Suitable AI

Technical Intelligence Designed for Complex Engineering

THE FOUR ARCHITECTURAL INTELLIGENCE LAYERS
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REQUIREMENT INTELLIGENCE

Translates high-level product roadmaps, PRDs, and architecture block diagrams into actionable capability matrices. Rather than searching for flat keywords, Requirement Intelligence understands the underlying dependencies—such as how an AUTOSAR BSW requirement maps to MCAL, SPI bus timing, and memory partitioning.

Mechanism:
Input: System Specs / Architecture Diagram → Output: Granular Competency Matrix
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TALENT INTELLIGENCE

Surfaces specialized engineering practitioners across deep technology clusters. Identifies verified contributors across specialized technical ecosystems, patent databases, open-source hardware repositories, and domain-specific product lineages.

Mechanism:
Input: Competency Matrix → Output: Focused Specialist Pipeline
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TECHNICAL INTELLIGENCE

Evaluates engineering depth before valuable engineering-manager time is consumed. Leverages Nancy for conversational technical verification and Alex for live code execution and architectural problem-solving.

Mechanism:
Input: Shortlisted Engineers → Output: Verified Technical Scorecards
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DELIVERY INTELLIGENCE

Integrates qualitative engineering manager feedback, sprint performance signals, and milestone delivery metrics to continuously refine evaluation models and accelerate subsequent team scale-ups.

Mechanism:
Input: Delivery Performance → Output: Calibrated Matching Algorithms

Nancy & Alex: Automated Engineering Verification

Sample Dossier Domain:
SOC-2 Verified Autonomous Assessment

Objective, In-Depth Architectural Evaluation

Nancy probes architectural trade-offs, hardware constraints, and failure modes through conversational technical questioning, delivering clear verification summaries before human interviews.

Why Engineering Managers Rely On It:
Zero Resume Keyword Fluff: Scores actual operational reasoning and code syntax.
Standardized Calibrated Rubric: Eliminates interviewer fatigue and subjective bias across cohorts.
Recovers 85% Engineering Time: Managers only meet pre-qualified, verified finalists.
N
NANCY AI SCREENING DOSSIERDOSSIER #SUIT-AUTO-9482
Validated Today • 42-min session•Lead AUTOSAR / Embedded Systems Engineer
RECOMMENDATION
STRONG ADVANCE (Top 2%)
96%
Evaluation Focus:AUTOSAR Classic / Multi-Core MCAL Buffer Synchronization
Platform: AURIX TC397 • ASIL-D • RTOS MCAL
1. Calibrated Competency Matrix4 / 4 Standards Met
MCAL & Hardware Abstraction98%
Score: 98/100Exceptional
Multi-Core Concurrency & Spinlocks96%
Score: 96/100Verified
ISO 26262 ASIL-D Safety Architecture94%
Score: 94/100Exceeds
DMA & Interrupt Latency Optimization95%
Score: 95/100Verified
2. Technical Question & Verified Candidate Defense
Nancy (AI Evaluator)Multi-Core Race Condition Probe

"In a multi-core AUTOSAR Classic ECU, how do you resolve inter-core data consistency when transferring high-frequency sensor frames between Core 0 and Core 1 in the Shared Memory MCAL buffer?"

Candidate Defense High Depth Audio/Text Captured

"We architected a lock-free ring buffer utilizing hardware spinlocks provided by the Infineon AURIX TC397 semaphore peripheral module, paired with DSB and ISB memory barrier instructions in the RTE layer to prevent compiler and hardware out-of-order reordering during multi-master DMA bursts."

Autonomous Agent Verification Findings:
VerifiedInfineon TC397 hardware spinlock peripheral correctly targeted for multi-master bus arbitration without CPU stall.
VerifiedIdentified Data Synchronization Barrier (DSB) to prevent memory store buffering race conditions across RTE.
ObservationZero textbook hesitation; demonstrates direct hands-on calibration experience on safety-critical automotive ECUs.
Hiring Manager Action:Strong candidate for Principal/Lead Firmware role. Recommending skipping preliminary screening and proceeding directly to Technical System Design round.
Auditable Dossier: Full transcript & test artifacts stored
AI SCREENING ENGINE

Nancy

Nancy conducts structured technical conversations designed to evaluate experience, technical understanding and relevance to engineering requirements. Acting as an initial technical screening partner, Nancy explores real system trade-offs, verifies architectural knowledge, and generates comprehensive technical dossiers.

Key Technical Features:
24/7 Assessment AvailabilityEngineers can complete in-depth technical evaluations at their convenience, dramatically accelerating recruitment cycle times.
Structured Technical QuestionsEvaluations follow standardized, domain-calibrated question trees tailored specifically to the engineering domain.
Role-Specific EvaluationAdapts intelligently to specific disciplines—from CANbus timing issues in automotive to timing closure in VLSI.
Objective Technical ScorecardsGenerates structured candidate reports highlighting demonstrated strengths, conceptual gaps, and verifiable project experience.
Consistent Evaluation FrameworkEnsures every candidate is evaluated against identical, objective technical standards without interviewer fatigue or bias.
Scalable Assessment ThroughputCapable of evaluating hundreds of engineering profiles concurrently without depleting internal engineering hours.
AI CODING EVALUATOR

Alex

Alex supports structured evaluation of software-engineering capability through coding and technical problem-solving assessments. Rather than generic textbook trivia, Alex evaluates how engineers write, test, optimize, and debug code for real-world production environments.

Key Technical Features:
Real-World Problem ScenariosSimulates practical engineering challenges such as buffer overflow prevention, concurrency management, and API design.
Multi-Language SupportEvaluates production-grade C, C++, Rust, Python, Go, TypeScript, and SystemVerilog constructs.
Edge-Case & Performance ProfilingTests memory efficiency, execution time complexity, and defensive exception handling.
Plagiarism & Code Integrity DetectionMonitors original problem-solving pathways and detects automated script submissions.

Accelerate Technical Screening