CANDIDATE INTELLIGENCE
Know the Candidate Beyond the Résumé.
Bring together professional story, capabilities, experience, evidence, trajectory, aspirations and relevant context into one explainable intelligence view.
What do we actually know about this candidate — and how trustworthy and relevant is that knowledge for this role?
The Candidate Intelligence Profile
Open a fictional candidate:
Maya R. — Candidate Intelligence Profile
Progressive exploration — deliberately not a giant résumé.
How did this person become who they are?
Maya moved from engineering into product, led two platform launches, and is developing enterprise-scale judgement. The story connects records into a narrative — not a list of titles.
From Fragmented Records to a Coherent Story
What the person learned, where they applied it, what they achieved, what they are developing — and what remains uncertain.
Sourced facts
- 8 years product roles
- 3 launched products
- MBA, 2018
- 2 certifications
AI interpretation
- Likely strong at ambiguity
- Developing enterprise sales exposure
- Trajectory suggests platform leadership
Sourced facts and AI interpretation are always kept separate — and labelled.
The Capability Landscape
Capabilities by domain, depth, recency, application and evidence strength.
Product strategy
Strong evidenceCustomer discovery
Strong evidenceAnalytics
Documented evidenceAI knowledge
Medium evidenceEnterprise delivery
Medium evidenceA capability is not treated as proven merely because it appears in a profile.
From Job Titles to Actual Work
Title says:
Senior Product Manager, AI platform
The actual work:
- Owned roadmap across 3 squads
- Rebuilt onboarding — activation up
- Led enterprise retention investigation
Evidence behind it: Roadmap artifacts · Launch reviews · Project retrospective
Title says:
Product Lead, analytics suite
The actual work:
- Scaled discovery program
- Shipped 2 major releases
- Coached 2 associate PMs
Evidence behind it: Research repository · Release notes · References
Title says:
Product Manager, marketplace
The actual work:
- Designed seller tooling
- Ran 40+ customer interviews
Evidence behind it: Interview recordings · Design docs
Evidence & Confidence
Every important insight has source, evidence type, date/recency, relevance and confidence.
| Insight | Source | Type | Recency | Confidence |
|---|---|---|---|---|
| Product strategy | Work sample + launches | Demonstrated | Current | High |
| Leadership | Manager reference | Verified | Recent | High |
| AI knowledge | Certificate + coursework | Documented | 1 yr | Medium |
| Enterprise sales | Self-declared | Claimed | — | Low |
Self-declared information is separated from documented, assessed, demonstrated or independently verified evidence.
A Trajectory, Not a Prediction
2015–2018
Engineering foundation, MBA
2018–2020
First product role — marketplace tooling
2020–2023
Product Lead — analytics suite, discovery program
2023–now
Senior PM — AI platform, enterprise delivery
Developing
Enterprise-scale judgement, sales exposure
This is a trajectory view — not a prediction of future success.
The Context Lens
Role family, business unit, geography, seniority, work environment and relevant constraints.
AI scale-up, product-led
Strong relevance
Platform experience, ambiguity tolerance, hands-on discovery
Regulated public sector
Lower relevance
Limited domain exposure to compliance-driven delivery
The same person can be relevant in one context and less relevant in another.
Explain My Candidate
Product strategy is a core requirement of the role model — and Maya's evidence is strong and current.
Answers expose the underlying evidence — every time.
The Candidate Intelligence Timeline
A chronological stream of education, projects, employment, credentials and assessments — filterable by capability or evidence source.
Candidate Comparison Without Ranking
| Dimension | Maya R. | Daniel K. | Priya S. |
|---|---|---|---|
| Evidence coverage | Strong on core skills | Strong on analytics | Partial — emerging |
| Experience relevance | High | Medium | High potential |
| Capability depth | Strategy deep, AI growing | Technical deep | Foundation building |
| Key uncertainty | Enterprise sales exposure | Product leadership | Scale of delivery |
The purpose is structured understanding — not an opaque leaderboard.
The Recruiter Investigation Workspace
Pin evidence, record observations, request additional evidence, flag inconsistencies — and create a candidate brief that preserves the reasoning trail.
Candidate brief
The reasoning trail is empty — take an investigation action and it will be recorded here.
The reasoning trail is preserved — every action is recorded.
AI Assistance, With the Facts Labelled
Summarise the profile
Identify missing information
Surface contradictions
Formulate follow-up questions
Prepare decision briefs
AI output must distinguish sourced facts from generated interpretation.
Investigate Maya
Illustrative demo — fictional candidate, synthetic data
One fictional candidate, six investigation moves — inspect, evidence, uncertainty, question, request, brief.
Illustrative demonstration — synthetic data, not validated enterprise data.
Candidate Agency
Where appropriate, candidates can review relevant information, correct inaccuracies, provide evidence — and understand how their information is used.
Governance
Human review
A person reviews consequential interpretations.
Source traceability
Every insight traces back to its source.
Data minimisation
Only necessary information is used.
Role relevance
Information must be relevant to the role.
Correction mechanisms
Inaccuracies can be challenged and fixed.
Access controls
Access is restricted to authorised users.
Audit trails
Views and changes are recorded.
SEE THE SYSTEM
A Candidate Is More Than a Résumé.
90 seconds · reserved film slot
Understanding before evaluation.
Connected, Never Duplicated
Understanding Before Evaluation.
Candidate Intelligence turns fragmented records into an explainable, evidence-aware view of a person — without reducing them to a résumé or a single score.