AI-Augmented Tendering & Recruitment Operations
A specialist recruitment consultancy partnered with ELab to break their dependency on one senior person for tender writing. In a focused 4-day sprint, we delivered a functional AI tendering solution and successfully submitted a live tender. The engagement set a target of enabling 80% of tender completion by non-expert staff, with subsequent recruitment automation proposals projecting $41,000-$55,000 NZD in Year 1 returns.
Industry: Recruitment / Executive Search | Duration: 4+ months | Region: New Zealand
The consultancy had built a strong reputation through high-quality tender submissions, but the entire tendering process depended on one senior person. This created a capacity ceiling on how many opportunities the business could pursue, and a critical business continuity risk.
On the recruitment operations side, the team faced a database of 150,000 candidate profiles with fewer than 5% tagged with relevant skills, making searches inefficient. And 30-40% of job applications were from offshore candidates not eligible for NZ-based positions, creating a manual processing burden.
What We Did
AI Tendering Sprint
We delivered a functional AI tendering MVP in a focused 4-day sprint, combining solution development with hands-on knowledge and data transfer. The approach uses a two-stage prompting methodology: first for response structure, then for content refinement. A live tender was successfully drafted and submitted during the sprint.
Knowledge and Data Foundation and Process Design
We identified that AI tendering quality depends fundamentally on the quality of the underlying knowledge base and data sources. The follow-on work focused on building a structured repository of model answers and methodologies, categorised by government and private sector.
Recruitment Automation (Proposed)
We scoped AI-powered solutions for offshore candidate identification (automated screening based on phone number analysis) and skills tagging (AI-driven CV parsing against the client's taxonomy for 75,000+ onshore candidates).
Results
| Metric | Result |
|---|---|
| Tendering MVP delivery | 4 days from concept to submitted tender |
| Live tenders submitted using AI | 1 during sprint |
| Non-expert tender completion target | 80% of tender by non-expert staff |
| Projected Year 1 return (recruitment automation) | $41,000-$55,000 NZD |
The sprint-based delivery model proved the value of focused AI investment with rapid returns. The roadmap includes integrating the tendering AI with CRM for pipeline tracking, AI-assisted candidate matching using enriched skills data, and automated market intelligence reporting.
For the current implementation sequence, see AI for recruitment agencies and the guide to choosing the first recruitment AI workflow.
