Find the people your team would miss.
Agents build role-specific markets, surface adjacent profiles, and keep outreach tied to the traits that actually predict fit.
10 to 200 without hiring drift
hireburst
Custom AI agents help you decide who to target, how to interview them, and what to adjust after every conversation without losing consistency.
target
live
interview
set
calibrate
tight
10
Start with the team you have and the roles that matter next.
200
Scale without every interview becoming a new folklore system.
1
Keep one consistent signal loop while every role gets sharper.
Sourcing agent
Market map
Interview architect
Signal calibrator
Rubric keeper
Feedback loop
Sourcing agent
Market map
Interview architect
Signal calibrator
Rubric keeper
Feedback loop
One operating system for the entire hiring push.
hireburst gives each role its own agent stack while keeping the company-wide bar, language, and evidence model aligned.
Turn every conversation into comparable evidence.
Question paths adapt to the candidate while the rubric, evidence standards, and debrief structure stay consistent.
Improve the loop after each interview.
Signals roll back into targeting, question design, and calibration so the system gets sharper without rewriting the rules.
Built for hiring velocity with grown-up control.
Wild speed on the outside, disciplined operating logic underneath: agents, maps, interviews, scorecards, and loops all moving together.
Scale
Grow without losing the bar.
Keep each new role attached to a clear talent thesis and interview standard.
Customize
Give every role its own agent mix.
Adjust sourcing, screening, interview design, and calibration by role stage.
Reach
Move faster than the obvious list.
Search strategy reacts to evidence instead of becoming stale after week one.
Explore the system.
Custom agents, target maps, interview architecture, the learning loop, developer APIs, and a live account-manager example.
Agents
Custom agent stacks for sourcing, screening, interviewing, and calibration.
Targeting
Decide who to pursue and why before the market gets noisy.
Interviews
Adaptive conversations that still produce comparable evidence.
Learning
Improve after each interview while keeping the same decision language.
Example
Run an Account Manager hiring scenario for a growing AI real estate company.
Developer APIs
Use MCP for agent workflows or REST for apps, dashboards, and integrations.
Choose the API surface that fits the thing you are building.
The same hireburst intelligence is available as agent tools or normal HTTP resources.
AGI-pill the hiring workflow.
Use MCP when an agent should decide the next hiring move, call hireburst tools, and feed role plans, target maps, interview loops, and calibration updates into the rest of your agent stack.
Read MCP docsBuild apps and integrations.
Use REST for dashboards, ATS handoffs, internal tools, approvals, and data syncs. It uses bearer tokens and exposes the same artifacts as straightforward JSON resources.
Read REST docsQuestions hiring leaders ask before they add AI to the loop.
hireburst is built for leverage without handing the decision away.
Does hireburst rank candidates automatically?
No. hireburst organizes evidence, suggests target pools, and helps keep interviews consistent while humans own hiring decisions.
What teams is hireburst built for?
hireburst is built for companies scaling from roughly 10 to 200 people, especially teams that need speed without losing interview consistency.
How does the system adjust after interviews?
Interview evidence feeds the next targeting move, question path, and calibration check while the role scorecard and decision standard stay stable.
Can hireburst support specialized roles?
Yes. Each role gets a custom agent stack for sourcing, interview design, scorecards, and learning loops based on the company stage and role pressure.
burst with a backbone
Turn the next hiring push into a system you can trust.
hireburst keeps your team fast, targeted, and consistent as the org chart expands from a tight crew into a real operating machine.