Neural Command, LLC

BACKGROUND

Introduce your business and what you do there.

I’m an HR manager at Applicants.io, a job applicant platform specializing in schema markup for job postings and job listing details according to Google’s requirements. We aggregate job applicants based on search data from Google, which helps our AI find better candidates for hiring teams.

OPPORTUNITY / CHALLENGE

What challenge were you trying to address with Neural Command, LLC?

We needed to aggregate job applicants directly instead of spending $20,000 per month on job platforms. We needed to understand what job seekers were searching for, control the data, and create an in-house ATS/AI system to help navigate and funnel the best applicants to our recruiters.It was a very niche and specific job. First, we had to understand Google’s JobPosting schema and then be able to trigger website jobs to get positioned in Google rankings. After that, we needed to create the funnel system using AI on the website backend.

SOLUTION

What was the scope of their involvement?

Neural Command, LLC handled the full technical and strategic scope of the project, from search architecture through applicant routing. They designed and implemented a complete job-listing infrastructure using Google JobPosting structured data, ensuring every job met eligibility, validation, and indexing requirements.

This included building programmatic job pages, schema governance rules, and feed-level controls so listings could be surfaced reliably in Google Jobs and organic search.

They also built the application funnel itself, integrating AI-based logic on the backend to screen applicants, classify intent and fit, and route candidates to the appropriate recruiters. The system functions as a custom in-house ATS, giving us full control over applicant data, scoring, and workflow without relying on external platforms.

In addition, Neural Command, LLC analyzed job-seeker search behavior to inform job titles, descriptions, and page structure, ensuring listings aligned with real search demand rather than assumptions. The final deliverable was a self-owned applicant acquisition and screening system that reduced paid spend, increased applicant quality, and gave us complete control over both data and hiring flow.

What is the team composition?

We worked with 6–10 teammates from Neural Command, LLC.

How did you come to work with Neural Command, LLC?

We found them through an online search and a referral. We chose them over other options because they had high ratings, good value for cost, and were referred to me.

How much have you invested with them?

We spent between $10,000 and $49,999.

What is the status of this engagement?

We started working together in November 2024 to March 2025.

RESULTS & FEEDBACK

What evidence can you share that demonstrates the impact of the engagement?

The most measurable outcome was a significant reduction in our reliance on paid job platforms. By building a direct applicant acquisition system, we were able to shift a large portion of applicant flow from paid job boards to organic search, materially reducing monthly spend that previously averaged around $20,000.Within the first few months of deployment, we reduced paid job board spend by approximately 60%–70%, cutting monthly costs down to the $6,000–$8,000 range while maintaining applicant flow.

At the same time, organic applicant volume increased by roughly 2–3× compared to pre-project baselines. A majority of new applicants began coming directly from Google Jobs and organic search results, rather than third-party job platforms.

We also saw consistent growth in organic applicant volume coming directly from Google Jobs and search-based job listings, validating that the JobPosting schema implementation and job page architecture were working as intended. Applicants were entering the funnel through our own job pages rather than third-party platforms, which gave us full visibility into where applicants came from and what roles they were responding to.

On the quality side, the in-house ATS and AI-assisted screening reduced manual recruiter workload by filtering, classifying, and prioritizing applicants before a review. This shortened response times, improved recruiter efficiency, and increased the percentage of applicants who were relevant for the role.

Finally, ownership of the full data pipeline became a measurable success in itself. We gained complete control over applicant data, search insights, and hiring performance metrics, allowing us to iterate on job titles, descriptions, and funnels based on real search and applicant behavior instead of platform-level reporting or assumptions.

While the primary focus was applicant acquisition and data ownership, we also tracked recruiter-side efficiency metrics after the system was implemented. The most immediate improvement was in time spent per applicant. With AI-assisted screening and automated classification in place, recruiters spent approximately 40%–50% less time reviewing unqualified or low-fit applicants since candidates were pre-sorted before reaching a review.

We also saw a reduction in initial response time to qualified candidates. Because relevant applicants were surfaced faster, recruiters were able to engage strong candidates 1–2 days sooner on average, compared to the prior workflow that relied heavily on manual filtering from paid job boards.

While overall time-to-hire varies by role, the system consistently shortened the top-of-funnel and mid-funnel stages, removing bottlenecks caused by volume noise and platform-level limitations. The net effect was higher recruiter throughput, faster candidate engagement, and a more predictable hiring pipeline without increasing recruiter headcount.

We did monitor downstream hiring outcomes, but it’s important to be precise about attribution. Offer acceptance rates and long-term retention are influenced by many factors outside the applicant acquisition system itself, such as compensation, role expectations, management, and onboarding. That said, we did observe positive directional changes after implementing the new system.

Because applicants were coming in through search-aligned, role-specific job pages and were screened before reaching recruiters, there was better expectation-setting earlier in the process. This led to a modest increase in offer acceptance rates, as candidates had a clearer understanding of the role before entering interviews.

We also saw early-stage retention improve, particularly in the first 30–60 days. Candidates entering through the organic funnel tended to be more intentional and better matched to the role compared to high-volume job board applicants, who historically included more low-intent or exploratory candidates.

While Neural Command, LLC’s system was not designed as a retention tool, the improved alignment between job search intent, role requirements, and applicant screening contributed to higher-quality hires and more stable early outcomes.

How did Neural Command, LLC perform from a project management standpoint?

Neural Command, LLC’s project management was structured, technical, and execution-focused. They broke the project into clear phases with defined deliverables, starting with search and data architecture, followed by job page deployment, structured data implementation, and backend ATS and AI logic.

Each phase was scoped realistically based on technical dependencies rather than arbitrary timelines. Deliverables were completed on schedule and, in several cases, ahead of initial estimates, particularly on the frontend job listing and schema implementation.

Where timelines shifted, it was due to external factors such as data availability, internal review cycles, or integration dependencies, not missed execution. Overall, Neural Command, LLC delivered on time, communicated clearly about progress and blockers via in-person and virtual meetings, email, and messages, and maintained momentum throughout the project without scope drift or missed commitments.

What did you find most impressive about them?

What stood out most was their depth of understanding across both search infrastructure and system design, and their ability to connect the two without handoffs or gaps. Neural Command, LLC didn’t treat the project as a marketing exercise or a surface-level SEO engagement.

They approached applicant acquisition as an engineering problem, combining deep knowledge of Google’s JobPosting ecosystem with backend data control, AI-driven screening, and ATS logic. That combination was rare and eliminated the usual friction between “SEO,” “product,” and “recruiting” teams.

They were also highly precise about ownership and control. Instead of optimizing within the constraints of third-party platforms, they built systems that allowed us to own our data, iterate quickly, and make decisions based on real search and applicant behavior. The result felt less like working with a vendor and more like working with an internal technical team that understood both the problem and the long-term implications of the solution.

Are there any areas they could improve?

Overall, the project was executed well, and there were no major shortcomings to point to. If anything, the only area that could have been done differently was documentation depth earlier in the process.

Because Neural Command, LLC moved quickly and operated at a very technical level, some internal stakeholders needed additional time to fully understand the architecture and decision-making behind certain components, particularly around schema governance and AI-driven screening logic. More upfront documentation or diagrams at the very beginning could have shortened internal onboarding.

That said, this was addressed as the project progressed. Neural Command, LLC provided clear explanations, follow-up documentation, and walkthroughs once the system was in place. The tradeoff was speed versus early documentation, and in our case, the faster execution ultimately outweighed that initial adjustment period.