A global HR services organisation (named reference under NDA) needed the volume absorbed without the people data ever leaving its own environment. We deployed two governed workflows inside its boundary, with personal data masked before anything reached a model.
The organisation runs HR functions for enterprise clients, which means a constant flood of repetitive support queries — leave policy, benefits, onboarding, payroll — alongside a demanding hiring workflow spanning hiring managers, candidates and internal teams.
The volume was manageable. The pattern was the problem. Specialists were spending most of their time answering questions with known, consistent answers: questions that needed specialist access, not specialist judgment. In parallel, hiring coordination meant tracking many threads at once — job-description approvals, interview scheduling, feedback collection, offer generation — mostly by email and manual follow-up.
Because this is people data, governance was non-negotiable. Data residency, masking and a clear audit trail were table stakes, which ruled out generic consumer AI tools.
The core problem: high-value people doing high-volume, low-complexity work, because no system existed to absorb the volume safely.
We deployed two governed workflows on TurfAI, with on-premises deployment so sensitive people data never left the organisation's environment.
HR support. Incoming queries are classified by type and intent. Known-answer queries — policy lookups, status checks, standard process questions — are resolved automatically with personalised responses drawn from the organisation's own policy documentation. Anything needing human judgment is routed to a specialist with full context pre-loaded. Personal data is masked before anything reaches a model, and every interaction is auditable.
Hiring orchestration. TurfAI coordinates the end-to-end hiring workflow: job-description generation from role briefs, interview scheduling with calendar integration, feedback collection from hiring managers, and offer-letter generation from approved templates. The HR team reviews and approves at each milestone; TurfAI handles the coordination and communication in between.
Integration was via API into the existing HR information system. No system migration — the platform worked inside the existing stack.
We owned the outcome, deployed on-premises to the organisation's governance standard, and stayed accountable through go-live. We took the build-and-operate load so the HR team kept its focus on advisory work rather than coordination. This ran inside TurfAI from the first workflow to production, under one named practice lead who carried both the build and the commitment behind it.
Specialist time was reallocated from query handling to advisory and strategic HR work, and hiring cycle time came down as coordination delays were removed and milestones tracked automatically.
This is the engagement we point at when a client asks whether any of this can run inside their own boundary. Two things make the answer yes: the deployment sits on their infrastructure, and the masking happens before the model call rather than after it — so the governance argument does not depend on trusting a vendor's retention policy.
Stack — TurfAI · on-premises deployment · HR information system API integration · pre-model data masking · calendar and template integration
Tell us the query volume and where the data has to stay. We will tell you what can run inside your boundary before anyone proposes a build.
