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Explainable AI & Decision Intelligence Layer for HR

Reasoning API

The Reasoning API serves a two-sided large reasoning model (LRM) that explains why the scores produced by the Matching API, Scoring API, and Grading API are high or low for a given profile or job. Powered by Hiring Superintelligence and trained on 1.2B hiring decisions and 400 million career paths, it surpasses human expert judgment, and it delivers audit-ready explanations, upskilling paths, and interview checkpoints to ensure fair, defensible hiring aligned with the EU AI Act.

Read the Docs
1.2B trainset
400M careerpaths
43+ languages
EU AI Act
GDPR-ready
99.99% uptime
output.json
{
  "explanation": "Strong match",
  "upskilling": ["Python"],
  "checkpoints": ["Leadership"],
  "eu_ai_act": "compliant"
}

Trusted by Customers, Partners & the AI Ecosystem

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API RESPONSE

Deterministic Spider Charts & Actionable Insights

Get fine-grained, structured JSON (Spider Charts) explaining every score. Extract differentiating strengths, coverage gaps, transferable skills, and uncertainty areas to power personalized rejection emails, internal mobility paths, and recruiter coaching.

Select Reasoning Type

Send an anchor profile + a query job → returns Spider Chart (JSON).

Explore

API GuideAPI EndpointTry Live Demo

Top Explainability Dimensions

Differentiating Strengths

Education, Expertise, Technical skills, Soft skills, Impact

Coverage Gaps

Tools, Industry knowledge

Uncertainty Areas

Evidence, Past context

Watch-outs

Career stability, False-negative risk

Items to Confirm in Interview

Motivation, Communication, Environment fit

Growth Potential

Career progression, Adaptability

Upskilling Opportunities

Analytical skills, Leadership

Transferable Skills

Collaboration, Autonomy

Evidence Level

Seniority, Responsibility scope, Work complexity

Overall Alignment with the Job

Professional experience, Execution, Job fit

response.json
 1{
 2  "code": 200,
 3  "data": {
 4    "spider_chart": {
 5      "skills_match": 0.91,
 6      "experience_fit": 0.84,
 7      "seniority_alignment": 0.78,
 8      "cultural_context": 0.72,
 9      "trajectory_signal": 0.88
10    },
11    "strengths": [
12      "Strong Python & ML track record",
13      "Relevant industry background",
14      "Progressive seniority trajectory"
15    ],
16    "coverage_gaps": [
17      "Missing cloud certification",
18      "Limited leadership experience"
19    ],
20    "transferable_skills": [
21      "Data modeling",
22      "Cross-functional collaboration"
23    ],
24    "upskilling_paths": [
25      "AWS Solutions Architect",
26      "Team lead experience"
27    ],
28    "interview_checkpoints": [
29      "Probe system design depth",
30      "Assess stakeholder communication"
31    ],
32    "eu_ai_act_compliant": true
33  }
34}
CUSTOMER STORIES

Don't take our word for it!

Trusted by fast-growing HR Tech and Global Enterprise

“

Audit-ready decision intelligence

vs. HiredScore
Before HrFlow.ai

HiredScore helped us understand criteria overlap, but it didn't give us a real reasoning layer for why a candidate should move forward, what was uncertain, or what still needed to be validated by a recruiter.

After HrFlow.ai

HrFlow.ai Reasoning gave us real-time, HR-native, audit-ready decision intelligence grounded in labor-market data. It turned opaque match outputs into structured strengths, gaps, uncertainty flags, interview checkpoints, and fairness-ready rationale.

“

Deterministic, legally defensible explanations

vs. Open-source LLMs
Before HrFlow.ai

Generic LLM outputs were too unstable, too generic, and too hard to govern for production hiring. JSON drift, hallucinated rationale, and inconsistent terminology made compliance-sensitive workflows risky.

After HrFlow.ai

HrFlow.ai Reasoning gave us deterministic, unified, HR-native explanations in real time, with labor-market-grounded reasoning for recruiter review, candidate transparency, and audit preparation.

“

Candidate-side reasoning that converts

vs. Phenom
Before HrFlow.ai

Phenom gave us criteria-based commentary, but it was still mostly recruiter-side and too limited when we wanted to explain to candidates why a job was worth pursuing.

After HrFlow.ai

HrFlow.ai Reasoning added a real candidate-side layer: why the role fit their background, what growth it could unlock, which skills would transfer, and what made the opportunity attractive. That clarity increased engagement and conversion.

“

Audit-ready decision intelligence

vs. HiredScore
Before HrFlow.ai

HiredScore helped us understand criteria overlap, but it didn't give us a real reasoning layer for why a candidate should move forward, what was uncertain, or what still needed to be validated by a recruiter.

After HrFlow.ai

HrFlow.ai Reasoning gave us real-time, HR-native, audit-ready decision intelligence grounded in labor-market data. It turned opaque match outputs into structured strengths, gaps, uncertainty flags, interview checkpoints, and fairness-ready rationale.

“

Deterministic, legally defensible explanations

vs. Open-source LLMs
Before HrFlow.ai

Generic LLM outputs were too unstable, too generic, and too hard to govern for production hiring. JSON drift, hallucinated rationale, and inconsistent terminology made compliance-sensitive workflows risky.

After HrFlow.ai

HrFlow.ai Reasoning gave us deterministic, unified, HR-native explanations in real time, with labor-market-grounded reasoning for recruiter review, candidate transparency, and audit preparation.

“

Candidate-side reasoning that converts

vs. Phenom
Before HrFlow.ai

Phenom gave us criteria-based commentary, but it was still mostly recruiter-side and too limited when we wanted to explain to candidates why a job was worth pursuing.

After HrFlow.ai

HrFlow.ai Reasoning added a real candidate-side layer: why the role fit their background, what growth it could unlock, which skills would transfer, and what made the opportunity attractive. That clarity increased engagement and conversion.

“

Audit-ready decision intelligence

vs. HiredScore
Before HrFlow.ai

HiredScore helped us understand criteria overlap, but it didn't give us a real reasoning layer for why a candidate should move forward, what was uncertain, or what still needed to be validated by a recruiter.

After HrFlow.ai

HrFlow.ai Reasoning gave us real-time, HR-native, audit-ready decision intelligence grounded in labor-market data. It turned opaque match outputs into structured strengths, gaps, uncertainty flags, interview checkpoints, and fairness-ready rationale.

“

Deterministic, legally defensible explanations

vs. Open-source LLMs
Before HrFlow.ai

Generic LLM outputs were too unstable, too generic, and too hard to govern for production hiring. JSON drift, hallucinated rationale, and inconsistent terminology made compliance-sensitive workflows risky.

After HrFlow.ai

HrFlow.ai Reasoning gave us deterministic, unified, HR-native explanations in real time, with labor-market-grounded reasoning for recruiter review, candidate transparency, and audit preparation.

“

Candidate-side reasoning that converts

vs. Phenom
Before HrFlow.ai

Phenom gave us criteria-based commentary, but it was still mostly recruiter-side and too limited when we wanted to explain to candidates why a job was worth pursuing.

After HrFlow.ai

HrFlow.ai Reasoning added a real candidate-side layer: why the role fit their background, what growth it could unlock, which skills would transfer, and what made the opportunity attractive. That clarity increased engagement and conversion.

“

Audit-ready decision intelligence

vs. HiredScore
Before HrFlow.ai

HiredScore helped us understand criteria overlap, but it didn't give us a real reasoning layer for why a candidate should move forward, what was uncertain, or what still needed to be validated by a recruiter.

After HrFlow.ai

HrFlow.ai Reasoning gave us real-time, HR-native, audit-ready decision intelligence grounded in labor-market data. It turned opaque match outputs into structured strengths, gaps, uncertainty flags, interview checkpoints, and fairness-ready rationale.

“

Deterministic, legally defensible explanations

vs. Open-source LLMs
Before HrFlow.ai

Generic LLM outputs were too unstable, too generic, and too hard to govern for production hiring. JSON drift, hallucinated rationale, and inconsistent terminology made compliance-sensitive workflows risky.

After HrFlow.ai

HrFlow.ai Reasoning gave us deterministic, unified, HR-native explanations in real time, with labor-market-grounded reasoning for recruiter review, candidate transparency, and audit preparation.

“

Candidate-side reasoning that converts

vs. Phenom
Before HrFlow.ai

Phenom gave us criteria-based commentary, but it was still mostly recruiter-side and too limited when we wanted to explain to candidates why a job was worth pursuing.

After HrFlow.ai

HrFlow.ai Reasoning added a real candidate-side layer: why the role fit their background, what growth it could unlock, which skills would transfer, and what made the opportunity attractive. That clarity increased engagement and conversion.

“

Flexible mobility without new tests

vs. TestGorilla
Before HrFlow.ai

TestGorilla was useful for defined workflows, but we needed to evaluate existing profiles instantly for new roles, support internal mobility, and re-engage talent pools without requiring new tests each time.

After HrFlow.ai

HrFlow.ai Reasoning generates real-time explanations of fit, gaps, mobility potential, and watch-outs directly from the profile and job. A more flexible decision layer for redeployment, internal mobility, and talent-pool reuse.

“

Beyond feature importance

vs. XAI Methods
Before HrFlow.ai

We built our own explanation layer with SHAP and LIME, but it never became truly usable for hiring. It explained features, not decisions. It lacked labor-market context, didn't generalize across job families, and wasn't built for multilingual hiring.

After HrFlow.ai

HrFlow.ai Reasoning gave us what we were missing: real-time, recruiter-ready, candidate-ready reasoning grounded in actual hiring data.

“

Real reasoning, not keyword highlights

vs. ElasticSearch
Before HrFlow.ai

We built an explanation layer with Elasticsearch highlights and boosting rules, but it was still just keyword overlap dressed up as reasoning. It missed labor-market context, transferable skills, and uncertainty.

After HrFlow.ai

HrFlow.ai Reasoning replaced that patchwork with a real-time API that explained why a fit was strong or weak, what was missing, and what needed human verification before making a decision.

“

Flexible mobility without new tests

vs. TestGorilla
Before HrFlow.ai

TestGorilla was useful for defined workflows, but we needed to evaluate existing profiles instantly for new roles, support internal mobility, and re-engage talent pools without requiring new tests each time.

After HrFlow.ai

HrFlow.ai Reasoning generates real-time explanations of fit, gaps, mobility potential, and watch-outs directly from the profile and job. A more flexible decision layer for redeployment, internal mobility, and talent-pool reuse.

“

Beyond feature importance

vs. XAI Methods
Before HrFlow.ai

We built our own explanation layer with SHAP and LIME, but it never became truly usable for hiring. It explained features, not decisions. It lacked labor-market context, didn't generalize across job families, and wasn't built for multilingual hiring.

After HrFlow.ai

HrFlow.ai Reasoning gave us what we were missing: real-time, recruiter-ready, candidate-ready reasoning grounded in actual hiring data.

“

Real reasoning, not keyword highlights

vs. ElasticSearch
Before HrFlow.ai

We built an explanation layer with Elasticsearch highlights and boosting rules, but it was still just keyword overlap dressed up as reasoning. It missed labor-market context, transferable skills, and uncertainty.

After HrFlow.ai

HrFlow.ai Reasoning replaced that patchwork with a real-time API that explained why a fit was strong or weak, what was missing, and what needed human verification before making a decision.

“

Flexible mobility without new tests

vs. TestGorilla
Before HrFlow.ai

TestGorilla was useful for defined workflows, but we needed to evaluate existing profiles instantly for new roles, support internal mobility, and re-engage talent pools without requiring new tests each time.

After HrFlow.ai

HrFlow.ai Reasoning generates real-time explanations of fit, gaps, mobility potential, and watch-outs directly from the profile and job. A more flexible decision layer for redeployment, internal mobility, and talent-pool reuse.

“

Beyond feature importance

vs. XAI Methods
Before HrFlow.ai

We built our own explanation layer with SHAP and LIME, but it never became truly usable for hiring. It explained features, not decisions. It lacked labor-market context, didn't generalize across job families, and wasn't built for multilingual hiring.

After HrFlow.ai

HrFlow.ai Reasoning gave us what we were missing: real-time, recruiter-ready, candidate-ready reasoning grounded in actual hiring data.

“

Real reasoning, not keyword highlights

vs. ElasticSearch
Before HrFlow.ai

We built an explanation layer with Elasticsearch highlights and boosting rules, but it was still just keyword overlap dressed up as reasoning. It missed labor-market context, transferable skills, and uncertainty.

After HrFlow.ai

HrFlow.ai Reasoning replaced that patchwork with a real-time API that explained why a fit was strong or weak, what was missing, and what needed human verification before making a decision.

“

Flexible mobility without new tests

vs. TestGorilla
Before HrFlow.ai

TestGorilla was useful for defined workflows, but we needed to evaluate existing profiles instantly for new roles, support internal mobility, and re-engage talent pools without requiring new tests each time.

After HrFlow.ai

HrFlow.ai Reasoning generates real-time explanations of fit, gaps, mobility potential, and watch-outs directly from the profile and job. A more flexible decision layer for redeployment, internal mobility, and talent-pool reuse.

“

Beyond feature importance

vs. XAI Methods
Before HrFlow.ai

We built our own explanation layer with SHAP and LIME, but it never became truly usable for hiring. It explained features, not decisions. It lacked labor-market context, didn't generalize across job families, and wasn't built for multilingual hiring.

After HrFlow.ai

HrFlow.ai Reasoning gave us what we were missing: real-time, recruiter-ready, candidate-ready reasoning grounded in actual hiring data.

“

Real reasoning, not keyword highlights

vs. ElasticSearch
Before HrFlow.ai

We built an explanation layer with Elasticsearch highlights and boosting rules, but it was still just keyword overlap dressed up as reasoning. It missed labor-market context, transferable skills, and uncertainty.

After HrFlow.ai

HrFlow.ai Reasoning replaced that patchwork with a real-time API that explained why a fit was strong or weak, what was missing, and what needed human verification before making a decision.

🔗 INTEGRATIONS

Works with the tools you use

Integrate 200+ tools with the flip of a switch.

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HrFlow.ai Data Studio

HrFlow.ai Data Studio

HR-native ETL with 200+ connectors plus Webhooks to ingest, normalise, and sync jobs & profiles across your stack—reliable pipelines with unified schemas.

Zapier ETL

Zapier ETL

No-code automation platform with 8,000+ app integrations to move data between tools using triggers + actions.

Make.com ETL

Make.com ETL

Visual automation platform to extract/transform/route data across 3,000+ apps (plus HTTP modules for any API).

Microsoft Flow ETL

Microsoft Flow ETL

Microsoft Power Automate—workflow automation with 1,000+ API connectors (and support for custom connectors).

Workato ETL

Workato ETL

Enterprise iPaaS/automation platform with 1,200+ pre-built connectors for orchestrating integrations and data workflows at scale.

Salesforce Flow Automation

Salesforce Flow Automation

Salesforce's low-code workflow automation tool; extended via AppExchange with 7,000+ apps to add integrations and capabilities.

🚀 KEY FEATURES

The Definitive Decision-Intelligence and Compliance Layer for Legally Defensible Hiring

HrFlow.ai Reasoning is an API that turns Matching, Scoring, and Grading outputs into structured hiring intelligence. Grounded in labor-market data and trained on 1.2B hiring and interview outcomes shaped by expert recruiter signals, it explains fit, gaps, uncertainty, interview checkpoints, and upskilling or mobility opportunities. With reasoning in 43+ languages, recruiter and candidate coaching, and audit-ready fairness, it serves as a decision-intelligence and compliance layer that helps teams make hiring decisions that are more transparent, reviewable, and legally defensible across GDPR/UK GDPR, EEOC, NYC AEDT, and EU AI Act contexts.

Tutorial Video3:45

🔒 ENTERPRISE-READY

Trust & Security

Built for sensitive HR data—secure by default, enterprise-ready.

01

Encryption

TLS in transit + encryption at rest to protect documents and extracted data.

02

Retention control

Minimal storage by default, with configurable retention policies to match your compliance needs.

03

AI-Act / GDPR / DPA ready

Built for sensitive HR data—secure by default, enterprise-ready. AI Act– and GDPR-ready processing, with documented controls for data handling and compliance.

04

Location / Region

Data processing and storage can be aligned with your required region (e.g., EU or US) depending on your deployment.

Visit Trust Center→
📊 FEATURE COMPARISON

HrFlow.ai Reasoning is the only real-time, HR-native, audit-ready decision-intelligence API for People & Jobs

Feature
HrFlow.ai Reasoning
HrFlow.ai Reasoning
HiredScore
HiredScore
OpenAI LLM
OpenAI LLM
TestGorilla
TestGorilla
Phenom
Phenom
XA
XAI Methods
GB
Gradient-boosting
ElasticSearch
ElasticSearch
Deployment & Trust
Headquarters
🇫🇷 France
🇺🇸 USA
🇺🇸 USA
🇳🇱 Netherlands
🇺🇸 USA
🇺🇸 USA
🇺🇸 USA
🇺🇸 USA
🇺🇸 USA & 🇪🇺 EU Servers
Built-in
config
config
Built-in
config
config
config
config
GDPR / AI-Act readiness
By design
By design
config
HR Compliance (Safety & Guardrails)
Built-in
(Class Action)
Built-in
Limited
Pretraining Data
1,2B Hiring Signals (Top Hiring firms)
Noisy Corporate data (Hiring Signals)
Noisy & Biased Web Data
No outcome-based training
Noisy Corporate data (Applicants' Signals)
Requires trainset
Requires trainset
No outcome-based training
HR-Focused
Input Security (Prompt injection)
Unified output object (JSON drift)
Deterministic output values (hallucination)
(Mood influence)
Pricing model
per request
per subscription (bundle)
per input+output tokens (expensive)
per subscription (bundle)
per subscription (bundle)
per Server
per Server
per Server
Real-time API
Built-in (~2s/request)
Not supported
built-in (~30s/request)
Requires candidates
Requires candidates
Built-in (~100ms/request)
Built-in (~100ms/request)
Built-in (~1s/request)
UI Access
Built-in
Built-in
Built-in
Built-in
Core Technology
Technology
Large Reasoning Model / Fairness & Bias Optimization
LLM / Custom ElasticSearch / XAI Methods
LLM
skills tests, cognitive tests, personality/psychometric tests
LLM / Custom ElasticSearch / LightCast
SHAP / LIME / saliency methods
XGBoost & LightGBM feature importance
Apache Lucene Highlighters / Fast Vector Highlighter
Multilingual
43 lang
5 lang
40 lang
12 lang
8 lang
Config
Crosslingual
Score Explainability
Concepts Overlap
Criteria Overlap
Concepts Overlap
Criteria Overlap
Criteria Overlap
Features Importance
Features Importance
Keywords Overlap
White-collar Roles Accuracy
High
Medium
Good
Medium
Medium
Low
Low
Low
Blue-collar Roles Accuracy
High
Low
Medium
Low
Low
Low
Low
Low
Junior Roles Accuracy
High
Low
Low
Low
Low
Low
Low
Low
Senior Roles Accuracy
High
Medium
Good
Medium
Medium
Low
Low
Low
Candidate Strengths and Gaps
Built-in (Outcome-based)
Limited (criteria-based)
Limited (generic)
Limited (criteria-based)
Limited (criteria-based)
Requires trainset
Requires trainset
Limited (criteria-based)
Job Attractiveness Reasoning
Built-in (Outcome-based)
Limited (generic)
Limited (criteria-based)
Requires trainset
Requires trainset
Limited (criteria-based)
Recruiter AI Trainer
Built-in (Outcome-based)
Interview Briefing & Fact-Checking
Built-in (Outcome-based)
Limited (generic)
Upskilling & Mobility Analysis
Built-in (Outcome-based)
Limited (criteria-based)
Limited (generic)
Limited (criteria-based)
Limited (criteria-based)
Custom Feature Engineering
Built-in (Tags & Metadata)
Built-in (Custom questions)
Concept boosting
Built-in (Custom questions)
Built-in (Custom criteria)
Built-in (Custom criteria)
Keyword boosting
Fairness Regularization
Built-in (Constraints)
Requires trainset
Requires trainset
Data Calibration & Debiasing
Built-in (Pipeline)
Requires trainset
Requires trainset
HR Stack integrations (add-ons)
Resume, CV, Job parsers
Built-in (Parsing API)
Third party
Config
Third party
Third party
HR data enrichment & taxonomies
Built-in (Linking/Tagging/Asking APIs)
Third party
Third party
Browser Extension
Connector (Data Studio)
Jobboards / ATS / HCM / HRIS connectors
200+ connectors (Data Studio)
Built-in
Built-in
Built-in
Candidate & Recruiter UI
Widgets (App Studio)
Built-in
Built-in
Built-in
❓ COMMON QUESTIONS

Frequently Asked Questions

Everything you need to know about the Reasoning API

🧩 COMPLETE API SUITE

Go beyond the Reasoning API

Our APIs are designed to complement each other and unlock your data's full potential

Full Extraction API Suite

Transform HR documents into structured, enriched Talent & Workforce Data — powering every layer of Hiring Intelligence.

API Overview
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Ranking API Suite

Unlock Hiring Superintelligence at scale — with transparent, fair, and explainable ranking across every Talent signal.

API Overview

HrFlow.ai is an API-first company and the leading AI-powered HR data automation platform.

The company helps +1000 customers (HR software vendors, Staffing agencies, large employers, and headhunting firms) to thrive in a high-volume and high-frequency labor market.

The platform provides a complete and fully integrated suite of HR data processing products based on the analysis of hundreds of millions of career paths worldwide -- such as Parsing API, Tagging API, Embedding API, Searching API, Scoring API, and Upskilling API. It also offers a catalog of +200 connectors to build custom scenarios that can automate any business logic.

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