AI Sourcing helps you find high-quality candidates quickly by combining an AI-generated Ideal Candidate Profile (ICP) with fast feedback loops. The loop is simple: start a search → rate candidates → refine → shortlist.
- Kick off search by providing one of the following: Job Description, Job Post link or a description of your ideal candidate
- The sourcing agent generates an Ideal Candidate Profile (ICP) for you. Review it and edit before you start rating candidates.
- The agent searches databases and finds candidates that match your ICP.
- Give feedback on at least 5 candidates using
Yes/ Maybe/ No. This feedback is how the system learns.
- Click
Refine search to improve results. Expect 2–3 refinement cycles to get dialled in.
- Saved candidates appear in the
Candidates tab (top right).
See our help documentation for more details on this process.
Improve quality faster
Ensure you have a strong ICP
Treat your ICP as a living document — refine it as you learn. Here is a helpful checklist:
- Must-have skills are specific (tools, systems, domain knowledge)
- Seniority is clearly defined (what “senior” means for this role)
- Company context is included (stage, size, pace, constraints)
- Nice-to-haves are separated from must-haves
- Exclusions are explicit (who you don’t want to see)
Give feedback that’s easy to learn from
The fastest way to improve results is to make “No” feedback specific.
| 👍 Good feedback examples | 👎 Common mistakes to avoid |
|---|
| “Too junior for the scope” | Rating too few candidates (aim for 5+ before refining) |
| “Missing X (e.g. Kubernetes / enterprise selling / payroll domain)” | Giving vague “No” feedback with no explanation |
| “Only enterprise background — we want Series A–C experience” | Judging results after the first batch (expect iteration) |
| “Wrong function (product marketing vs growth)” | Forgetting to update the ICP as patterns emerge |
Advanced prompting techniques
AI Sourcing works best when you describe the context of the role, not just the job title. Think about where this person is coming from, what they’ve done before, and why they’re a fit for your company.
| ❌ Don’t use basic keywords | ✅ Be specific & contextual |
|---|
| Why it’s a problem: Job titles alone don’t give enough signal, so results tend to be generic. | Why it works: Context tells the AI what “good” looks like for this role and your company. |
| Example: “Senior Software Engineer” | Example: “Senior Software Engineer for our Series B fintech startup, with backend scaling experience at high-growth companies. Comfortable with hands-on coding and technical leadership as we grow from 50 to 200+ employees.” |
| Missing info: domain, seniority definition, company stage, scope, constraints | Include: stage + domain + scope + constraints (and 1–2 must-haves) |
Pro tip: Upload a resume of a top performer and ask: “Find candidates similar to this person, but with more fintech experience and leadership background.”
Include strategic exclusions
Exclusions help the AI avoid “close but wrong” profiles. Call out the backgrounds you don’t want to see to tighten results faster.
- Marketing example: “Growth marketing manager with B2B SaaS experience, but not pure brand marketers”
- Data example: “ML engineer with production deployment experience, but not purely academic researchers”
Add company context
Company context explains what it’s like to work at your company (industry, size, stage, and pace) so the AI can prioritise candidates with relevant experience.
- Stage: Define “early-stage startup” vs “scale-up” vs “enterprise”
- Work style: “Remote-first culture requiring async communication skills”
- Team: “Small team where everyone wears multiple hats”
- Growth: “Rapid scaling phase, need someone comfortable with ambiguity”
Prompt templates
These example templates show how to combine role requirements, company context, and exclusions into a clear sourcing prompt.
Technical role template
[Role] for a [company stage] [industry] company, with [X years] experience in [technologies/domains]. Must have: [must-have requirements]. Nice to have: [nice-to-haves]. Exclude: [clear exclusions]. Must be comfortable with: [work environment / culture].
Leadership role template
[Role] who has scaled [function/team] from [X to Y], with experience in [industry or domain]. Must have: [leadership scope, change management, hiring/structure]. Exclude: [wrong background]. Looking for someone who leads with: [leadership style].
Advanced features you can use
Once you’ve calibrated your search, AI Sourcing can help you analyse and share your candidate set. Try asking:
- “Show me a breakdown of candidates by experience level”
- “Create a chart of candidates by previous company size”
- “Export this candidate list as a CSV for my hiring manager”
- “Visualize the geographic distribution of these candidates”
Quick troubleshooting
When results aren’t great yet, try running this loop:
- Rate at least 5 candidates
- Add a reason for every
No feedback
- Update the ICP (missing must-haves, clarify exclusions)
- Click
Refine search
- Repeat for 2–3 cycles
Benchmarks
Use these benchmarks to know when your search is calibrated:
- Hit rate: 50–75% of candidates are “Yes” or “Maybe”
- Calibration speed: 2–3 refinement cycles to reach quality matches
- Outreach-ready: ~80% of “Yes” candidates are worth contacting
- Time to quality: find strong candidates within ~15 minutes
