Every interview. A clearer hiring decision.
Know who fits the role. See the gaps. Make your next hire with clear evidence.
Interview transcript
“I added queue
backpressure”
Evidence receipt
- Role criterion:
- Resilient systems
- Evidence:
- Supported
- Follow-up:
- Recovery tradeoff
- Video interview
- Timestamped evidence
- Role alignment
- Human review
YOUR INTERVIEW, MADE READABLE
From recording.
To clear evidence.
- How did you handle the failure?
- I added queue backpressure.
- We isolated the failing service.
Flag possible read or AI-assisted answers. Review the source.
Have a transcript? Check the speakers, technical terms, and missing words.
Only have the recording? Purchase transcription first.
Transcript requirements & speech-pattern review
Start with a recorded technical interview. Your company has already recorded the interview; use a quality transcript or provide an authorized video URL and purchase transcription before onboarding.
Speech-pattern irregularities can be flagged for review of possible read or externally assisted answers, including AI-provided responses. Review depends on usable source material. A cleaned transcript cannot establish how an answer was delivered. Accent, pauses, disfluency, disability, or polished delivery alone must not be treated as proof that an answer was read or generated by AI. A flag requires source review and a contextual follow-up; it does not determine candidate eligibility.
Set up the role.
Bring in the candidate.
Your job. Your questions. Your ideal answers.
01 / Bring your interview
Bring the interview.
Start with your recorded interview and a quality transcript.
02 / Set up your account
Make it your workspace.
Set up your account and company profile.
03 / Add the job
Define what good looks like.
Add your role, questions, and ideal answers.
How do you design for failure?
- Failure isolation
- Queue backpressure
- Recovery tradeoffs
04 / Add your candidate
Connect the candidate.
Add the candidate and attach their interview transcript.
“I added queue backpressure.”
05 / Review the score
See the fit. Inspect the gaps.
Review the alignment score, source evidence, and open questions.
See the full setup checklist
- Create your account and company profile
- Define the role
- Add the job description and must-haves
- Add the interview questions
- Add the ideal-answer criteria
- Create the candidate profile
- Upload the quality-checked transcript
- Process the interview evidence
- Review the evidence and gaps
- Make the human decision
Review the evidence. Resolve the gaps. Your team makes the final decision.
THE OUTPUT
Every finding shows its source.
See the question, the candidate’s words, the role requirement, and what is still missing.
“How do you design for failure in a distributed system?”
CANDIDATE EVIDENCEI broke the service into smaller failure domains, added queue backpressure, and instrumented each stage before the rollout.
Exact source, preserved for inspection.
No missing detail invented.
Designs resilient production systems
- Evidence
- Supported
- Ownership
- Direct
- Tradeoff depth
- Incomplete
- Contradiction
- Not assessed in this excerpt
Supported with incomplete tradeoff depth
Follow-up required
BEFORE YOU HIRE
See the fit. Expose the gaps.
Measure interview answers against your role. See what fits and what needs proof.
WORK → DELIVERY
See how they think through the work.
Connect demonstrated reasoning to the work, decisions, and handoffs across your human-and-agent delivery chain.
Reduce release failure without slowing delivery.
- ProductDefines value
- PlatformMoves work forward
- SecurityChecks risk
- ClientReceives the outcome
Work-reasoning evidence
- Supported
- Systems reasoning
- Needs evidence
- Cross-team influence
- Unknown
- Incident ownership
Human review
THE CONTROL
Evidence first. Math second. Human decision.
No evidence means no number.
- 01
Find evidence
Identify the candidate’s exact answer and its job-related meaning.
- 02
Lock the source
Bind the question, criterion, ownership, and contradiction to the transcript.
- 03
Apply the method
Use configured rules, depth anchors, and core gates. Keep missing inputs missing.
- 04
Human review
Confirm, correct, request more evidence, or approve a result for release.
THE METHOD
44 methods. Six math families. One controlled result.
Evidence is identified. Software runs the math. A person controls the decision.
See the method- 01Signal
Is there usable evidence?
- 02Measurement
What does it support?
- 03Semantic geometry
Does the meaning match?
- 04Systems topology
How does it connect to the work?
- 05Reliability
What remains uncertain?
- 06Decision gates
Can a core gap block the result?
GLOBAL ENGINEERING TALENT
Bias controls. Integrity review.
Technical English is the baseline. Native-sounding English is not.
Evaluate the reasoning.
Role-specific ideal answers, answer-opportunity checks, and language normalization keep the comparison tied to the work.
Technical substance, source quality, and context remain separate from accent, fluency, and presentation.
Inspect irregular patterns.
Speech-pattern irregularities can be flagged for review of possible read or externally assisted answers, including AI-provided responses.
How to interpret a flag
Review depends on usable source material. A cleaned transcript cannot establish how an answer was delivered. Accent, pauses, disfluency, disability, or polished delivery alone must not be treated as proof that an answer was read or generated by AI. A flag requires source review and a contextual follow-up; it does not determine candidate eligibility.
OPERATING HISTORY
Built from real technical interviews.
- 30+ US companies
- Company-reported use of related methods and processes.
- 13,000+ interviews
- Company-reported empirical source material.
- 8+ years
- Research and operating history.
Axiom Cortex is the internal evaluation process inside TeamStation AI’s Distributed Engineering OS. Hardened through internal use, its mature evaluation engine is now available to companies hiring for the modern agentic delivery chain.
Company-reported operating and research history · September 2026. These figures show applied use and the size of the evidence base, but do not by themselves prove predictive validity. Read the hiring history.
- Company-reported history
- The figures above describe operating history, not independently verified results.
- Synthetic studies
- Controlled examples test a method under stated conditions; they do not establish hiring outcomes.
- Independent and production verification
- Not publicly verified by this site. A current study or release receipt is required for either claim.
THE SCIENCE BEHIND THE DECISION
Behavioral axioms.
Measured against the role.
The science connects answer-level evidence, work-reasoning patterns, and the demands of human-agent delivery.
- 05B-Axiom checksRead each answer
- 06Reasoning domainsMap the mental shape
- Role alignmentConnect it to the work
01Behavioral axioms
Accuracy, mental models, procedural knowledge, clarity, and cognitive load, examined answer by answer.
- Accuracy
- Technical correctness against the question and ideal-answer criteria.
- Mental Model
- The explanation of mechanisms, dependencies, and cause and effect.
- Procedural Knowledge
- The steps used to implement, diagnose, test, and recover.
- Clarity
- Whether the technical explanation communicates the relevant reasoning.
- Cognitive Load
- How the answer handles interacting constraints and technical complexity.
Answer Evaluation Units keep each answer connected to its question and evidence before trait synthesis.
02Mental-shape domains
Six dimensions describe how the interview demonstrates understanding, judgment, adaptation, collaboration, learning, and self-calibration.
- Conceptual Fidelity
- Does the explanation preserve the concepts the system actually depends on?
- Architectural Instinct
- How are boundaries, dependencies, failure paths, and tradeoffs handled?
- Problem-Solving Agility
- How does the approach change when new evidence changes the problem?
- Collaborative Mindset
- How are decisions shared, corrections handled, and handoffs made?
- Learning Orientation
- How is new evidence used to revise an incomplete or outdated model?
- Metacognitive Conviction
- Does expressed confidence match the evidence, including what remains unknown?
The 2026 model extends the research framing to human-task-agent alignment.
03Role & agent alignment
Measure the distance between demonstrated reasoning and the requirements of the role, team, and agent workflow.
- Weighted Euclidean distance
- The published outer model compares a normalized six-domain profile with a task requirement profile.
- Team-specific requirements
- Separate requirement profiles model stream-aligned, platform, enabling, and complicated-subsystem teams.
- Agent autonomy
- Autonomy-dependent shifts test how the required human reasoning changes as agents take on more work.
- Ideal-answer alignment
- In the product, configured role criteria connect the candidate’s answer to the expected technical evidence.
The published outer calculation is a research model; it does not specify the production scoring configuration.
04Meaning & language calibration
Separate technical understanding from surface wording, then apply behaviorally anchored measurement and configured aggregation.
- Conceptual Fidelity
- Compare the meaning and technical substance of an answer, rather than a memorized phrase.
- ESL / L2 calibration
- The report describes language-aware calibration to reduce second-language effects on technical evaluation.
- Behaviorally anchored scoring
- Relate a measurement to observable answer evidence and defined evaluation anchors.
- Deterministic aggregation
- The research specifies mathematical aggregation from measured signals into higher-level findings.
05Reliability & bias analysis
Study agreement, error, calibration, subgroup differences, and drift so the measurement itself can be tested.
- Inter-rater reliability
- Test whether evaluators agree when reviewing the same evidence.
- ROC and precision-recall analysis
- Examine discrimination, false positives, and false negatives against defined reference outcomes.
- Calibration error
- Test whether estimated confidence corresponds to observed results.
- Subgroup fairness
- The report specifies adverse-impact ratios and equal-opportunity gaps for fairness audits.
- Drift and oversight
- Monitor changes in evaluation behavior, with thresholds and human oversight.
These are documented validation methods, not a claim that every metric has a published production result.
06Delivery physics & sensitivity
Stress-test missing evidence, measurement noise, weighting, review queues, and topology assumptions around the work.
- Noise and missing domains
- Synthetic perturbations test how uncertain or absent inputs alter the alignment result.
- Weight sensitivity
- Vary weights to test whether the preferred team topology changes.
- Kingman queue model
- Study how utilization and variability affect waiting time in the delivery system.
- Topology-health scenarios
- Explore system-level conditions around human-agent work through Teamlemetry scenarios.
- Logistic coefficient recovery
- Check whether the analysis recovers effects deliberately planted in synthetic data.
The fixed-seed study uses 24,000 synthetic profiles. It tests the calculation and its assumptions, not real hiring outcomes.
Read the working papers.
- FOUNDATIONAL WORKING PAPER · SSRNAxiomCortex: Scientific R&D Report
Defines Answer Evaluation Units, B-Axiom checks, trait synthesis, Conceptual Fidelity, L2-aware calibration, aggregation, reliability, fairness, and monitoring.
- WORKING PAPER · SYNTHETIC STUDYHuman-Task-Agent Alignment Across Software Team Topologies
Tests six reasoning domains, weighted alignment distance, team topology, agent autonomy, measurement sensitivity, queue pressure, and synthetic coefficient recovery.
Research applications & study design
The 2025 report combines five B-Axiom checks into four role-fit traits. The 2026 study represents mental shape using six work-reasoning domains. These are related research layers, not interchangeable score definitions.
- COMPANY ARTICLE · TEAMSTATION AIAxiom Cortex for LATAM Agentic Engineering
Explains how interview evidence connects to role fit, engineering loops, governance, and delivery alignment.
- COMPANY ARTICLE · TEAMSTATION AICTO Guide to Agentic Workflow Fit Signals
Connects work-reasoning signals, team topology, and human-plus-agent engineering workflows.
- COMPANY ARTICLE · TEAMSTATION AITelemetry Predicts Team Performance
Explores how post-hire delivery signals can test whether interview evidence stays aligned with real work.
The 2025 report documents the evaluation framework. The 2026 study stress-tests an outer alignment model on synthetic data. Both are working papers, not peer-reviewed validation. Company articles are not independent scientific validation.
What this proves: the methods and synthetic tests are documented for inspection. What it does not prove: a validated prediction of an individual’s future job performance.
The report supports a human decision, with source evidence, gaps, and follow-up questions available for review. See an evaluation
DIRECT ANSWERS
What teams need to know.
Does it make the hiring decision?
No. A qualified human reviewer inspects the evidence, corrects or excludes invalid findings, requests more evidence when needed, and makes the final decision.
What does Axiom Cortex evaluate?
It examines job-related evidence in a technical interview, using the role, must-haves, questions, ideal-answer criteria, and attributable candidate responses.
Do I need a transcript?
Yes. Start with a recorded technical interview and a quality-checked transcript. Check speakers, technical terms, and missing words before evaluation.
Can Axiom Cortex transcribe a recording?
The early-beta workflow offers paid transcription from an authorized video URL before onboarding and evaluation. Service availability and price are confirmed during the beta demo; this website does not upload, transcribe, or charge for a recording.
Does it judge facial expressions or accents?
No. Face, gaze, appearance, accent, voice quality, emotion, personality, disability, nationality, and other protected traits are excluded from capability scoring. Interview-integrity flags require a separate source review; grammar, pauses, or polished speech alone do not prove AI use or technical depth.
Can it work for global engineering teams?
The product is designed for job-related technical evidence across global teams, with technical English as the shared baseline. Native-sounding English is not a requirement, and missing evidence stays missing. This is not a promise that bias has been eliminated.
How is candidate evidence protected?
This public website does not collect candidate data, recordings, or transcripts. Before production use, confirm the application’s access, retention, deletion, and security terms. Those protections must be verified for the deployed service, not inferred from this preview.
What does the customer receive?
A comprehensive, role-specific alignment score when evaluation requirements are met, with source excerpts, criterion findings, ownership, depth, contradictions, gaps, and follow-up requirements. Your team reviews the evidence and makes the final decision.
What remains under human control?
Your team defines the role and criteria, checks source quality, reviews and corrects findings, requests further evidence, controls release, and owns the final decision.
FOR TECHNICAL LEADERS
Put one interview to the test.
Bring one role. See the alignment, the gaps, and the next question.
30-minute call with TeamStation AI.
Booking opens in Zoom Scheduler.