HR & RECRUITMENT
RAG Chatbot Development for HR & Recruitment
HR & Recruitment teams face a familiar wall: good candidates lost in a process that cannot keep up with applications. Chatbots that actually know your business — grounded in your data. Midalaxy builds it as a production system — integrated with your tools, measured against your metrics.
vector retrieval latency
What this looks like in hr & recruitment
What gets automated in hr & recruitment
- Application screening and shortlisting
- Interview scheduling
- Candidate and employee queries
- Onboarding documentation
- Right-to-work and background checks
SYSTEMS IT HAS TO MEET
- · ATS — Greenhouse, Workday, Lever
- · HRIS and payroll
- · background-check providers
- · learning management systems
We integrate with what you already run rather than asking you to replace it.
WHAT MAKES THIS HARDER
Screening tools sit in a high-risk category in most emerging AI regimes, and a model that filters unevenly across protected groups is a legal exposure regardless of intent.
What governs hr & recruitment systems
Employment discrimination law
Anything influencing who progresses is subject to fair-treatment obligations, and disparate impact is a finding regardless of intent.
High-risk AI classification
Employment screening sits in a high-risk category in most emerging regimes, which brings documentation and oversight requirements.
Candidate data rights
Applicants have access and erasure rights over data that is often scattered across ATS, email and spreadsheets.
Where the data actually lives
- The ATS holds the pipeline and rarely holds the reasoning behind a decision, which is exactly what an audit asks for.
- CVs are unstructured and formatted for humans, and extraction quality caps everything downstream.
- Historic hiring data encodes historic preferences, including the ones an employment tribunal would examine.
- Interview feedback is free text of wildly varying quality, and the useful signal is often absent rather than hidden.
The numbers hr & recruitment teams manage by
Time to hire
The measure recruitment is run on, and where slow processes lose the best candidates first.
Pass-through rate by stage
Where a screening change shows up, and where disparate impact becomes visible if anyone looks.
Offer acceptance rate
Whether the process attracted people who actually wanted the job.
Quality of hire at 12 months
The only measure that says whether screening improved anything, and the one most often skipped.
How these projects fail in hr & recruitment
Learning historic preference
A model trained on who was hired before reproduces it. Subgroup testing before deployment is the minimum, and it frequently stops the project.
Screening without reviewable reasoning
A rejection nobody can explain is unusable when challenged, and challenges in this domain arrive with a lawyer.
Optimising for the wrong outcome
A screener tuned to hiring-manager approval learns to predict the manager, not the performer.
Silent CV parsing failure
A layout the parser cannot read becomes a candidate who quietly never appears. That failure is invisible unless it is measured.
Capabilities
- Document, website and database ingestion
- Self-updating vector indexes
- Source-cited answers with guardrails
- Lead capture and handoff built in
Shipped, and measured
Frequently asked questions
What is a RAG chatbot?
RAG (retrieval-augmented generation) grounds every answer in your actual documents and data, so the bot answers from facts rather than guessing.
How does the bot stay up to date?
We build self-updating pipelines that re-index automatically when your source content changes — no manual retraining.
Can it run on our website today?
Yes. Our SiteChat platform turns any website into a chatbot via URL discovery and vector indexing, deployable as a widget in days.
Related
RAG Chatbots for hr & recruitment — let's scope it
Tell us what you are building. We reply within one business day.