.approved (when a human approves), candidate.rejected (when a human rejects). All payloads are logged with timestamps, user IDs, and IP addresses for the Article 30 audit trail. The API is deployed on your existing infrastructure (AWS, GCP, or on-prem) behind your existing authentication layer. Rate limits: 100 requests/minute per API key. Error responses follow RFC 7807 (Problem Details for HTTP APIs).

GDPR-Compliant AI Candidate Screening for B2B SaaS: A 6-Month Rollout Plan

The Problem: Manual Candidate Screening at Scale

Prerequisites Before You Start

Step 1: Audit the Current Screening Workflow

Step 2: Scope the Pilot on One Role Family

Step 3: Build the Document Extraction and pgvector Pipeline

Step 4: Train the Predictive Scoring Model

Step 5: Integrate via REST API and Webhooks

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