Service
Practical AI for HCM — start where it actually pays back.
HR leaders are being asked to do something with AI. The advice on offer is mostly keynote-level hype or generalist AI consultancies who don't know HCM. We work the specific intersection — AI applied to the workforce systems you already run.
Problems we solve
Every AI vendor is pitching keynote-level transformation, nothing about what to do next quarter.
A Readiness Assessment that maps where AI actually pays back in your business, with a prioritized sequence keyed to your operational priorities. You leave with a path, not a slide deck.
Generalist AI consultancies don't understand HCM data structure, payroll compliance, or workforce operations.
AI engagements built on the same HCM fluency we've delivered for two decades — integrated with your HCM data, designed for your compliance reality.
Employee-affecting AI decisions without human oversight are a legal and trust risk.
Every deployment ships with a human-in-the-loop design. The model surfaces options. Humans decide. Governance is documented, not improvised.
AI pilots never make it out of the lab because nobody designed for production from day one.
Scoped engagements with a production deployment path defined before we start. Pilots land as operational systems, not demos that get shelved.
Proven in our own operations
RJ Reliance uses AI inside its own business before recommending anything to clients. A structural commitment, not a marketing moment.
Our order-to-cash workflow — quoting, proposal generation, contract signing, invoicing, and new-engagement setup — runs on AI-assisted automation our sales team won't give up. Workforce optimization runs in production, giving us sharper reads on capacity, utilization, and staffing than any spreadsheet did. This website was built in the same AI-assisted workflow, from Sanity schemas to copy iteration to the component library.
If we haven't tried it ourselves, we won't recommend it.
Offerings
What ai solutions engagements look like
- 2–3 weeks
AI Readiness Assessment
A written readout on where AI actually pays back in your organization — which workflows are ripe now, which aren't, and a prioritized sequence tied to operational priorities. Self-serve version available via the assessment tool (Phase 4 roadmap); guided engagement when the assessment itself is where you need help. Output: a prioritized map plus optional next-step engagement scope. No commitment beyond the assessment.
- 4–8 weeks
Strategic Advisory
The "where to start" engagement for organizations that need guidance before they can pick tools or scope deliveries. We walk through your operational priorities, constrain the AI question to your actual use cases, and deliver a written implementation sequence tied to the business outcomes you care about. Sometimes this is the first of several engagements; sometimes it's the only one needed.
- 6–12 weeks per workflow
Workflow Automation
Scoped engagements automating specific high-friction HCM workflows — onboarding flow, benefits routing, compliance reporting packs, PTO-to-timesheet reconciliation, a named automation opportunity the Assessment surfaced. Bounded scope, bounded budget, measurable outcomes. Deploys to production with the human-in-the-loop and governance model we build for every AI deployment.
- 8–16 weeks
Predictive Forecasting
Longer engagements building predictive models for scheduling, turnover, and labor-cost forecasting. Includes model construction, validation against your historical data, integration with your existing planning systems, and the human-in-the-loop cadence that keeps the model honest. Requires data maturity and a willing operational sponsor; we'll tell you honestly during the Assessment if the data isn't there yet.
How we work
Delivery process
Scope & context
1–2 weeks. Understand the operational priority, data landscape, and success criteria. No code yet.
Design & data review
2–4 weeks. For advisory engagements: stakeholder interviews and recommendation drafting. For delivery engagements: data schema review, security review, model approach selection.
Build & validate
Varies by engagement. Iterative, with weekly check-ins. Output validated against known-good samples before any production traffic.
Production deployment
Supervised rollout with human-in-the-loop. Starts with small-batch review, scales as confidence builds.
Steady state or handoff
Ongoing managed service, or clean handoff to your team with runbooks and governance docs.
Operating model
How the engagement runs
Human-in-the-loop cadence
Every AI deployment we build has a designated human accountability layer. For pilots, that's typically daily spot-checks of output against a sample set. For steady-state operations, it's weekly or monthly review cycles keyed to the business rhythm. We never ship an AI system as a black-box decision-maker for employee-affecting decisions — the model surfaces options. Humans decide.
Model governance
Versioning, audit trails, drift detection, and model-refresh cadence are agreed at design time. We document which inputs the model uses, how often it retrains, what its known failure modes are, and what triggers a pause-and-review. Governance lives alongside the deployment, not in a separate compliance binder.
Security and data handling
HCM data is the most sensitive category most of our clients handle. Our deployments use models and vendors we've reviewed for data-handling posture, with a preference for patterns that keep sensitive data out of third-party model-training pipelines by default. Explicit opt-in when training is the point. Every engagement includes a written data-flow diagram your security team can review before go-live.
Frequently asked
Questions we hear
Is our data safe — where does it go and who can see it?
Short answer: by default, sensitive HCM data stays out of third-party model-training pipelines. Longer answer depends on the engagement type. For assessments and advisory work, we operate under standard consulting NDAs and work with data summaries and structure, not raw employee records. For delivery engagements (Workflow Automation, Predictive Forecasting), every project includes a written data-flow diagram your security team reviews before we start. If a specific use case requires something different, we tell you explicitly and get written sign-off before anything moves.
What's a realistic starting point if we've never used AI?
The Readiness Assessment. Before we talk about tools, use cases, or budgets, we'll help you map where AI actually pays back in your organization. Some companies walk out of the Assessment with a sequence they execute in-house; others walk out with a scope for the next engagement. Either is a successful outcome.
Do you work with the AI already built into our HCM vendor?
Often yes. Most of the major HCM platforms now ship AI features for scheduling, candidate screening, attrition prediction, and more. We'll evaluate whether those native features fit your use case — they often do for the obvious cases — or whether you need something outside the platform. We don't build custom when native works. We build custom when native doesn't.
What's the pricing model — project, retainer, or subscription?
Project pricing for assessments (fixed fee) and scoped delivery engagements (Workflow Automation, Predictive Forecasting) — sized during scoping, written into the SOW, no hourly surprises. Retainer pricing for advisory engagements that extend beyond the initial 4–8 weeks. No subscription products; we're a services firm, not a software vendor.
How do we know if we're ready?
If you have reasonably clean HCM data, an operational priority that doesn't require rebuilding underlying systems first, and an executive sponsor who understands this is a bounded investment (not a transformation program), you're ready for the Readiness Assessment. If the data isn't clean, the Assessment will flag it. If the operational priority requires fixing underlying systems first, we'll say so — and you'd engage our Optimization practice instead. No judgment in either direction.
What happens if the AI produces a wrong answer about an employee?
The design anticipates this. Every AI deployment we build has a human-in-the-loop layer — the model surfaces options, humans decide, and decisions affecting employees are reviewable with their rationale on file. AI isn't accurate all the time, and any consultant who claims otherwise isn't being straight with you. What we can do is design systems where wrong answers are caught fast, the human-review cadence matches the stakes of the decision, and your team has clear authority to override the model. That's the responsibility layer that separates thoughtful AI deployment from a black-box liability.
Ready to talk through ai solutions?
Tell us about your operational priority. We'll respond within one business day and route the right people to the conversation.