AI Use Case
AI for Recruiting
Make recruiting faster and more consistent with structured screening, summaries, and workflow automation—built with approvals and auditability.
Problems
What’s slowing teams down
Common bottlenecks we see before AI workflows are implemented.
Too many candidates
Teams can’t review resumes quickly and consistently.
Inconsistent screening
Different reviewers apply different criteria.
Scheduling overhead
Back-and-forth scheduling slows hiring cycles.
Poor audit trail
Decisions and changes are hard to trace without structured logs.
Delivery
What we deliver
Implementation-ready modules designed for reliability, safety, and real operations.
Structured summaries
Extract key fields and generate consistent candidate summaries.
Transparent scoring
Use rubrics and explainable fields for fit evaluation.
Workflow automation
Route candidates and schedule interviews with clean handoff.
Approvals + audit
Keep changes controlled and auditable.
Deliverables
What you’ll get
Concrete outputs designed for predictable handoff and measurable improvements.
Screening workflow + scorecards
Structured summary and extraction pipeline
Routing and scheduling automation
Approval steps and audit logs
Optional policy Q&A grounding
Handoff documentation
Process
How we work
A pilot-first approach, with the quality and governance needed for production rollouts.
Define
Role criteria, rubric, and guardrails.
Build
Parsing, summaries, routing, scheduling.
Harden
Approvals, logs, and monitoring.
Stack
Suggested implementation stack
A practical stack we can adapt to your constraints and existing systems.
Automations
Example automations
A few workflows that usually deliver ROI quickly.
Resume ingestion and structured summaries
Candidate routing to hiring managers
Scheduling workflows and reminders
Policy Q&A for recruiting SOPs (optional)
Start Small
Start small in 7 days
Three pilot-friendly options that reduce risk and ship value fast. Choose one, share access, and we deliver a production-ready baseline.
Standard
AI delivery standard
Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.
Logging + tracing
Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.
Guardrails + safety
Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.
Evals + regression tests
Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.
Cost + latency controls
Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.
Documentation + handoff
Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.
Security-first integration
Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.
Pricing
Typical pricing ranges
We confirm scope before starting. These ranges help you plan a pilot versus a full rollout.
Pilot workflow: $900–$3,500
Integrated recruiting ops: $2,500–$9,500
Timelines
Delivery timelines
Common timelines for pilots and production hardening, depending on integrations and governance.
Pilot: 1–2 weeks
Expanded rollout: 2–4 weeks
Risks
Risks & mitigation
The failure modes we design for so reliability and trust stay high.
Fairness concerns
We use explicit rubrics, approvals, and human oversight—automation assists but doesn’t decide.
Unclear criteria
We define structured criteria and evaluation sets to keep scoring consistent.
AI Case Examples
Micro case studies (anonymous)
A few safe examples of outcomes we build for real operations—no client names, just results.
Secure Mobile Solution in Australian Defence Ecosystem
Problem: Secure data workflows were required in a regulated environment with strict access controls.
Solution: Hardened architecture with strict auth, encrypted storage, and audit-friendly engineering patterns.
Outcome: Deployed securely within a regulated ecosystem with clear handoff and operational guidance.
AI Knowledge Base Across 2,000+ Pages
Problem: Teams needed fast answers across long PDFs, but search was slow and results were inconsistent.
Solution: RAG with hybrid retrieval and reranking, plus grounded answers and safer fallback behavior.
Outcome: Reliable answers with <10s response times and measurable improvements on real queries.
Ops Automation with AI + n8n
Problem: Manual approvals and CRM syncing created delays and data inconsistencies across tools.
Solution: Event-driven automation with validation gates and AI-assisted classification where it improved routing.
Outcome: Reduced manual workload significantly with more reliable workflows and operator visibility.
Relevant Gigs
Start with a fixed-scope gig
Pick a gig to launch a pilot quickly with clear deliverables and timeline.
Compare
Decision guides
Quick comparisons to help you choose the right approach before building.
Related Services
Explore deeper implementations
When you need more depth than a pilot, these services cover full delivery.
Explore
More AI pages
Additional pillars and use cases to help you plan your roadmap.
FAQ
Frequently asked questions
Does this replace recruiters?
No. It accelerates screening and workflow steps, but decisions stay with humans. We design approvals and transparency.
How do you handle fairness?
We use explicit rubrics, limit automation scope, and keep humans in the loop for decisions.
Can it integrate with our ATS?
Often yes, depending on API access. We can also export structured outputs for batch import.
Can it schedule interviews?
Yes. Scheduling integration is a common workflow component.
How fast can we pilot this?
A pilot workflow typically ships in 1–2 weeks once criteria and access are agreed.
Do we own the workflow code?
Yes. Source code and handoff notes are included.
Want an AI pilot for your workflow?
Start with a fixed-scope gig or request a tailored implementation plan for your systems.