From Insight to Flywheel: How Sariio Turns Preference Data into an Engagement Engine

Global employee engagement is low and moving in the wrong direction.

Gallup’s latest State of the Global Workplace reports engagement at around 21–23%, with low engagement costing the global economy an estimated US$8.9 trillion a year, or 9% of global GDP.

At the same time, high-engagement organisations report higher productivity, profitability, lower turnover, and fewer safety incidents.

The case for improving engagement is clear.

What is missing is a rigorous way to link how people prefer to work, how teams are designed, and the outcomes that follow.

Sariio MAPS is built to close that gap – not only through individual insight, but through a data flywheel that strengthens with every survey response.

Why preference data matters more than another engagement score

Most organisations now run engagement surveys. They know the scores.

The challenge is turning “engagement is low in Unit X” into something actionable.

Engagement scores answer “how people feel right now”.

They say little about “how people prefer to work”.

This is the gap Sariio MAPS targets:

• It measures work preferences across Connecting, Thinking, Deciding, and Implementing.
• It explains the motivations and patterns that sit underneath engagement.
• It makes these patterns visible from the individual level right up to the board view.

In practice this means:

• An individual can explain, in plain language, the conditions that support their best work.
• A manager can see why a team clashes on decisions or implementation pace.
• HR can see preference patterns across functions, countries, and layers of seniority.

Preference data does not replace engagement data.

It adds the missing “why” underneath it.

Three levels of value: individual, team, organisation

1. Individuals: a language for “how I work best”

Through Mirror (the free individual product) people:

• Complete a short, 60-item survey.
• See their four MAPS factors and archetypes.
• Receive a short, AI-generated narrative in clear language.
• Get a small set of practical suggestions for the next few weeks.

The immediate value is personal:

Better self-awareness, better preparation for performance reviews, and more honest conversations with managers.

Crucially, Mirror is free and shareable.

Users invite colleagues and compare profiles one-to-one, which seeds the Sariio language inside teams before procurement enters the picture.

2. Teams and managers: turning data into conversations

With Map, managers, coaches, and individuals move beyond static reports:

• Managers see team patterns across preferences and archetypes.
• They build 1:1 agendas informed by both people’s profiles and the current context.
• Teams see where they are over-loaded with, for example, “Driven–Decisive” profiles and light on “Thorough–Analytical” ones.

The key difference:

Map always combines stable preference data with live context.

Managers and teams can describe the situation (scope, deadlines, budget constraints, relationship issues) in their own words.

Sariio’s AI then blends that context with the MAPS data to produce specific, situational guidance – not generic teamwork advice.

3. Organisations: heatmaps and structural decisions

With Compass, HR and leadership gain an aggregated view:

• Heatmaps of preferences, factors, and archetypes by team, unit, region, and function.
• Trends across survey waves.
• Signals that highlight teams with high pace but low tolerance for ambiguity, or strong innovation preferences but low decision clarity.

Compass links these patterns to other metrics, such as engagement, retention, or absence, where the organisation chooses to share them.

At this level, Sariio becomes a people analytics engine, in the sense used by CIPD and others: a structured way to use people data to understand and improve performance.

The architectural advantage: why the data is trustworthy

For investors, the question is not only “what insight is possible?”, but “how defensible and scalable is the system that generates it?”.

Sariio’s architecture creates that defensibility through a clear separation of concerns:

1. Product layer – Mirror, Map, and Compass as user-facing applications.
2. Service layer – identity, survey, reporting, AI orchestration, analytics, and integration services.
3. Data layer – structured storage for individuals, tenants, survey results, and aggregated metrics.

Some key design choices drive long-term value:

Person / Identity / Tenant model

Instead of tying everything to an email address, Sariio introduces:

• A Person – a pseudonymous anchor for an individual.
• One or more Identities – personal email, work email, SSO credentials, all mapping to that Person.
• Tenants and TenantUsers – organisational accounts and roles.

This has two advantages:

• Any individual can start in Mirror with any email they prefer.
• Later, if their employer adopts Map/Compass, their profile can be linked to a corporate TenantUser only when they agree, without data duplication.

From an investor perspective, this supports frictionless upgrade paths while remaining aligned with GDPR and similar regulations.

AI orchestration, not “AI everywhere”

Sariio does not let the front-end talk to AI models directly.

Instead, all AI work flows through an AI orchestration service that:

• Builds a context packet for each call (preferences, history, context text, and aggregated metrics where relevant).
• Ensures no direct identifiers enter the packet.
• Selects the right prompt and model for the specific task (Mirror narrative, Map coaching, Compass scenario analysis).
• Runs pre-call and post-call checks to enforce tone, scope, and safe output.

This architecture makes AI use:

• Transparent and auditable.
• Easier to improve centrally.
• Safer from a regulatory and reputational point of view.

For investors, that means lower technical risk and a clearer story to compliance-conscious customers.

The big-data flywheel: how value compounds over time

The transformational opportunity lies in the data flywheel that Sariio creates.

Step 1 – Individuals generate high-quality preference data

Every Mirror, Map, and Compass survey produces:

• Clean, structured scores across twelve preference pairs.
• Factor and archetype profiles.
• Time-stamped history when people retake the survey.

Because the survey is short, well-structured, and randomised (item order and left/right positioning change on each run), the data remains robust over time and across contexts.

Step 2 – Organisations link preferences to their own outcomes

In Compass, organisations can choose to connect MAPS data with:

• Engagement scores from their existing survey provider.
• Retention and absence data.
• Performance bands or other outcome indicators.

This is where correlation becomes interesting.

Research already shows strong statistical links between engagement and business outcomes: higher productivity, profitability, and lower turnover.

At the same time, research notes that engagement–performance links are largely correlational, with debate over causation.

Sariio does not claim to solve that debate overnight.

It offers something more grounded: a way to systematically explore how different motivation and preference mixes relate to engagement and outcomes inside each organisation.

Step 3 – An anonymised, cross-client dataset

With explicit opt-in, Sariio aggregates data across participating clients:

• Preference distributions by sector, region, role, and size.
• Engagement and outcome patterns at an anonymised level.
• Changes across time and across waves of organisational change.

This supports questions such as:

• Which combinations of Connecting, Thinking, Deciding, and Implementing patterns show up most often in highly engaged, high-performing teams?
• Do certain “MAP mixes” perform better in specific contexts (for example, high-regulation environments vs high-growth product teams)?
• How do preference patterns shift before and after major restructures or strategic pivots?

Over time, this dataset becomes a differentiator:

• It underpins annual reviews that position Sariio as a thought leader on motivation and engagement.
• It informs product improvements and AI guidance.
• It builds an evidence base that goes beyond anecdotes and isolated case studies.

The more organisations that participate, the more informative these “heatmaps” become.

From transactional tool to transformational system

For investors, the key question is whether Sariio is another survey vendor or a platform with compounding value.

Three points matter here:

1. Revenue is tied to sustained use

Sariio’s revenue model follows the maturity curve:

• Mirror builds awareness at zero cost to the individual.
• Map adds managerial and coaching capability on a per-seat basis.
• Compass adds organisational analytics and integrations on an enterprise subscription.

The same MAPS data supports all three levels.

As organisations see value in team and organisational decisions, they have strong incentives to keep running MAPS surveys, not treat them as a one-off.

2. Data improves guidance over time

Each cycle improves:

• The quality of AI suggestions for individuals and managers.
• The accuracy of signals and heatmaps in Compass.
• The ability to benchmark against similar organisations without exposing any client’s proprietary data.

This transforms Sariio from a transactional reporting tool to a learning system:

The more it is used, the more precise and contextually useful it becomes.

3. A measured approach to correlation and causation

Sariio does not over-claim.

The platform is based on robust evidence that shows a link between engaged employees and better performance and lower turnover.

It then focuses on establishing a clean and consistent measurement of preferences and motivation that can be aligned with engagement and outcomes over time.

In other words:

• Short term: Sariio provides practical, context-aware guidance that enhances conversations and decisions today.
• Medium-term: Organisations identify where preference–role mismatches and team design issues undermine engagement.
• Long-term: Accumulated data support clearer views on which MAP mixes align with the outcomes that employees and organisations aim for.

For investors, this is the core of the proposition:

A product that delivers value immediately at the individual and team level, while building a defensible, anonymised data asset that strengthens the brand, the product, and the economics over time.