SeamlessHR · Mar 2022 – Mar 2025
SeamlessHiring 2.0
Recruitment Management System (RMS)
Rebuilding fragmented recruiting workflows into a scalable hiring operating system.
SeamlessHiring began as a fragmented recruitment add-on that broke under scale during a high-volume graduate hiring programme. The redesign focused on restoring workflow trust, restructuring the hiring lifecycle, and repositioning RMS from a low-cost add-on into a scalable, enterprise-priced product across local and international markets.
UX strategy
Workflow architecture
AI-layered experience design
Delivery implementation
- ·Phased modernization over full replacement
- ·Workflow-first UX
- ·Evidence-led research before wireframes
02 Core Tensions
What was broken
Three systemic failures that made redesign unavoidable — not isolated interface issues, but structural breakdowns that prevented the system from supporting enterprise hiring at scale.
Recruiters were abandoning core tasks mid-flow and coordinating outside the system — creating invisible work and audit gaps that compounded with every new hire.
Interview scoring was informal and undocumented, leaving hiring decisions without a defensible trail — a consistency and legal exposure risk at enterprise scale.
Legacy RBAC could not support multi-entity client realities, forcing manual CS workarounds and capping how large the platform could sell.
Figure B — Navigation misrouted core tasks, evaluation had no system surface, and permission models blocked enterprise use cases.
- ·01 — Workflow collapse: 'Requests' button routed to application history, not the application form — the primary recruiter action was buried behind the wrong entry point
- ·02 — Evaluation without structure: no scoring or assessment surface existed inside the system — evaluation happened in spreadsheets and email threads outside SeamlessHiring entirely
- ·03 — Permission model: rigid access control could not accommodate multi-entity enterprise clients — every new onboarding required manual CS intervention
Figure C — Restructured the product around recruiter and applicant journeys before interface work — separating legacy constraints from the target hiring lifecycle.
Figure D — The redesign spanned three years across five phases — with an AI-powered layer added only after workflow trust was restored.
03 Evidence in Practice
Job creation was inconsistent across hiring managers — no shared templates, no structured input model, and errors compounding downstream in the pipeline before a role even reached applicants.
Guided job creation templates standardizing inputs and enforcing required fields before a role was published to the applicant-facing system.
Figure 01 — Standardized job creation through guided templates that reduced setup friction and improved consistency across hiring teams before a role enters the pipeline.
- ·Template-first approach chosen over form validation alone — structure at entry prevents downstream reconciliation work
- ·Required fields enforced at creation stage, not mid-application — eliminating a class of errors that only surfaced after applicant submission
Figure 02 — Reframed the application journey to eliminate abandonment and support higher-volume candidate processing without recruiter intervention.
- ·Progress indicator placed at top of flow, not in sidebar — reduces cognitive load without adding navigation complexity
- ·Required documents shown at start, not mid-application — directly removing the surprise abandonment pattern visible in FullStory session recordings
Applicants were abandoning mid-flow with no recovery path and no visibility into what would be required of them until they were already partially through the application.
Application journey reframed around completion confidence — required documents surfaced at the start, persistent progress state visible throughout.
Interview scoring was informal and undocumented. Evaluation lived in spreadsheets and email threads outside the system, leaving hiring decisions without a defensible trail.
Structured evaluation workflows with a shared scoring rubric, documentation trail, and consolidated recruiter action surface.
Figure 03 — Introduced structured evaluation workflows that improved decision quality, reduced recruiter context switching, and brought hiring decisions back inside the system.
- ·Consolidated fragmented recruiter actions into a single decision surface — direct response to context-switching patterns generating mid-task abandonment in FullStory sessions
- ·Scoring rubric developed collaboratively with HR SMEs, not imposed from the design side — adoption required co-authorship, not mandate
Figure 04 — Permission patterns transformed access control from operational friction into scalable enterprise administration — removing the CS dependency from every new client onboarding.
- ·Role inheritance model chosen over flat permissions — supports multi-entity clients without permission explosion
- ·Self-service admin assignment eliminated a recurring CS ticket category that scaled with every new enterprise onboarding
RBAC configuration was too rigid for multi-entity enterprise clients. Every new onboarding required CS workarounds, creating a ceiling on how large the platform could sell.
Permission model redesigned to support role inheritance, entity-level overrides, and self-service admin assignment.
Recruiters were spending disproportionate time on manual CV screening — a high-volume, low-judgment task the redesigned system was now stable enough to augment.
AI-assisted recommendations were introduced in the final phase to support recruiter evaluation and shortlisting — augmenting structured recruiter decision-making rather than replacing it.
AI was designed as decision support, not decision authority.
Figure 05 — Pilot validation of AI-assisted candidate ranking and explainable evaluation within live recruitment workflows — the Smart Assessment Summary surfaces structured scoring rationale and sentiment signals for recruiter review.
- ·AI introduced in Phase V deliberately — augmenting a workflow only after trust in the core system was re-established. AI on top of a broken process inherits the broken process's failure modes
- ·Ranking logic calibrated against client hiring criteria rather than generic resume heuristics
- ·Ranking surfaces signal, shortlisting remains recruiter-owned — boundary set by design, not by default
- ·AI outputs were surfaced as explainable recommendations rather than opaque scores, preserving recruiter agency while improving decision signal quality — the interface showed reasoning, not just results
Those structural fixes restored workflow trust first — the judgments below explain what we traded off to get there.
04 Strategic Decisions
Judgment, trade-offs, and outcomes
Five choices that shaped the programme — alternatives considered, costs accepted, and what each unlocked.
05 Outcomes
Results across four dimensions
RMS transitioned from a ₦150k one-time add-on into a recurring product (~₦200k/month), while international pricing increased from $200 to up to $500/month depending on enterprise scale.
"The redesign transformed SeamlessHiring from a functional but frustrating tool into a scalable enterprise product."
— Femisayo Olofintila, Head of Product Management, SeamlessHR
Promotional overview of SeamlessHiring 2.0.
06 What This Unlocked
Established the phased redesign model applied to every subsequent enterprise product — including IBEDC and FetsProza.
Seeded the organizational case for Seamkit. SeamlessHiring was the first product migrated to the unified design system, and the proof-of-concept that made cross-team adoption credible.
Helped reposition design from delivery support into a pricing and retention lever — changing how sales framed the product category.
Defined how I approach AI integration: as a Phase V decision, not a Phase I feature.