Create technical roles, collect applicants from multiple sources, review evidence-based scorecards, prepare interview kits and capture interview outcomes — while the final decision stays with your team.
Synthetic demo available now · MVP access by waitlist
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Walk through a synthetic hiring flow: create a role with AI, review an evidence-based candidate scorecard, generate an interview kit, record post-interview feedback and explore source quality analytics.
Demo data only. No real candidate data is stored or processed.
Demo workflow
Available nowSmall and mid-sized tech teams do not always need another heavy ATS. They need a simple way to review applicants, see proven skills, understand risks and keep interview findings in one place.
Email, web forms, communities, LinkedIn, referrals, messengers and manual imports quickly become separate applicant streams. The team loses the full picture.
Recruiters and team leads spend hours reading resumes, trying to separate real technical signals from keywords.
Without a technical review before the interview, teams often discover basic mismatches during the call instead of validating the most important risks.
After the interview, notes often stay in chat messages, call notes or personal documents. The team loses track of what was actually confirmed.
Six clear steps — create a role, collect applicants, understand technical evidence, prepare the interview and keep the final decision with your team.
Start with rough notes. AI turns them into a structured job description, highlights missing details and prepares a technical scorecard for the role.
Approve the job description and get source-specific posts, a universal apply link and ready-to-copy text for your channels.
Applicants from web forms, email, communities, LinkedIn, messengers and manual import flow into one candidate inbox.
AI parses CVs and applications into strengths, gaps, risks, confidence and scorecard breakdowns — every important conclusion includes evidence.
Team leads get focused questions, risk checks, practical task ideas and strong-answer signals tailored to the candidate and role.
After the interview, add notes and the team decision. AI updates candidate signals and remaining risks without making the final hiring decision.
Not a generic HR dashboard. TechHireHQ helps your team move from rough role notes to evidence-based candidate analysis, interview preparation and post-interview outcomes.
Raw job idea
"We need a Frontend Developer for a B2B SaaS product..."
AI output
Missing details
Nina Roberts
Frontend Developer · React / TypeScript / Next.js
Strong React production experience
9/10Evidence: candidate describes production dashboards with React, TypeScript and complex forms.
Gap
Next.js App Router is not clearly confirmed.
Nina looks like a strong potential fit. The interview should verify Next.js depth, testing experience and architecture ownership.
How would you structure data fetching in Next.js App Router?
SkillValidates practical Next.js depth beyond keyword mentions.
What testing strategy would you use for a critical form flow?
GapTests whether limited testing evidence hides real experience.
Describe a complex frontend feature you owned end-to-end.
RiskHelps verify architecture ownership and decision-making depth.
Practical task
Design a dashboard page with a filterable list, form validation and API loading/error states.
Team decision
Technical interview passed
Frontend Team LeadProfile update
Before: No clear evidence for Next.js App Router
After: Practical understanding confirmed during interview
AI profile update summary
Create technical roles, collect applicants, review evidence-based candidate analysis, prepare interviews and capture the outcomes in one place.
Turn rough notes into a job description, role scorecard, apply link and ready-to-post texts.
Collect applicants from different channels in one structured inbox.
Extract skills, experience, projects, tools and technical signals from CVs and application answers.
See how each candidate maps to every role requirement — skill by skill.
Strengths, gaps, risks and confidence are backed by concrete data from the application.
Questions by skill and gap, risks to verify, a practical task idea and strong-answer signals.
Interviewer notes complement the profile with confirmed skills, remaining risks and the interview outcome.
See which channels bring strong candidates and candidates who pass technical interviews — not just application volume.
TechHireHQ brings applicant collection, evidence-backed technical analysis, interview preparation, interview outcomes and source quality analytics into one workflow. AI explains the findings, while your team makes the final decision.
General-purpose AI hiring tools
Broad hiring workflows across different roles
TechHireHQ
Technical hiring for IT and engineering teams
General-purpose AI hiring tools
General scoring and requirement matching
TechHireHQ
Skill-based scorecards with evidence, gaps and risks
General-purpose AI hiring tools
General questions and templates
TechHireHQ
Questions tailored to the role, candidate and identified gaps
General-purpose AI hiring tools
Notes and feedback are usually stored separately
TechHireHQ
Interview outcomes refine confirmed skills and remaining risks
General-purpose AI hiring tools
Helps process and rank applicants
TechHireHQ
Explains findings while the team makes the final decision
General-purpose AI hiring tools
Application volume and conversion
TechHireHQ
Candidate quality and technical interview outcomes
Application volume alone does not show source quality. Compare strong-fit rate, average technical score and interview outcomes for each source.
Community
Highest-quality source
47%
Strong-fit rate
1
Candidates passed technical interview
89/100
Source quality score
Source quality overview
Illustrative data for one technical hiring cycle
The source quality score is an illustrative combined score based on strong-fit rate, average technical score and interview outcomes.
Community
89/100Applicants
19
Strong-fit rate
47% · 9
Avg technical score
82/100
Interview outcome
1 of 6 passed
Applicants
42
Strong-fit rate
26% · 11
Avg technical score
76/100
Interview outcome
0 of 5 passed
Career page
68/100Applicants
13
Strong-fit rate
31% · 4
Avg technical score
73/100
Interview outcome
0 of 2 passed
Applicants
12
Strong-fit rate
25% · 3
Avg technical score
71/100
Interview outcome
0 of 2 passed
Web form
61/100Applicants
23
Strong-fit rate
22% · 5
Avg technical score
69/100
Interview outcome
0 of 2 passed
Telegram
58/100Applicants
58
Strong-fit rate
14% · 8
Avg technical score
64/100
Interview outcome
0 of 3 passed
| Source | Applicants | Strong-fit rate | Avg technical score | Interview outcome | Source quality score |
|---|---|---|---|---|---|
| Community | 19 | 47% · 9 | 82/100 | 1 of 6 passed | 89/100 |
| 42 | 26% · 11 | 76/100 | 0 of 5 passed | 72/100 | |
| Career page | 13 | 31% · 4 | 73/100 | 0 of 2 passed | 68/100 |
| 12 | 25% · 3 | 71/100 | 0 of 2 passed | 65/100 | |
| Web form | 23 | 22% · 5 | 69/100 | 0 of 2 passed | 61/100 |
| Telegram | 58 | 14% · 8 | 64/100 | 0 of 3 passed | 58/100 |
AI insight
In this demo hiring cycle, Community brought fewer applicants than Telegram or LinkedIn, but had the highest strong-fit rate and produced the candidate who passed the technical interview.
Candidate data is sensitive. TechHireHQ is being designed around team oversight, data minimization, clear candidate consent and secure production architecture.
AI explains role fit, evidence, gaps and risks. It does not automatically approve or reject candidates.
Important conclusions must be backed by CV, application or interview evidence.
The product should collect only the data that is needed for hiring workflows and user communication.
The production plan includes server-side validation, role-based access, safe file handling, rate limits, audit logs and protected secrets.
Candidate-facing flows will clearly explain how data is used and provide a way to request its deletion.
Candidate data will be available only to authorized workspace members, with sensitive actions recorded in an audit log.
We are onboarding early teams gradually. Tell us about your hiring context and we'll prioritize your access.