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Candidate Assessment & Intelligence

Evidence you can make
a decision from.
Not recruiter opinion.

Most shortlists are formatted CVs with a summary call attached. Talhive's candidate assessment produces a written intelligence brief for every shortlisted candidate, competency evidence, motivation analysis, risk flags, and a pool rank, before the first interview. You read the candidate before you commit time to meeting them. This is what structured candidate assessment is supposed to do, and rarely does in practice.

The brief does not begin with the candidate. It begins with the role. The brief determines the scoring criteria. The criteria determine the evidence we gather. The evidence goes into writing before any recommendation is made. That sequence is why the document is useful rather than decorative.

What you receive

Five sections.
Every shortlisted candidate.

Every section is written against the specific role brief, not a generic template. Gaps that matter are named. If a dimension cannot be verified, it is marked as unverifiable, not estimated.

Candidate Snapshot, Verified profile: role, years of experience, education, archetype, and composite score at a glance.
Verified Skills Profile, Competency assessment with evidence and verdict for every dimension. No unsubstantiated ratings.
Culture Fit Fingerprint, Scored against the client's specific cultural criteria, not generic proxies or personality type labels.
Value Projection Brief, 12-month milestone-specific impact hypothesis built from comparable career evidence.
Competitive Risk Assessment, Competing offers, urgency signals, and a recommended closing strategy specific to this candidate.
Scoring

A composite score with
the evidence to defend it.

Each dimension is scored 1 to 10 and labelled. The composite is a weighted average calibrated to what the specific role requires most. Pool rank tells you where this candidate sits relative to everyone assessed for the same mandate.

Strong Match
Evidence is clear, specific, and verifiable. The dimension is met or exceeded by the candidate's background.
Match
Solid evidence. Meets the requirement without exceptional signal. No significant concern.
Partial
Evidence present but incomplete or inferential. Named gap noted with impact level.
Gap
Evidence does not support the dimension requirement. High or medium impact, coaching note included.
Engineering & AI Assessment

Technical competency with
production evidence.

The engineering assessment framework is built around what candidates have shipped in production, not what they can articulate in a whiteboard session. Every dimension requires verifiable evidence from the candidate's actual career before a score is applied.

Gate Criterion

Hands-On Execution is always a gate criterion in engineering assessments. A candidate who cannot demonstrate production-grade, hands-on engineering work does not clear the gate, regardless of their scores on other dimensions.

This criterion is applied before any shortlist presentation. It cannot be waived.

System Design & Architecture
Clarity of architectural decisions, scalability judgment, trade-off reasoning at the system level. Evidence drawn from specific products built, not whiteboard responses.
App / Platform Ownership & Scale
Evidence of owning a significant technical surface end-to-end, not just contributing to one. Includes metrics: MAU, DAU, transaction volume, or equivalent.
Stack Depth & Technical Currency
Verified proficiency in the specific stack the role requires. Self-reported skills are tested against verifiable career evidence.
Performance & Reliability
Production evidence of performance work: crash rates, latency, ANR reduction, uptime, or equivalent. Inference is not accepted.
Hands-On Execution (Gate)
Production code shipped, features owned end-to-end, and personal technical contribution visible in the career narrative. This is the gate dimension.
AI / ML Signal (role-dependent)
For AI and ML roles: production deployment evidence, model evaluation rigour, and the distinction between research and production context.
Testing & Engineering Discipline
TDD evidence, coverage targets, CI/CD ownership, code review rigour. Sets the engineering culture signal for founding and senior roles.
DevOps & Platform Maturity
Infrastructure ownership, deployment pipeline maturity, observability approach. Assessed relative to role scope, CTO vs Staff Engineer dimensions differ.
Leadership & Mentorship
Direct evidence of raising team quality: hiring, coaching, code review culture, technical decision-making process. Weighted higher for senior and leadership roles.
Product Thinking & UX Impact
Engineering decisions aligned to product and user outcomes. Particularly weighted for founding engineer and mobile roles.
Domain Relevance
Relevance of prior domain experience to the client's specific context. Assessed as signal, not gate, unless the brief makes domain mandatory.
Public Evidence & Portfolio
GitHub, publications, open-source contributions, or equivalent public signal. Absence is neutral; presence is a positive signal.
Sample

The Chhatrasal Bundela Intelligence Dossier

Founding Senior Android Engineer, Asymmetric Labs, Bengaluru. Open case study showing the full engineering assessment format, 12 dimensions, composite score 8.1/10, pool rank, culture fit fingerprint, 12-month value projection, and closing strategy.

Product Assessment

Product judgment. Not a process audit.

Product assessment cannot be done through CV review alone. The dimensions that distinguish strong PMs, problem framing quality, prioritisation under constraint, how they work with engineering, require direct conversation and structured evidence-gathering.

Product Judgment
Quality of decision-making under ambiguity and constraint. Assessed through specific examples of hard calls made, not through familiarity with PM frameworks.
Problem Framing
Ability to define the right problem before jumping to solution. Evidence: how they have changed the direction of a product after reframing what problem was actually being solved.
Prioritisation Quality
Evidence of hard tradeoffs made under constraint, features not built, scope deliberately cut, resource conflicts resolved. Not a list of what was built.
Metrics Fluency
Ability to identify the signal metrics that move the business, and distinguish them from vanity metrics. Evidence drawn from actual measurement decisions in prior roles.
User Empathy & Research
How product decisions connect to user insight. Not whether the candidate has done user research, but how it has changed what they built.
Execution with Engineering
How they work with engineers: how they handle pushback on scope, how they resolve ambiguity in specs, how they manage the engineering relationship under pressure.
Roadmap Ownership
Quality of roadmap reasoning, strategic clarity, stakeholder buy-in approach, horizon balance. Assessed through specific roadmaps they have owned.
Stakeholder Management
How they handle competing priorities from CEO, commercial, and engineering. Evidence of influencing without authority and managing up effectively.
Communication & Decision Documentation
Can they write a tight brief? Can they explain a product decision to a non-product audience? Clarity of written communication is a strong PM signal.
Zero-to-One vs Scale Fit
Context fit assessed explicitly: builder vs operator, early vs growth vs mature. Mismatched context is a common failure mode, assessed directly before presentation.
Design Assessment

Beyond the portfolio.

Portfolio review reveals craft in controlled conditions. The design assessment framework tests the thinking, collaboration quality, and judgment that portfolios do not show, the dimensions that determine whether a designer is effective inside a real product organisation.

Product Thinking
Does design connect to user and business outcomes, or stop at visual quality? Evidence: decisions made that changed a product direction, not just a screen.
Interaction Quality
Depth of interaction design thinking: state management, edge cases, error handling, micro-interactions. Assessed through specific design decisions, not portfolio surface.
Systems Thinking
Mental model for design consistency, component reuse, and scale. Evidence of design systems contribution or creation, not just consumer.
Visual Craft
Quality and intentionality of visual execution. Assessed relative to role context: a design systems lead and a consumer UX designer require different calibration.
User Research Fluency
How user insight connects to design decisions. Not whether research was done, but whether it changed what was designed.
Design Systems
Proficiency in building, extending, or consuming design systems. For design systems leads, this is a gate-level dimension.
Collaboration with PM and Engineering
How the designer works with product managers and engineers. How they handle scope constraints, technical pushback, and design review with non-designers.
Speed vs Polish Judgment
Does the candidate distinguish contexts that require craft from contexts that require iteration? Strong signal of maturity in a product environment.
Critique & Communication
Can they articulate a design decision to a non-designer? Can they give and receive critique in a way that improves the output rather than defending the work?
Portfolio Evidence
Coherence and depth of portfolio as a signal, not just aesthetic quality. Unexplained design decisions and missing context are assessed as risk signals.

Want to see the
assessment in full?

The Chhatrasal Bundela Intelligence Dossier is an open sample of Talhive's candidate assessment — a real engineering assessment, composite score, culture fit fingerprint, 12-month value projection, and competitive risk assessment. Request it directly.