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A US-based technology enterprise with over 1,000 employees reached a recruitment scaling inflection point: engineering applications had grown to 1,500–3,000 per month, while hiring targets increased to 120–200 engineers annually. Senior engineers were spending 200–400 hours monthly reviewing test assignments, and fragmented tools across sourcing, scheduling, and evaluation were causing response delays exceeding 24 hours. The result was rising hiring costs, slower cycle times, and growing risk of missed high-quality candidates.
Codebridge was engaged to design and deliver a production-grade, AI-assisted recruitment platform that would augment — not replace — human decision-making. The mandate was clear: automate early-stage screening, technical validation, and structured interview synthesis while preserving human-in-the-loop control at all final decision points. The system needed to integrate with existing HR workflows without requiring a full ATS replacement.
Over a 3-month engagement, a dedicated 5-person Codebridge team delivered a scalable multi-agent platform built on LangGraph and LangChain. The system unified data from 20+ sourcing channels, implemented structured technical test evaluation with confidence-based routing, and introduced AI-assisted interview synthesis grounded in internal hiring standards.
As a result, full-cycle hiring time decreased from 24 days to approximately 10–12 days, manual engineering test review workload dropped by 60% (saving 200–300 hours per month), and candidate response time was reduced to under 2 minutes. The system achieved break-even within the first year of operation and has been operating in production without critical disruptions since launch.
The client is a prominent American technology company with an engineering-heavy culture and over a thousand employees. The business was scaling aggressively, with demand for new engineering hires outpacing the HR team's capacity to process applicant volume. All details are anonymized under NDA.
The company operated several disconnected tools: an ATS for tracking, Calendly for scheduling, Fireflies for call recording, and LinkedIn Recruiter for sourcing. The absence of a unified platform created information silos: recruiters lacked full candidate context in one place, and response times regularly exceeded 24 hours — long enough to lose top candidates to competitors.
Before the project began,recruitment had systemic bottlenecks at every stage of the funnel. A detailedprocess audit uncovered five root-cause problems.
Existing auto-screening toolsrelied on keyword matching. Candidates had learned to circumvent this byembedding relevant terms in PDF documents using invisible text (white text on awhite background). The result: the ATS passed unqualified candidates andrejected strong ones — a fundamental breakdown in screening accuracy.
Real Audit Finding
During the pre-project audit, over 12% of applications contained hidden keyword stuffing. A significant portion were for roles where candidates lacked even baseline qualifications — yet they passed the initial automated filter.
Senior designers and engineerswere spending 200 to 400 hours per month manually reviewing early-stage testassignments. This represented a direct productivity drain on the company's mostexpensive specialists — people who should have been building product, notreviewing code submissions from candidates who hadn't yet been properlyscreened.
Direct cost calculation: 250 hours/month x $120/hour = $30,000/month. Annualized:$360,000/year lost to manual review alone.
Recruiters gravitated towardcandidates with flawless credentials and elite university backgrounds,systematically overlooking candidates with non-traditional profiles but strongpractical skills. This narrowed the talent pool, introduced structural bias,and led to missed hires who would have performed exceptionally.
Candidate data lived indisconnected systems: LinkedIn, job boards, email threads, ATS records, andCalendly. Recruiters had to manually aggregate information before everyinterview. The 24-hour average response time put the company at direct risk oflosing top-tier candidates to competitors who moved faster.
None of the existing tools couldevaluate resilience, judgment, decision-making style, or cultural fit. Thisfailure cascaded to the bottom of the funnel: an interview-to-offer ratio ofjust 12%, meaning 88% of final-stage interviews ended in rejection —identifying mismatches that could have been caught weeks earlier.
The system's core is a centralOrchestrator Agent built on LangGraph — a library for stateful agent workflowmanagement with native support for conditional transitions, retries, andobservability. The orchestrator coordinates five specialized agents, eachresponsible for a distinct stage of the funnel.
Agent Architecture:
• Intent Detection Agent —analyzes application relevance and classifies each candidate by a proprietaryRelevance Index based on career progression patterns, not just keywordpresence.
• Screening Agent —automatically validates CV fit against role requirements, grounded in thecompany's internal hiring standards via RAG to prevent hallucinated feedback.
• Assessment Agent —generates personalized test assignments with embedded marker questions designedto detect AI-generated submissions and reveal genuine problem-solvingcapability.
• Interview Agent —synthesizes call transcripts from Fireflies.ai, analyzing tone, speechpatterns, and response consistency to build a structured psychological profileof the candidate.
• Onboarding Agent — createspersonalized Just-in-Time learning paths for new hires based on ingestedConfluence documentation, role requirements, and the hire's technical profile.
The 90% Confidence Threshold
Agents make autonomous decisions only when confidence exceeds 90%. Borderline cases are automatically escalated to human recruiters. Final-stage candidates are never rejected autonomously — that decision always remains with a person.
The system aggregates data from20+ sources into a single unified candidate profile: LinkedIn, Jooble, Indeed,Stack Overflow Jobs, GitHub, Behance (for designers), the corporate careerspage, and others. The Intent Detection Agent evaluates every profile acrossthree dimensions:
• Technical fit: hard skills,technology stack alignment, depth of hands-on experience.
• Career progression: is thiscandidate growing in their field? What scope of projects have they led orcontributed to?
• Soft signals: open-sourcecontributions, public speaking, published writing — indicators of initiative,depth, and intellectual curiosity that keyword tools miss entirely.
The Relevance Index — aproprietary score from 0 to 100 — allows direct comparison of candidates fromdifferent sources on a single scale. Weighting criteria adapt in real timebased on seniority level (Junior, Middle, Senior, or Lead), giving HR leads controlover business logic without requiring engineering changes.
One of the most technicallyinnovative components of the system is the Protection Layer, designed to detectboth hidden keyword stuffing in CVs and LLM-generated responses in testassignments. This addressed a widespread problem that no existing tool in theclient's stack could handle.
Detection Methods:
• Document metadata analysis:creation timestamps, authoring software, font anomalies, and invisible-layerdetection.
• Statistical text analysis:perplexity and burstiness scores — metrics by which AI-generated text differsmeasurably from human writing.
• Marker questions: taskelements specifically designed to require contextual reasoning and practicalintuition that an LLM without domain understanding cannot reproduce reliably.
• Cross-section stylecomparison: detecting inconsistencies in writing style across different partsof a submission — a strong signal of patchwork LLM generation.
Test assignments are generateddynamically and personalized: the system factors in the technology stack listedin the candidate's CV, the seniority level of the role, and real problemcontexts from the company's own codebase (surfaced via RAG). This makescopy-paste of generic internet solutions ineffective.
Validation Against Senior Engineers
Before production launch, all historical test tasks were re-graded manually. AI scores were compared against senior engineer scores across the same submissions. Agreement rate observed: approximately 90%. This validated system reliability and minimized the risk of unfair rejection of qualified candidates.
Following integration withFireflies.ai (or equivalent meeting recorder), the Interview Agent receives thetranscript of every candidate call and generates a structured debrief report —available in the Recruiter Dashboard before any human reviews the recording.
What the Agent Analyzes:
• Answer content: technicaldepth, accuracy, clarity of reasoning, and alignment with role requirements.
• Speech patterns: confidenceindicators, hesitation markers, tone consistency — behavioral signalscorrelated with resilience and stress tolerance.
• Mimicry and adaptability:does the candidate adjust their communication style to context? A signal ofemotional intelligence and team fit.
• Red flags: contradictionsbetween CV claims and interview answers, evasiveness around specific topics,inconsistent technical claims.
The output is a structuredpsychological portrait of the candidate, rendered in the Recruiter Dashboardalongside the technical assessment summary. Recruiters arrive at everyfinal-stage conversation with full context and a clear, evidence-backed perspectiveon each candidate's strengths and risks.
The system extends beyond the hiredecision. Once an offer is signed, the Onboarding Agent automatically activatesand begins preparing the new hire's ramp-up experience:
• Ingests currentdocumentation from Confluence: architecture docs, team wikis, coding standards,and internal tooling guides.
• Builds a personalizedJust-in-Time learning path based on the new hire's technical profile,seniority, and assigned team.
• Generates a first-weekstarter assignment tailored to the company's actual tech stack.
• Compiles a role-specificFAQ drawn from the most common questions asked by previous new hires in similarpositions.
This reduces time-to-productivity — the period beforea new engineer begins making meaningful independent contributions. Internalestimates project onboarding acceleration of 20 to 30% compared to thecompany's prior standard process.
A core architectural decision ishierarchical LLM usage based on task complexity — routing work to the smallestmodel that can handle it reliably:
• Small / fast models: syntaxchecking, basic candidate classification, routing decisions between agents.
• Mid-tier models: CVscreening, response letter generation, standard test task analysis.
• Heavy models (GPT-4, ClaudeOpus, Gemini Ultra): code architecture analysis, psychological portraitsynthesis, full interview debrief generation.
The result is a 40% reduction inLLM operating costs compared to a naive approach of routing all tasks throughthe most capable (and most expensive) model. Cost per evaluated candidate:$1.50 to $3.00. At 2,000 candidates per month, total monthly LLM spend runs$3,000 to $6,000.
Every agent is grounded viaRetrieval-Augmented Generation on the company's internal knowledge base:technical requirements by role, hiring standards, annotated examples of strongand weak candidate responses. This eliminates hallucinated feedback — caseswhere AI invents evaluation criteria that do not exist in the company's actualprocess — which was a critical requirement for the client's trust inAI-generated outputs.
The React frontend givesrecruiters complete candidate context in a single interface, purpose-built tosupport decision-making rather than information retrieval:
• Aggregated candidateprofile from all sources, with Relevance Index score and source breakdown.
• Test assignment summarywith the agent's scoring rationale explained in plain language.
• Psychological portrait frominterview analysis, structured by dimension.
• Risk heatmap: AI cheatingsignals, credential-to-interview mismatches, red flags from transcriptanalysis.
• One-click actions: advancethe candidate, escalate to senior reviewer, or flag for further human review.
Critically, the Dashboard surfacesnot just the agent's decision but its chain-of-thought reasoning. Recruitersalways understand why the system reached a given conclusion. This transparencywas a deliberate design principle: it builds warranted trust in the AI outputsand enables confident human override when needed.
The project was executed by adedicated team of five specialists over three months. Each role was scoped to aspecific technical challenge within the system.
The increase in Interview-to-OfferRatio from 12% to 38% is the most telling quality indicator. It means thesystem is far more effective at identifying fit earlier in the funnel — beforecandidates reach the final interview stage. Hiring managers now spend theirtime exclusively on candidates who have already been validated acrosstechnical, psychological, and cultural dimensions.
In parallel, the rate of bad hiresdecreased significantly. Each incorrect hire carries a hidden cost estimated atthree or more months of fully-loaded salary: onboarding time, managerattention, re-recruitment, and lost team productivity. Preventing even five badhires per year delivers $150,000 to $300,000 in avoided costs — independentlyof the operational savings.
The system delivered on the"25 squared" strategy: a 25% increase in candidate throughputcapacity alongside a 25% reduction in administrative overhead. Recruiters movedup the value stack — from manually processing applications and checking testsubmissions to strategic relationship-building with top talent and high-intentcandidate engagement.
When extended across all business units, the estimated recruiter time savings reach 1.5 million hours annually. Even at conservative utilization assumptions, this represents tens of millions of dollars in freed productivity across the organization.
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