Interview Agent
Interview Agent
Interview Agent
Real-time AI Copilot for Technical Hiring
AIcopilotthathelpsnon-technicalrecruitersconfidentlyassesstechnicalcandidatesduringliveinterviewstomakebetterhiringdecisions,quicker.
Areal-timeinterviewcopilotfornon-techrecruiterstoconfidentlyassesstechnicalcandidatesandmakebetterhiringdecisions,quicker.
AIcopilotthathelpsnon-technicalrecruitersconfidentlyassesstechnicalcandidatesduringliveinterviewstomakebetterhiringdecisions,quicker.
CHALLENGE
Non-technical recruiters screening specialized tech roles often lack the expertise to fully validate candidate responses, leading to unqualified candidates reaching hiring managers. This increases interview hours, slows hiring, produces inconsistent evaluations, and adds friction between recruiting and engineering teams.
Non-Technical Recruiters: Need guidance on follow-ups, evaluating technical answers, and making informed hiring decisions.
Technical Hiring Managers: Want fewer, higher-quality interviews with stronger signals before committing their time.
Non-technical recruiters screening specialized tech roles often lack the expertise to fully validate candidate responses, leading to unqualified candidates reaching hiring managers. This increases interview hours, slows hiring, produces inconsistent evaluations, and adds friction between recruiting and engineering teams.
Non-Technical Recruiters: Need guidance on follow-ups, evaluating technical answers, and making informed hiring decisions.
Technical Hiring Managers: Want fewer, higher-quality interviews with stronger signals before committing their time.
Non-technical recruiters screening specialized tech roles often lack the expertise to fully validate candidate responses, leading to unqualified candidates reaching hiring managers. This increases interview hours, slows hiring, produces inconsistent evaluations, and adds friction between recruiting and engineering teams.
Non-Technical Recruiters: Need guidance on follow-ups, evaluating technical answers, and making informed hiring decisions.
Technical Hiring Managers: Want fewer, higher-quality interviews with stronger signals before committing their time.
Commercial real estate loan origination is document-heavy, high-risk, and hard to scale. Loan originators and underwriters spend hours per deal:
Chasing missing or outdated documents
Reconciling inconsistencies across records
Repeating manual checks under tight timelines and regulatory pressure
As deal volume grows:
Per-loan costs increase, often requiring more analysts or underwriters
Mistakes can lead to rework, delayed decisions, or financial risk
SOLUTION
An AI copilot that guides recruiters with pre-interview preparation, provides real-time follow-ups based on live conversation context, and generates unbiased, data-driven post-interview analysis, accelerating time-to-hire of top candidates from the start.
A coordinated workforce of AI agents, each handling a specific task, replicates and improves the loan origination workflow. Together, they streamline manual processes, verify key information, execute actions, and deliver detailed deal insights to guide officers’ decisions.
My Role
Owned product vision, defined AI capabilities, and set strategic direction to transform technical hiring.
Led key trade-offs across LLM performance, latency, and guardrails to ensure reliable real-time interview support.
Directed cross-functional execution, stakeholder alignment, and enterprise adoption of a responsible AI solution.
Outcomes
Outcomes
Assisted Fusemachine's 100% initial technical interviews
Assisted Fusemachine's 100% initial technical interviews
Assisted Fusemachine's 100% initial technical interviews
Assisted Fusemachine's 100% initial technical interviews
Reduced time spent interviewing unqualified candidates
Reduced time spent interviewing unqualified candidates
Reduced time spent interviewing unqualified candidates
Reduced time spent interviewing unqualified candidates
Lowered false positives reaching technical rounds
Lowered false positives reaching technical rounds
Lowered false positives reaching technical rounds
Lowered false positives reaching technical rounds
How it works
How it works
PREPARATION BEFORE INTERVIEW
BEFORE INTERVIEW
Upload a job description to generate relevant technical questions, key competencies, and probing guidance
LIVE-GUIDANCE DURING INTERVIEW
DURING INTERVIEW
AI joins the calls and guides recruiters with contextual follow-up questions in real-time.
POST interview ANALYSIS
POST interview
Get structured unbiased candidate analysis with interactive chat for better hiring-decision.
Key Decisions I made
Key Decisions I made
Guardrails & LLM prompts for fast, context-aware, reliable AI interview guidance
Impact: Accurate, relevant follow-ups were delivered in under 1.5 seconds, preserving natural interview flow and producing structured, trusted evaluations for technical leaders.
Impact: Accurate, relevant follow-ups were delivered in under 1.5 seconds, preserving natural interview flow and producing structured, trusted evaluations for technical leaders.
Impact: Accurate, relevant follow-ups were delivered in under 1.5 seconds, preserving natural interview flow and producing structured, trusted evaluations for technical leaders.
One-click connection to calls via browser extension
Impact: Near-instant connection removed setup friction, allowing recruiters to use the AI immediately and driving rapid adoption.
Impact: Near-instant connection removed setup friction, allowing recruiters to use the AI immediately and driving rapid adoption.
Impact: Near-instant connection removed setup friction, allowing recruiters to use the AI immediately and driving rapid adoption.
Chatbot for interactive post-interview review
Impact: Enabled deeper post-interview analysis, reducing ambiguity and improving confidence in final hiring decisions.
Impact: Enabled deeper post-interview analysis, reducing ambiguity and improving confidence in final hiring decisions.
Impact: Enabled deeper post-interview analysis, reducing ambiguity and improving confidence in final hiring decisions.
The Process
The Process
Discovery & Problem Framing
I immersed myself in technical hiring by interviewing recruiters, talent acquisition teams, and hiring managers across AI/ML and data roles. While the problem was initially framed as “better screening,” the real bottleneck emerged during live interviews.
Recruiters faced three key challenges:
Identifying the right technical questions and probing effectively
Interpreting candidate answers with confidence
Consistently assessing candidates across interviews
These gaps allowed unqualified candidates to advance, wasting senior engineers’ time, slowing projects, and creating thousands in lost productivity per hire. The core issue wasn’t lack of information, butit was decision uncertainty during live interviews, caused by inconsistent structure and variable probing.
Key decision: Shift the product focus from resume screening to live interview decision support, using the STAR framework to standardize how recruiters probe, assess, and evaluate candidates.
Impact: Interviews became more consistent, false positives dropped, senior time was saved, hiring accelerated for critical roles, and trust in early-stage decisions increased.
Discovery & Problem Framing
I immersed myself in technical hiring by interviewing recruiters, talent acquisition teams, and hiring managers across AI/ML and data roles. While the problem was initially framed as “better screening,” the real bottleneck emerged during live interviews.
Recruiters faced three key challenges:
Identifying the right technical questions and probing effectively
Interpreting candidate answers with confidence
Consistently assessing candidates across interviews
These gaps allowed unqualified candidates to advance, wasting senior engineers’ time, slowing projects, and creating thousands in lost productivity per hire. The core issue wasn’t lack of information, butit was decision uncertainty during live interviews, caused by inconsistent structure and variable probing.
Key decision: Shift the product focus from resume screening to live interview decision support, using the STAR framework to standardize how recruiters probe, assess, and evaluate candidates.
Impact: Interviews became more consistent, false positives dropped, senior time was saved, hiring accelerated for critical roles, and trust in early-stage decisions increased.
Discovery & Problem Framing
I immersed myself in technical hiring by interviewing recruiters, talent acquisition teams, and hiring managers across AI/ML and data roles. While the problem was initially framed as “better screening,” the real bottleneck emerged during live interviews.
Recruiters faced three key challenges:
Identifying the right technical questions and probing effectively
Interpreting candidate answers with confidence
Consistently assessing candidates across interviews
These gaps allowed unqualified candidates to advance, wasting senior engineers’ time, slowing projects, and creating thousands in lost productivity per hire. The core issue wasn’t lack of information, butit was decision uncertainty during live interviews, caused by inconsistent structure and variable probing.
Key decision: Shift the product focus from resume screening to live interview decision support, using the STAR framework to standardize how recruiters probe, assess, and evaluate candidates.
Impact: Interviews became more consistent, false positives dropped, senior time was saved, hiring accelerated for critical roles, and trust in early-stage decisions increased.
Discovery & Problem Framing
I immersed myself in technical hiring by interviewing recruiters, talent acquisition teams, and hiring managers across AI/ML and data roles. While the problem was initially framed as “better screening,” the real bottleneck emerged during live interviews.
Recruiters faced three key challenges:
Identifying the right technical questions and probing effectively
Interpreting candidate answers with confidence
Consistently assessing candidates across interviews
These gaps allowed unqualified candidates to advance, wasting senior engineers’ time, slowing projects, and creating thousands in lost productivity per hire. The core issue wasn’t lack of information, butit was decision uncertainty during live interviews, caused by inconsistent structure and variable probing.
Key decision: Shift the product focus from resume screening to live interview decision support, using the STAR framework to standardize how recruiters probe, assess, and evaluate candidates.
Impact: Interviews became more consistent, false positives dropped, senior time was saved, hiring accelerated for critical roles, and trust in early-stage decisions increased.
