Recruitment & HR Tech

Enterprise AI Platform for Recruitment & HR Tech

Enterprise AI Platform for recruitment & hr tech, built around the constraint that defines the sector: any screening automation must be tested for bias and be explainable to a rejected candidate.

Regulations in scope
4
Systems we integrate
4
Typical first release
6 weeks

What changes when it is recruitment & hr tech

Central governance fails when it becomes a queue. We build self-service onboarding with the policy enforced automatically, so teams move without waiting for approval.

In recruitment & hr tech, any screening automation must be tested for bias and be explainable to a rejected candidate. That single fact reshapes how enterprise ai platform has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is job description drafting, usually integrated against assessment platforms. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.

Built by engineers who ship production systems, not by a practice that subcontracts the build. You own the code, the models where they are open-weight, and the documentation to run it without us.

The sector constraints we design around

Defining constraint
any screening automation must be tested for bias and be explainable to a rejected candidate
Regulations in scope
labour laws · DPDP Act 2023 · equal opportunity obligations · EU AI Act high-risk classification for hiring
Systems of record
ATS · HRMS · assessment platforms · background verification services
Where we usually start
CV parsing and structured screening

Enterprise AI Platform workloads in recruitment & hr tech

  • CV parsing and structured screening
  • interview scheduling
  • candidate communication
  • job description drafting
  • interview note summarisation

What is included

  • Model gateway across providers with failover
  • Central prompt and template registry with versioning
  • Per-team quotas, budgets and cost allocation
  • Policy enforcement, PII handling, allowed models, data residency
  • Full audit log of every prompt and response
  • Self-service onboarding for product teams

Questions from this sector

Is AI screening legal?

In India, with care; under the EU AI Act hiring is classified high-risk with specific obligations. Either way, bias testing, explainability and human review of rejections are the baseline we build to.

How do you prevent bias?

Testing outcomes across demographic groups, excluding proxy features, and keeping a human decision on every rejection. We report the test results rather than asserting fairness.

Why not let teams call the APIs directly?

Because you lose cost visibility, audit trail and policy enforcement, and you end up with keys in a dozen repositories. A gateway gives teams the same speed with none of that exposure.

Does it lock us to one model provider?

The opposite, the gateway is what makes providers swappable, with failover when one has an outage.

How long does a platform take?

A usable first version with gateway, logging and quotas typically lands in six to eight weeks; governance depth grows from there.

Enterprise AI Platform for recruitment & hr tech, worth a conversation?

Tell us the workload and the regulation it sits under. We will tell you what is realistic.

Or email bd@dtrasglobal.com · call +91 74118 77878