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BRAIN HR
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ENAM
AMAmélie M.amelie.m@agency-example.com

CockpitTuesday 12 August

Hello Amélie

3 priorities today · 1 live AI interview · Norvia client review at 16:00

Active mandates3+1 this week
Relevant profiles94out of 3,248 CVs
AI interviews41 live now
Dossiers to review2before 16:00

Today’s priorities

3 to do
  • Validate the “Model deployment” evidenceThomas Renaud · Norvia14:30AM
  • Review the decision dossierThomas Renaud · score 79 · 1 point to confirm15:00AM
  • Confirm Nadia Berrada’s slotRecruiter interview · tomorrow 10:3017:00AM
  • Approve the mandate criteriaProduct Manager — Helix Assurance09:12AM

Active mandates

3
NSenior data scientistNorvia AnalyticsInterviews3,248943Client reviewToday 16:00AM
HProduct ManagerHelix AssuranceMatching1,86241—ShortlistThursdayKB
KEngineering ManagerKea SystemsClient dossier2,417574Dossier sentYesterdaySL

Recent activity

  • NAI interview completed · Nadia BerradaSenior data scientist — Norvia
  • N3 profiles flagged for recruiter reviewSenior data scientist — Norvia
  • KClient dossier sentEngineering Manager — Kea Systems
  • HMandate criteria approvedProduct Manager — Helix Assurance
Live
TRThomas RenaudSenior data scientist · Norvia

Topic 2 of 3 · Model deployment

Evidence captured · Model deployment

Today

Tuesday 12 August
  1. AI interview · Thomas RenaudNorvia · in progress
  2. Client review · Norvia Analytics3 profiles to present
  3. Team check-inActive mandates
  4. Tomorrow
  5. Recruiter interview · Nadia BerradaNorvia
  6. Client check-in · Helix AssuranceProduct Manager shortlist

Norvia AnalyticsSenior data scientistMatching

Matching results

Run #1 · 3,241 profiles analysed · 6 criteria · completed at 12:04

Shortlist

3 of 94 relevant

JKto move through the shortlist

Criteria coverage

evidenced among the 94 relevant profiles
  • Python94
  • SQL88
  • Forecasting models71
  • Product-team collaboration63
  • Model deployment38
  • Technical mentoring12
TR

Thomas Renaud

Senior data scientist · 6 years of experienceFinal score791/3
How the score is calculatedFictional example · review aid
82Base scorecriteria evidenced in the CV1.02Preference bonusproduct-team experience0.95Quality coefficientdeployment ownership to clarify
Scoring commentary

Relevant Python and SQL experience . Deployment ownership still needs clarifying in the interview .

E1 · CV · page 2Evidence found
Forecasting models in Python
“Developed forecasting models in Python and SQL pipelines for a product team.”
Vigilance signal

8-month career gap · Jul 2024 — Feb 2025. A 4-month experience overlap also needs clarifying.

Norvia AnalyticsSenior data scientistAI interviews

AI interview · Thomas Renaud

A live video interview led by BRAIN HR. Keep the recording and transcript to review every piece of evidence.

LiveInterview completed · 12:00

AI interview plan

06:02 / 12:0012:00 / 12:00
  1. Python & SQLCovered · 2 questions
  2. Model deploymentFrom open point E2 · CV p.3
  3. Production monitoringPerformance and drift after release
Follow-up suggested by the agent

“Who set the thresholds for the drift alerts?”

Added to topic 3

Illustration of Thomas Renaud speaking on a video call
Video interviewRecording previewREC12:00
Thomas RenaudCandidate · camera on12 min interview
BRAIN HRConducts the interview
RecordingRecording savedIllustrated preview · fictional interview

Live transcriptInterview transcript

Auto · EN
  1. AI agent

    What was your role in deploying this model to production?

  2. Thomas Renaud

    I designed the forecasting model and prepared its release with the platform team. Since then, I have tracked accuracy and data drift every week.

Speech transcribed in real timeTimestamped source · 06:03Evidence · Model deployment

Evaluation grid

6 mandate criteria
  • PythonEvidencedCV p.2
  • SQLEvidencedCV p.2
  • Forecasting modelsEvidencedCV p.2
  • Product-team collaborationEvidencedCV p.2
  • Model deploymentTo confirmConfirmed in interviewCV p.3CV p.3 + interview 06:06
  • Technical mentoringNo evidence—
AI summary

Personal role in deployment described: model design, release with the platform team, weekly drift monitoring.

Recruiter validation

Nothing to validate yet1 to validate✓ Validated

Evidence joins the Norvia client dossier once you validate it.Added to the Norvia client dossier · Amélie M.

WorkspaceAgenda

Plan and follow interviews

See upcoming and past interviews, then open each candidate sheet to follow up.

11 – 15 August
09:30Matching launch · Norvia15:00AI interview · Camille Roy
16:00Client review · Norvia17:30Team check-in
10:30Recruiter interview · Nadia Berrada14:00Client check-in · Helix
09:00AI interview · Julien Petit11:00Recruiter interview · Camille Roy
10:00Client review · Kea Systems

Matching in progress

Analyze thousands of CVs and identify the relevant profiles. In minutes, not days.

BRAIN HR compares the full batch against the job criteria, shows the evidence behind every match, and prepares candidate dossiers for client review.

Book a demoExplore the product

Without / with BRAIN HR

Same mandate. Two ways to handle it.

A client entrusts you with a senior data scientist role and 3,294 CVs arrive. Here is what your team goes through, without and then with BRAIN HR.

TodayAn inbox and a spreadsheet
With BRAIN HRThe same batch, sorted and explained
What changes, in practice
TodayWith BRAIN HR
Reading the batchThousands of CVs opened one by one from the inbox.The whole batch is read and sorted in minutes.
Justifying the shortlistA spreadsheet filled in by hand, hard to defend to the client.Every criterion linked to the CV sentence that proves it.
Preparing the interviewGeneric questions; grey areas go unnoticed.Targeted questions on what still needs checking.
Delivering to the clientThe dossier is copied and reformatted by hand.The dossier comes out in the client’s format, reviewed then sent.

From a CV batch to a client dossier.
On a single platform.

  1. ATS, talent pool and inbound applications: duplicates merged and suspicious files isolated before any analysis.

  2. Every CV read against the same grid. Each criterion points to the CV sentence that proves it.

  3. An explicit formula, references to the sources and vigilance signals. Never an opaque ranking.

  4. Questions start from open points; answers become evidence attached to the dossier.

  5. The dossier in your client’s format, reviewed and approved by the recruiter before sending.

Launch the demo

Fictional data · Integrations depend on your configuration.

Days become minutes

One framework for the first CV. And the thousandth.

Import the batch, apply the job criteria and open the comparison. In minutes, not days. Your team focuses on the results that need a decision.

BeforeCVs opened and compared one by one.

With BRAIN HRThe full batch follows one approved framework.

BRAIN HRNorvia AnalyticsEN
ProjectsSenior data scientistCandidate batch
  1. Define the requirement
  2. Prepare the batch
  3. Review the results

Approved requirement

Senior data scientist

One shared framework

Must-have criteria

  • Python
  • SQL
  • Model deployment and monitoring

Preferences, assessed separately

Product-team experience. A preference must not silently become an exclusion criterion.

Multiple-file import

3,294 fictional CVs, one batch

PDF · DOCX
PDFThomas RenaudPreparing source data
PDFNadia BerradaPreparing source data
PDFJulien PetitPreparing source data
Files received3,294
Duplicate copies reconciled−46
Documents isolated for review−7
Profiles ready for matching3,241

The same criteria apply across the batch. This animation explains the workflow; its duration is not processing time.

Matching results

Open the comparison, not another CV.

Three sample profiles shown below. Not a measured selection rate.

  • TRThomas RenaudTo explore: personal ownership of model deployment.↗
  • NBNadia BerradaDepth of SQL experience needs confirmation.↗
  • JPJulien PetitNo evidence of model deployment in this CV.↗
Compare these profiles

  • Process the volume

    One framework applied to the whole batch, instead of repeated manual sorting.

    See how it works

  • Understand the match

    Criteria, experience and source excerpts explain the differences between profiles.

    See how it works

  • Keep the decision

    Uncertainties remain visible. The recruiter reviews the dossier before delivery.

    See how it works

One connected workflow

From requirement received to client-ready dossier. One workflow.

Criteria, matching, evidence, interview questions and the client dossier remain connected from the first CV to recruiter approval.

Matching in progress

Compare profiles on the criteria that matter.

One shared framework, evidence behind every match and explicit open points. Select a candidate to understand the result and find its source.

BRAIN HRNorvia AnalyticsEN
ProjectsSenior data scientistComparison matrix

Compare on the same criteria

3 sample profiles · Must-haves shown separately from preferences

Simplified matrix

Select a profile to read the evidence behind the result.

CandidatePythonSQLModel deployment
Evidence foundEvidence foundTo confirm
Evidence foundTo confirmEvidence found
To confirmEvidence foundNot evidenced

Scroll the table horizontally on a small screen.

A preference is not a must-have.

Product-team experience adds context. It does not replace the evidence required for Python, SQL or deployment.

From result to source

Thomas Renaud

Python and SQL used in production projects.

Fictional CV · page 2

Developed forecasting models in Python and SQL pipelines for a product team.
A question, not an assumption

What was your role in deploying this model to production?

See Thomas Renaud’s interview example
Missing evidence and a technical failure are different.

“Not evidenced” means that the analyzed CV does not establish the criterion. An extraction or analysis failure leaves the result incomplete: it must be reviewed or rerun, not treated as a candidate rejection.

Relevant profiles

What the CV does not say becomes a question. Not a surprise.

Open points become questions connected to the requirement. The interview investigates them while the recruiter keeps final approval.

Demonstration · fictional data · Thomas Renaud
BRAIN HRBRAIN HR+
Norvia AnalyticsENAM
Interview workspaceDemonstration · fictional data

Thomas Renaud

Project · Senior data scientist

Project
Senior data scientist
Matching runs
Current matching
Decision
Decision pending

Human interview preparation

Questions, assessment criteria and notes for the recruiter.

Technical3 prepared questions
What was your role in deploying this model to production?
Assessment criteria
Ownership of deployment and model monitoring.
Demonstration · fictional dataThomas Renaud · Norvia Analytics

Thomas Renaud’s deployment responsibilities remain to be confirmed by the recruiter. AI interview availability depends on your workspace configuration. Explore the interview workspace →

Recruiter decision

Your client needs a candidate submission. Not a score.

The profile, relevant experience, evidence and open points feed the client dossier. Your template sets the format; the recruiter approves the content.

From analysis to deliverable

The useful information, already connected.

  1. Profile and experienceRelevant context for the job.
  2. Evidence and open pointsWhat is documented; what still needs checking.
  3. Client template and human reviewAn active template and recruiter approval are required before delivery.

Layout example, not an export from the live platform. Actual generation must be validated with an active client template.

BRAIN HRNorvia AnalyticsEXAMPLE · DRAFT

Senior data scientist

Thomas Renaud

6 years of experience · Python · SQL

Relevant experience

Developed forecasting models in Python and SQL pipelines for a product team.

E1 · CV page 2

To verify before delivery

To explore: personal ownership of model deployment.

Interview question
Recruiter approval pendingFictional data

Fits your workflow

With your ATS. Not instead of it.

Keep your application tracking. BRAIN HR adds batch analysis, evidence review, targeted interviews and preparation of the client dossier.

CV import is available. Direct integrations depend on your tools and the connectors validated for your setup.

Understand the differences

Value, measured on your workflow

Measure the time to your first approved candidate dossier.

The same requirement, a defined batch and an agreed deliverable. Compare the whole process, not just how fast a file uploads.

Machine processing
From the defined batch to complete, usable results.
Recruiter time
Active time spent reviewing, correcting and validating.
Rework
Incomplete results, errors and checks still required.
Client-ready output
Time until the first dossier approved for delivery.
Free 30-minute demonstration

Start with fictional data. Quantified gains require a documented benchmark on a defined workload.

Book a demo

Dossier delivered

The speed of a new generation of tools. The control an enterprise expects.

Human approval, access, retention and traceability remain explicit before every candidate dossier is delivered.

  • Hosting

    Locations, providers and data flows documented.

  • Access

    Roles, permissions and company isolation described.

  • Retention

    Retention periods and deletion made explicit.

  • Audit

    Sensitive actions and changes traceable.

View the detailed commitments

Mandate open

Show us the requirement that takes you days.

We will show you how to turn it into relevant profiles and client-ready dossiers — in minutes, not days.

  • Mandate volume
  • Required seniority
  • Main constraint
  • Current tool
  • Optional deadline

No CV or candidate data is requested.

Book a demo

Free 30-minute demonstration · Synthetic data available