Saas · Web app – Juan Menguiano https://juanmenguiano.com Senior Product Designer Thu, 16 Apr 2026 06:11:55 +0000 en-US hourly 1 https://wordpress.org/?v=7.0.2 https://juanmenguiano.com/wp-content/uploads/2025/04/cropped-Favicon-32x32.png Saas · Web app – Juan Menguiano https://juanmenguiano.com 32 32 Perty – Real Estate AI Tool https://juanmenguiano.com/project/perty/ Thu, 05 Feb 2026 11:19:20 +0000 https://juanmenguiano.com/?post_type=ohio_portfolio&p=246818 The Brief · Proactive AI agent https://juanmenguiano.com/project/the-brief-optimize-proactive-ai/ Sat, 27 Dec 2025 15:19:50 +0000 https://juanmenguiano.com/?post_type=ohio_portfolio&p=246559

Problem

Optimisation in Optimize was reactive and user-driven. Insights required manual triggering, marketing expertise, and interpretation of long-form outputs—often disconnected from daily campaign monitoring.

This meant users were forced to pull insights instead of being guided by the system, making prioritisation difficult and causing high-impact opportunities to surface too late or go unnoticed.

At a system level, Optimize lacked a clear decision-making model: signals, context, and actions were not connected in a way that supported timely, confident optimisation.

My role

I led the end-to-end design of Optimize’s shift from a reactive analysis tool to a proactive, AI-assisted optimization system.

I reframed the problem from “insight generation” to “decision-making”, defined the system-level interaction model, and partnered closely with Product, Engineering, and Data to align AI signals with human workflows.

I owned the UX strategy, interaction patterns, and foundational design principles to ensure the solution was scalable and capable of supporting future AI-driven actions, from recommendations to execution.

Scope & influence
  • End-to-end ownership (discovery → system definition → execution)
  • Cross-functional leadership with Product, Engineering, and Data
  • Platform-level patterns reused beyond a single feature
Challenge

Understanding the limits of reactive optimisation

This analysis revealed that the core challenge wasn’t the quality of AI insights, but how optimisation was triggered, delivered, and scaled.

Optimisation relied on manual user actions, required marketing expertise to interpret long-form insights, and often surfaced only after performance had already dropped.

As a result, optimisation was reactive, cognitively demanding, and difficult to scale across multiple campaigns — highlighting the need for a proactive, system-led optimisation model.

Paradigm shift

From reactive optimisation to system-led decisioning

This marked the need for a shift:
from user–triggered optimisation → to system-initiated decisioning, where insight timing becomes as critical as insight quality.

System-led optimisation

Optimisation was no longer a matter of better insights, but of better timing and delivery. Relying on users to decide when to act meant insights arrived late, fragmented, or were never used. This marked the need for a shift: from user-triggered optimisation → to system-initiated decisioning.

Designing the proactive optimisation layer

Instead of waiting for users to ask, the system continuously monitors campaign signals and health patterns — detecting risk, opportunity, and performance shifts in real time. Optimisation becomes a persistent layer in the workflow, not a destination users have to actively reach.
  • Always on
  • Always evaluating
  • Always prioritising what matters next
Designing the proactive optimisation
Attention

Campaign health as the primary signal

Insights are no longer buried in data or long conversations.

They surface at the right moment, prioritised by expected impact.

Suggestions shift from narrative explanations to decision support:

  • What’s happening — and why it matters

  • What to change — grounded in signals

  • What to expect — quantified before acting

Rather than forcing immediate execution, teams can pin suggestions to maintain visibility and build a focused optimisation backlog — balancing global awareness with campaign-level action.

Suggestion → Decision

AI-driven optimisation, surfaced when it matters

The system continuously monitors campaign health and proactively surfaces high-impact opportunities — removing the need for manual triggers or expert interpretation.

Health signals provide immediate visibility, while AI Suggestions translate performance data into prioritised, actionable decisions. Each suggestion includes context, rationale, and expected impact, and can be pinned to support planning and collaboration over time.

Optimisation becomes continuous, proactive, and system-led.

Campaign health turns performance data into a clear signal of where to focus.

High-impact opportunities are prioritised automatically, based on expected outcome.

 

Each suggestion provides confidence through forecasts, context, and quantified impact.

Design principles

Guiding the solution

System over user initiative

Optimisation should be triggered by system signals, not user intent.

Global visibility, focused action

Users need a high-level health overview with the ability to drill down only when necessary.

Signals before narratives

Insights must be structured, scannable, and prioritised by impact.

Decision support before execution

AI should help users assess confidence and risk before acting, not push immediate changes.
Goals

What this unlocks

What this enables

This new optimisation model unlocks a shift from manual, reactive analysis to continuous, system-led decision-making.

It creates a foundation for:

  • Earlier detection of performance risks

  • Consistent optimisation across accounts and campaigns

  • Reduced reliance on individual marketing expertise

  • Future AI-driven actions, from recommendations to direct execution

Next steps

Roadmap

The current iteration focuses on insight quality, prioritisation, and interaction patterns.

Upcoming phases include:

0
Validating signal accuracy and trust with live campaigns
0
Testing suggestion clarity and decision confidence with marketers
0
Gradual rollout of AI-assisted execution and automation
Let’s Collaborate

Designing products that perform.

Let's talk
]]> Genially · Real-time Collab https://juanmenguiano.com/project/genially-real-time-collab/ Thu, 14 Apr 2022 10:19:54 +0000 https://juanmenguiano.com/?post_type=ohio_portfolio&p=245916

Problem

As Genially scaled, real-time collaboration shifted from a feature problem to a platform-level reliability and trust challenge.

Multiple users editing the same content at the same time led to:

• Conflicts between simultaneous edits
• Lack of visibility about who was doing what
• Uncertainty during uploads and shared actions
• Reduced confidence in collaborative sessions

My role

I led the end-to-end UX definition of real-time collaboration, from problem scoping and interaction design to prototyping, testing, and handoff.
I partnered closely with Product and Engineering to align on system constraints and edge cases, ensuring the experience worked reliably under real-world concurrency scenarios.
  • End-to-end ownership of real-time collaboration UX, from problem framing to platform-level delivery
  • Defined the collaboration model adopted across multiple surfaces, beyond a single feature
  • Established design patterns that scaled with concurrency and usage growth
  • Raised platform reliability and user trust in high-density, multi-user editing scenarios
Challenges

Analysing the current experience

The existing collaboration experience didn’t scale to support real-time, simultaneous editing—especially in high-density scenarios with multiple active users.

As Genially grew, collaborative sessions became harder to trust: users lacked visibility into who was doing what, whether actions were still in progress, and if the system was in a stable state.

Key issues identified:

  • No clear visibility of user presence or activity
  • Overlapping actions without real-time feedback
  • Upload and processing states lacked transparency
  • Increased cognitive load during collaborative editing
Before
From single-user editing to real-time collaboration
After
The experience evolved from isolated actions to a shared, real-time collaborative workflow.
Before After

The shift from isolated actions to shared feedback reduced uncertainty and enabled confident real-time collaboration.

System-level interaction rules

Design patterns for real-time collaboration

We defined a set of platform-level patterns to ensure clarity, trust, and predictability in high-concurrency editing scenarios.

Key patterns:

  1. Live presence — cursors, labels, and selection states clarify who is acting.

  2. Shared object states — predictable behavior when multiple users interact.

  3. Upload transparency — clear feedback during async actions.

These patterns reduced conflicts and enabled predictable, trustworthy collaboration in high-density sessions.

Commenting & Review

In-context feedback and approval

We designed in-context feedback and approval to keep collaboration tied to the canvas, reducing context switching and ambiguity in shared decisions.

Feedback is anchored to specific elements, clearly attributed, and easy to resolve supporting fast alignment without interrupting the editing flow.
Collaboration

Real-time co-editing with clear ownership

We designed real-time co-editing around clear ownership, making it explicit who controls changes and when actions are in progress.

By surfacing editor attribution, ownership states, and in-flight actions, the system prevents conflicts and enables confident parallel work in high-concurrency scenarios.

Testing & Iteration

User test

User testing in real-time scenarios

I validated the real-time collaboration model through targeted user tests in high-concurrency scenarios.

Sessions focused on simultaneous edits, parallel uploads, and inactive participants to stress system awareness and trust.

Iterative adjustments based on feedback


Testing revealed inactivity as a primary source of confusion in dense collaborative sessions.

I iterated on inactivity handling with progressive fade-outs and soft system signals to reduce noise while preserving awareness.

Key Lernings

  • Most collaboration issues surfaced in high-concurrency, multi-user scenarios
  • Clear, system-level visual feedback increased user confidence
  • Explicit inactivity handling reduced ambiguity and improved focus
Iteration / After testing

Inactivity detection

During user testing, we observed a recurring breakdown in trust when collaborators became inactive without clear system feedback.

Participants were unsure whether an editor had intentionally stopped, lost connection, or was still holding control—leading to hesitation, duplicated actions, or unnecessary interruptions.

What we added:

  • Inactivity detection signals to infer disengagement over time

  • Clear visual state changes when an editor becomes inactive

  • Graceful ownership release to unblock collaboration without manual intervention

This iteration closed a critical trust gap that only surfaced under real usage, reinforcing the system’s reliability in long-running, high-concurrency sessions.

Statistics

Impact by the numbers

Key indicators based on early adoption and qualitative user feedback.

0 %
Clarity &
presence feedback

Live presence indicators and selection states significantly improved users’ understanding of who was doing what, in real time.

0 %
Editing
conflicts reduced

Clear ownership signals and shared object states reduced accidental overlaps and conflicting edits.

0 %
Upload objects
transparency

Explicit progress and system feedback reduced uncertainty during async uploads and background actions

Strategic learnings

What this project taught us

01.

Clarity is critical in multi-user environments

Real-time collaboration only works when users can instantly understand who is acting, what is happening, and what state the system is in.

03.

Design must reflect live complexity

Simulating real concurrency early revealed edge cases invisible in static flows—and shaped more resilient interaction models.

02.

Inclusive design improves the experience for everyone

Accessible colour, iconography, and spacing reduced friction and made collaboration more intuitive across skill levels.

04.

Proactive feedback builds trust

Presence indicators, progress signals, and previews helped users stay confident and in control during live collaboration.

Let’s Collaborate

Designing products that perform.

Let's talk
]]> Abacum · Integrations https://juanmenguiano.com/project/abacum-scalable-integrations/ Wed, 23 Mar 2022 18:21:06 +0000 https://juanmenguiano.com/?post_type=ohio_portfolio&p=246412

Problem

As Abacum scaled, integrations evolved from a one-off setup task into a critical platform bottleneck.

Highly variable data formats and fragile validation logic forced data imports and updates to rely on manual support workflows, resulting in frequent errors, long turnaround times, and limited visibility into data health.

FP&A teams lacked autonomy to configure and maintain integrations confidently, while internal teams spent disproportionate time resolving issues. The system did not scale with customer growth.

My role

I led the end-to-end UX definition of Abacum’s integrations experience, reframing it from a support-driven process into a scalable, self-service platform capability.

I partnered closely with Product, Engineering, Data, and Support to define system-level interaction models, validation states, and error-handling patterns that balanced flexibility with data reliability.

Scope & impact
  • End-to-end ownership of integrations UX (discovery → system definition → delivery)
  • Platform-level interaction and validation models reused across multiple integrations
  • Reduced support dependency and enabled FP&A self-service
  • Direct impact on operational efficiency and system reliability
Analysis

Support & customer-led journey analysis

As there was no user-facing integration interface, the analysis focused on internal
support workflows and recurring issues surfaced by both support teams and finance users.

By mapping these inputs into a shared journey, we identified systemic pain points across
data imports, updates, and error handling—allowing us to prioritise the most critical
failures and define an MVP focused on reducing operational friction from day one.

Synthesized from support tickets, customer interviews, and internal workflows.

The shift from intervention to self-serve integrations reduced operational friction and enabled finance teams to manage data with confidence.

 

Original MVP

Guided integrations for faster, safer setup

The MVP introduced a guided, self-service integration flow that replaced manual, support-led processes.

Finance teams could connect data sources step by step, with clear validation and system feedback at each stage—reducing setup errors and uncertainty.

This shifted integrations from a support dependency to a reliable, scalable product capability, freeing internal teams to focus on complex edge cases rather than repetitive setup tasks.
Data in everywhere

Integrations as a real-time data layer

Self-serve integrations evolved into a real-time data backbone across Abacum, powering forecasts, KPIs, and scenario modelling with always-up-to-date inputs.

By eliminating manual updates and fragile syncs, finance teams gained continuous visibility across planning, reporting, and decision-making—while the platform scaled reliably with growing data complexity.

Mapping comprehension

Early user test

User testing in real-world scenarios

Tested the MVP with finance teams using real production data and live integrations to validate clarity, error handling, and autonomous setup without support intervention.

Iterative adjustments based on feedback

Testing surfaced friction around mapping and recovery from errors. The flow was iteratively simplified, introducing live previews, clearer field ownership, and safer defaults to prevent costly setup mistakes.

Key Lernings

  • Guided, constrained flows reduced critical data errors
  • Live previews and ownership clarity lowered support dependency
  • Reliable integrations became a reusable data foundation across workflows
Statistics

Project by the numbers.

Key indicators based on early adoption and user feedback.

0 %
Successful first-time completions

After launch, significantly more users completed the integration flow on their first attempt, driven by clearer guidance and a fully self-serve setup experience.

0 %
Drop in support tickets

Requests related to data imports and sync issues dropped notably, freeing the support team from manual intervention and repetitive troubleshooting.

x 0
More integrations per user

The new workflow encouraged finance teams to configure and manage more integrations autonomously, increasing adoption and long-term product usage.

Iteration & Impact

From MVP to a core product capability

Launched in 2022 as an MVP to reduce manual support effort, the self-serve integrations system became a core capability of Abacum. Over time, it scaled to support multiple data sources and use cases across forecasting, reporting, and modeling — turning integrations into a foundational layer of the product rather than a standalone feature.

This MVP became foundational, not incremental.

Strategic learnings

What this project taught us

01.

Self-serve systems scale teams, not just products

Moving integrations out of support workflows and into user hands unlocked autonomy, reduced operational load, and scaled without increasing internal complexity.

02.

Clarity beats flexibility in early-stage systems

Early iterations showed that guided flows and opinionated defaults outperform overly flexible setups when users are dealing with complex data structures.

03.

Integrations are product infrastructure, not features

Treating integrations as a real-time data layer—rather than isolated connections—enabled consistent experiences across forecasting, reporting, and scenarios.

Let’s Collaborate

Designing products that perform.

Let's talk
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