Via TechTarget: Case study: GenAI saves time for Landing Point's recruiters
Landing Point wanted recruiters spending more time with clients and candidates, not wrestling resumes into shape. In an October 2, 2025 TechTarget interview, Landing Point lead AI engineer Faizel Khan described the administrative friction inside the New York executive-search firm: resume formatting, candidate biographies, contracts and related documents consumed time that could have gone into relationships and better matches.
The data made the problem more delicate. Khan said Landing Point held personal information on thousands of candidates, alongside client notes and other unstructured records. The firm had an applicant tracking system with some structured data, but much of the material needed for new tools was unstructured. Before Khan joined, he said there was no internal IT person able to govern and model that data. Landing Point hired people to label it and turn it into structured data before the tools could use it.
This was not a generic “add AI” exercise. Landing Point has fewer than 100 employees, according to Khan, so scale was not the first constraint. Speed and governance were. The firm needed outputs quickly while keeping client and candidate information private. A public chatbot pasted onto the side of the workflow would have been easy. It would also have missed the point rather spectacularly.
Landing Point is the featured organization in this independent TechTarget case study. It is not presented as an AgentsROI client, and the outcomes below were reported by Landing Point through Khan's interview.
Khan's team built the tools behind Landing Point's existing applicant tracking system rather than asking recruiters to adopt another application. The firm used AWS virtual private cloud infrastructure for data and applications, fine-tuned different large language models for different use cases, and selected models by output quality. Recruiters could format resumes, create job material and draft documents from the workspace they already used.
The controls were part of the architecture. Khan described role-based access, private infrastructure, zero data retention, extensive prompt validation and mandatory recruiter review before anything left the system. Landing Point also built a private chat application on an open-source model for content that used client or candidate information. Tools came to the users; sensitive work did not have to wander off in search of a clever browser tab.
That fit helped adoption. The first resume formatter addressed friction recruiters felt every day, and the time saving motivated independent use. Khan said the project started with the end user's pain rather than a technology looking for somewhere to sit.
Landing Point reported at least four hours returned to every recruiter each week. Khan also reported that its job-formatting tool enabled at least 10 times the number of jobs the firm previously produced, alongside faster time-to-placement. Those are Landing Point-reported operational outcomes quoted by TechTarget; Khan said the firm did not have a formula translating them into financial ROI.
The combination matters more than either half alone. Landing Point increased tempo while keeping human review and controlled access in the workflow. It did not claim that automation replaced recruiter judgment. It moved repetitive preparation into governed infrastructure so recruiters could spend more time on clients, candidates and matching—the work for which relationships still matter.
Start with a Workflow ROI Audit. Map where recruiters lose expensive hours, measure the baseline and rank use cases by return and risk. Landing Point chose daily administrative friction and tracked time and throughput rather than inventing a grand financial formula. That is a useful standard: know what changed before buying more tools.
Pair that work with a Shadow-AI Risk Assessment & AI Governance Audit where candidate and client information is involved. Inventory what staff already use, trace where data goes and define access, retention and human-review rules. Then use Managed AI Operations to keep models, prompts, controls and performance under review. Landing Point had a lead AI engineer doing this work. Firms without one can hire the operating discipline rather than pretending software will govern itself.
AgentsROI did not deliver Landing Point's project and does not claim its results. The practical offer is to help another owner-led recruiting firm capture a similar class of opportunity on its own facts: identify the bottleneck, protect the data, select the right model and run the system after launch. Book a no-pressure assessment to find out whether the hours are real before committing to a build.
Credit: This article independently summarizes TechTarget's reporting and its interview with Faizel Khan. Landing Point is the featured organization and the source of the reported operational outcomes.
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