AI is rewriting work. The organisation is falling behind.
WORK IS CHANGING
Job design is lagging.
84%
of surveyed companies have yet to redesign jobs around AI.
LONG-TERM AMBITION
Short-term workforce planning.
ONLY11%
of surveyed organisations take a long-term view of workforce
planning.
Today's workforceFuture needs
THE COST IS ALREADY HERE
41%
of daily time reported as work that adds no organisational
value.
Separate studies · different populations and measures
BUILDING AND WINNING THE CATEGORY
The operating system for work.
Across people, AI and agents.
Company + employee dataWhole-company MapConnected Blueprints
THE FOUNDATION
Make the work visible. Make every contribution count.
Anna, Uniio’s AI guide, combines company context with employee
conversations. Reviewed contributions build the Work Map and each
employee’s Work Profile.
COMPANY CONTEXT
HRIS
Workflows
Company systems
Living Work Map
AnnaLearning from real workILLUSTRATIVE CONVERSATION
ANNA
Which decisions rely on your judgement?
BRAND MANAGER
I compare market evidence and recommend which opportunities
we should pursue.
REVIEWED BY EMPLOYEEMarket judgementTurning market evidence into priorities.
REVIEWED EVIDENCE
WPPERSONAL WORK PROFILEEMPLOYEE OWNED
Brand Manager
A growing record of real contribution.
01
JUDGEMENTMarket judgement
Turning evidence into commercial priorities.
✓
02
RESPONSIBILITYLaunch recommendations
Preparing the decision for the commercial owner.
✓
03
NEW CAPABILITYAI oversight
Reviewing AI output and handling exceptions.
✓
3reviewed work examples Skills · decisions · contributions
A personal asset. A reason to keep contributing.
A living record of skills, judgement and contribution. A
foundation for development and future opportunities.
FROM UNDERSTANDING TO DESIGN
Understand the work. Design what comes next.
THE WORK BLUEPRINT
Turn knowledge into execution.
Who does what. Who decides. What success means.
LIVING WORK MAP
The work, understood.
Company contextReviewed contributionsWork and handoffs
→
WORK BLUEPRINT
How the work runs.
OUTCOMEWhat to achieve
EXECUTIONWho does what
AUTHORITYWho decides
PeopleAIAgents
Reviewed work → Approved design
READY TO CONNECT
✓
A shared design. A named owner.
Activated after human approval.
From shared understanding to approved execution.
LIVE CALIBRATION
Find better ways to work. Keep people in control.
Work reveals. Anna proposes. People approve. Results inform.
BUYING TRIGGERLive executive mandate · named outcome owner
THE TEAM
The team to build the category.
CO-FOUNDER · CAIPO
David Beckmann
BolighedPrior exitJuice4 years of HR tech insightValifiedCPO · ESG reporting tech
CO-FOUNDER · CCO
Mik Thobo-Carlsen
Tattoodo.comWorld’s largest tattoo communityMindpoolPrior exit · 3 years of organisational intelligenceUnited NationsAdvisor
NOW / BUILD & DELIVERCEOCompany building, capital and executionOPEN
CTO / technical co-founderArchitecture and applied AIOPEN
Founding engineersMapping, integrations and reviewOPEN
NEXT 12 MONTHS / REPEAT & SCALECustomer delivery leadWhole company rollout and acceptanceOPEN
Commercial teamEnterprise sales and category developmentOPEN
Product & customer specialistsCapacity matched to customer demandOPEN
CURRENT STAGE
Qualified customer pain. Detailed prototype in development.
THE FINANCING PLAN
€1m to earn the next round.
RAISING / PRE SEED€1m
Capital buys evidence in sequence.
Five paid design partners is the commercial target.
01Working Map product02Paid scope and accepted Maps03One implemented improvement04Paid continuation and measured delivery effort
Financials v4.2 · forecasts and funding milestones remain conditional
A future where AI expands human potential and creates lasting
prosperity.
Mission
We build the operating system for people, AI and agents to create
greater value together.
For people
A reviewed record of real contribution, skills and outcomes. A
personal Work Profile to own, export and share.
For organisations
A living understanding of work, clearer authority and learning that
improves future designs.
DAILY TIME · DELOITTE
41%
Respondents report this share of daily time on work they see as
contributing no organisational value. Deloitte published the article
on 3 November 2025, citing its Trends research.
This is self-reported time, not an audited waste estimate or an
Uniio savings forecast. The article does not state a separate
sample size for this item.
Surveyed companies that have not redesigned jobs around AI
capabilities. The denominator is companies, not jobs or employees.
The 2026 State of AI survey covered 3,235 business and IT leaders in
24 countries and six industries in August and September 2025. Its
AI-active sample is not a census of all companies.
HR Monitor 2026 reports that only 11% of organisations take a long-term
perspective on workforce planning. The study surveyed approximately
1,300 HR professionals and 5,500 employees across ten countries,
primarily in Europe, with US and China comparisons. Published 8 June
2026.
The horizon graphic illustrates the difference between current workforce
planning and future needs. It is not a measured time series, nor does
11% mean the remaining organisations have no plan.
Supporting evidence: clarity of expectations
In Q2 2025, 47% of US employees strongly agreed that they knew what was
expected of them at work. Gallup published this finding on 5 August
2025. The remaining respondents must not be described as knowing nothing
about their work.
The studies show separate dimensions of the problem: reported low-value
work, incomplete redesign and limited strong agreement on expectations.
They do not establish a causal chain or validate Uniio’s product.
Uniio’s commercial proposition is paid whole company mapping followed
by ongoing improvement. Customer evidence must establish released
capacity, realised savings, adoption and renewal.
A versioned design for delivering an outcome. Uniio connects the
work, allocation and controls. A named leader approves the version and
activation date.
Outcome
Value to create and measures of success
Work
Tasks, dependencies, decisions and handoffs
Allocation
People, AI, agents, workflows and partners
Authority
Named owners, mandates and approval boundaries
Controls
Permissions, stop rules and escalation
Learning
Observed results, reviewed evidence and version history
One organisation, one evidence base
Company systems provide context. Employees explain their actual work
and review the proposed record. Leaders receive the Map, capability
and dependency views, and a prioritised opportunity register.
Employees receive their personal Work Profile.
People review their own evidence before it enters the shared Map.
Coverage and open questions remain visible.
A defined delivery boundary
Uniio provides mapping software and a repeatable onboarding
process. The customer appoints an executive sponsor, data owner and
rollout lead. Acceptance reviews coverage, evidence quality and the
usefulness of the first leadership decision.
The financial plan allows one quarter between signature and platform
activation. Design partners establish actual employee effort and
time to accepted value.
McKinsey · 2026
27% → 40%
Respondents at companies with annual revenue of at least $1bn
reporting agent scaling in one or more functions, 2025 to 2026.
Leaders recognising intentional human and AI interaction design as
important, and those who say they lead in this area. These describe
perceived importance and maturity.
Share reporting fundamental workflow redesign. McKinsey defines high
performers through significant value and at least 5% EBIT impact. This
is an association between redesign and performance.
The category we are building
As agents enter execution, companies need a persistent system for work
design, authority and learning. Uniio is building for this structural
transition. Paid whole company mapping and recurring platform adoption
will establish demand in the initial 300+ employee market.
Establish the work design layer as organisations build around people, AI
and agents.
Company systems HRIS · ERP · collaboration
Uniio Work design · authority · calibration
Execution People · AI · agents · workflows
Category definition is The category we are building. Customer adoption,
competitive evidence and repeatable economics determine the position we
can earn.
A structural universe
Uniio’s bottom up model combines company size bands in selected
sectors. The model uses a 100+ employee threshold. The initial sales focus remains organisations with 100+ employees and an
active leadership mandate. Counts precede qualification for
transformation intent, purchasing power and sales readiness.
EU and Nordic estimates use companies with 250+ employees plus 32%
to 38% of the 50 to 249 employee band. That proxy is derived from
detailed UK size bands. EU finance is outside the available
calculation.
The 140,000 to 220,000 global range remains an expansion scenario.
Revenue sizing additionally requires validated employee counts and
willingness to pay.
A launch needs extra review. The result enters the history. Anna
proposes an earlier check. A leader approves the next design. The next
outcome tests whether repeat exceptions and time to acceptance improve.
The network graphic shows how a relevant learning can inform several
designs. Applicability, evidence quality and human review govern reuse.
Revenue impact is measured through customer outcomes.
Company intelligence
The near term asset is each customer’s reviewed work evidence,
approved designs and observed outcomes. Retention is a hypothesis
grounded in usefulness and accumulated context. Customer value and
renewal behaviour will test it.
Future model development
Purpose permitted, protected aggregate patterns could support
Uniio model development over time. Training depends on explicit
rights, privacy controls and validated technical benefit. Company
evidence remains governed by customer permissions.
The operating boundary
Uniio holds the approved design, named authority, permissions and
version history. Connected customer systems execute the authorised
workflows. Results return as evidence for the next proposed
adjustment.
Integration setup and organisational change have an explicit
delivery scope. Leaders retain authority over activation and
employment decisions.
A personal return for participation
Employees review and correct their evidence, access their personal
Work Profile, export it and choose what to share. The company Map
uses reviewed contributions within the permitted purpose.
Cross company model learning requires protected aggregation,
explicit rights and evidence that it improves recommendation
quality.
INITIAL MAPPING
An accepted company baseline.
A reviewed Company Map, priority outcomes and an agreed delivery boundary.
ANNUAL PLATFORM
A recurring improvement system.
Live understanding, calibration and connected learning begin after mapping acceptance.
SEPARATELY SCOPED
Implementation stays explicit.
Blueprint design, customer implementation and large custom integrations are priced to the agreed scope.
Initial mappingDKK 199,900
Map & Live / annuallyDKK 99,900
First year total / Map & LiveDKK 299,800
Design & Calibrate / annuallyDKK 149,900
Connected / annuallyDKK 199,900
DKK per employee in each band. Mapping is one time. Platform rates are annual.
Employee band
Mapping
Map & Live
Design & Calibrate
Connected
1 to 300
1,999
999
1,499
1,999
301 to 1,000
1,499
699
999
1,299
1,001 to 3,000
999
399
599
799
3,001+
699
299
449
599
01
Sell
Five paid whole-company Maps with an executive mandate and named outcome owner.
02
Prove
Implement one priority redesign and record customer acceptance, elapsed time and total effort.
03
Continue
Sell a paid calibration period. Detect a meaningful change and support the next decision.
04
Repeat
Deliver comparable scope again with less bespoke work before widening the segment.
Initial customer
A Danish knowledge-intensive company with 100+ employees, a live strategic change, a CEO sponsor, a named operational owner and approved access to the relevant work evidence.
The Company Map creates the shared context. One priority outcome supplies the first complete proof of changed work.
Evidence to record
Signed scope, customer acceptance, implementation ownership, Uniio hours, customer hours, time to implemented change, outcome quality and a subsequent payment.
Comparable deployments should show which product components were reused, configured or newly built.
Direct proof first
Founder-led sale
David secures the executive mandate and keeps the buying problem concrete.
Qualified route
Implementation partners
Test one joint opportunity with explicit product ownership, implementation responsibility and commercial terms.
Scale condition
Repeatable economics
Broaden the channel when direct delivery has established an accepted result and a controllable scope.
Build the Mapping product
David owns product direction and applied AI priorities. Recruit a
technical lead, product engineers and applied AI capability for
interviews, reviewed evidence, company integrations and the Map.
The base model budgets four product and engineering roles, one
commercial role, one delivery role and two leadership or
administration roles by the end of 2026.
Sell and deliver the first five
Founder led selling secures the executive mandate. A delivery lead
coordinates data access, rollout and acceptance with the customer.
Commercial and technical leadership hiring follows the funding and
delivery gates.
CEO, CTO and CCO are open positions. Role coverage can begin through
hands on founding hires and fractional finance support. Every
planned hire sits within the headcount budget.
Founder history Experience supplied by David
Beckmann. The Bolighed exit and Juice and Valified roles relate to his
prior work.
Uniio status Customer pain has been qualified. A
detailed prototype is being built. Five paid design partners is the
next commercial target.
Headcount by capability · base plan
Capability
2026
2027
2028
2029
2030
Product & AI
4
17
40
85
140
Sales & marketing
1
10
45
135
245
Delivery & success
1
14
40
100
200
Leadership & operations
2
5
15
30
50
Forecast from Financials v4.2. Hiring follows funding, sales
productivity and delivery capacity.
€1m PRE SEED
Build and deliver.
50% product and applied AI, 20% customer rollout and acceptance, 10% founder-led sales and partner qualification, 10% company operations and security, and 10% contingency.
EVIDENCE BEFORE SEED
Make the case inspectable.
Paid scope, accepted Maps, one implemented result, a paid continuation period and measured delivery effort across comparable work.
FUNDING CONTROL
Stage the cost base.
The base model assumes a €5m Seed in Q1 2027. Moving it one quarter creates an approximately €227k fixed-spend shortfall after the corrected commission formula.
Operating response.
Hiring and customer deployment follow secured cash, paid scope and delivery capacity. A monthly operating plan should be locked before commitments are made against the Seed timetable.
Series A follows repeatable delivery, platform conversion, retention and a credible sales motion. Series B remains an acceleration option with zero proceeds in the base plan.
The closest competitors already connect work intelligence, redesign and feedback. Our thesis is a more coherent operating model around the outcome — not exclusive ownership of AI, governance or continuous learning.
Skan AIWork context, Blueprints and production agents
What is already there
Observed work feeds a context graph, Blueprint opportunity discovery, process intelligence and agents. Skan describes testing against real cases, continuous updates, human oversight and auditability.
What Uniio must demonstrate
A complete outcome design: employee-reviewed evidence, human and agent responsibilities, economic assumptions and a named owner's approved version. Cross-Blueprint reuse must be visible in the product, not just in the positioning.
ReejigA Work Operating System with a seven-stage loop
What is already there
Map, Analyze, Build, Run, Measure, Log and Update. Reejig combines a work context graph, human–agent workflow design, adoption, measurement and an audit trail. Its architecture updates as work changes.
What Uniio must demonstrate
Why an outcome-owned Blueprint makes a better unit of redesign: test the whole design, approve the change and make relevant learning reusable across outcomes. “A Work OS” and “a continuous loop” are not differentiators by themselves.
SorocoWorkflow observation, simulation and redesign
What is already there
Soroco describes a continuous Plan–Build–Run loop spanning human and agent work. It maps workflow variations, links activity to effort and cost, and simulates changes before implementation.
What Uniio must demonstrate
That the governed object is the whole outcome design, not just its workflow or agent. The demo must connect live results to a proposed version, an accountable approval and a relevant improvement in another Blueprint.
Sources checked 16 September 2026. Product descriptions establish public positioning, not independently tested performance. Unknown is not absent. The three market signals measure different things and must not be added together.
The map includes all 15 researched companies. The horizontal axis asks what is redesigned: roles and tasks, connected workflows, or the whole work design for a business outcome. The vertical axis asks where learning is reused: within a workflow, or across work designs. Placements are a qualitative assessment of public product emphasis, not measured scores. Distances between points have no quantitative meaning.
Uniio's top-right marker is its intended position: outcome-owned Work Blueprints that carry relevant, tested learning into another design through the receiving owner's approval. It is a product thesis to demonstrate, not a shipped-capability or performance ranking. Skan, Reejig and Soroco already connect several of these elements; the diagram does not establish exclusive ownership of the combination.
Talent platforms reuse skills and work context; automation platforms reuse workflow and agent components; work-design platforms connect observation, redesign and operational feedback. These are different forms of reuse. The source-linked profiles below explain each starting point. Several competitors are themselves AI-native, and broad enterprise platforms can support additional use cases through customer implementation.
Orgvue centres organisational design and workforce scenarios; TechWolf connects skills and work intelligence; Draup Etter models workforce, cost and capacity changes; Visier brings people analytics and planning. Their intelligence can update continuously: this placement does not mean static data or no operational use.
Talent orchestration Context reused across opportunities
Talent intelligence connects to ongoing matching, mobility and workforce action. Gloat also extends into work orchestration; Eightfold links skills, opportunities and talent decisions. Both reach beyond the people-data starting point shown here.
AI & process improvement Opportunity, adoption & ROI
Workhelix measures AI opportunity, adoption and ROI, including real-time usage and agent tracking. SAP Signavio supports process analysis, modelling and transformation. Their main lens in this comparison is deciding where and how to change work; both also support ongoing improvement.
Work design & execution Redesign with operational feedback
The closest product overlap: evidence, work context, redesign, agents and continuous feedback. They are highlighted for relevance, not market share or a claim that the other players are less capable. The full profiles preserve features, commercial signals and sources.
Process intelligence, workflow execution, automation and enterprise distribution. Microsoft is considered through Power Automate and its wider agent ecosystem; ServiceNow through its workflow platform. These broad portfolios span several regions. Customers may extend an existing platform instead of buying a separate work-design layer.
Services & internal build
Consultancies · transformation teams · internally built agents
The practical alternative may be a project, an internal team or no purchase at all. Time to a useful approved redesign matters as much as feature breadth.
Qualitative positioning by Uniio, based on public product material reviewed 15–16 September 2026. Product portfolios overlap; placements have not been independently benchmarked. Consultancies and internal teams remain alternatives in the analysis. The map does not rank market share, installed base, valuation or measured customer results. Uniio's intended position remains to be demonstrated.
The differentiator is the operating model. The economic thesis is compounding reuse.
Outcome, not job title.
A Work Blueprint connects the outcome, human judgement, AI and agents, decision rights and success measures in one versioned design.
Reality changes the design.
Live work produces evidence. Calibration proposes a change. Review separates a useful suggestion from an approved operating decision.
Learning travels with context.
A relevant improvement can inform another Blueprint. Its evidence, applicability and ownership travel with it; the receiving owner approves adoption.
The next design starts ahead.
Reuse should reduce rediscovery and repeated errors. Faster redesign, lower change cost and stronger execution are hypotheses to measure, not promised exponential gains.
What the demo must prove.
Capability
Competitive baseline
Uniio proof to show
Map & design
Work graphs and human–agent workflows already exist.
Reviewed work evidence becomes a complete outcome-owned Blueprint.
Test & approve
Simulation, oversight and auditability are already offered.
Compare whole-design alternatives, assumptions and trade-offs; approve a specific version and mandate.
Live & calibrate
Continuous monitoring and updates are already offered.
A real exception becomes a traceable proposal, not an automatic override of the approved design.
Review & compound
Context reuse and feedback loops are already offered.
Follow one learning into a second Blueprint, through applicability checks, owner approval and measured results.
Uniio is shown as a target architecture and competitive thesis. This chapter does not claim that all capabilities are shipped, that the model is unique, or that competitors cannot reproduce it. The evidence must come from the product and customer outcomes.
MODEL CORRECTIONBookings now include platform conversion.
ARR is unchanged. Commission expense and downstream cash reflect the corrected calculation.
REVENUE QUALITYMapping and platform stay separate.
Mapping is a one-time accepted deliverable. ARR contains annual platform subscriptions only.
FUNDING DEPENDENCYSeed timing remains material.
A one-quarter delay creates an approximately €227k fixed-spend shortfall in the corrected base model.
2030 ARR
2030 revenue
2030 EBIT
80%
Base mapping to platform conversion.
12% → 8%
Base annual logo churn as the product matures.
6 + 3 months
Base sales cycle to signature, then activation.
Downside: 60% conversion, 18% churn and a nine month sales cycle.
Ambition: 90% conversion and higher sales productivity.
Base qualified opportunities grow from 240 in 2027 to 6,500 in 2030.
The plan includes 2, 8, 25 and 50 productive partner organisations
respectively, with six months to ramp. Partner feasibility is a pre
seed qualification gate.
Delivery capacity governs activation. Five design partners are
targeted for signature in 2026 and activation in 2027. Signed mappings
awaiting delivery remain in backlog.
Initial mapping revenue funds a substantial share of expansion.
Willingness to pay, accepted delivery and platform conversion are
critical sensitivities. Shared model development and engineering sit
in separate budgets from mapping compute.
Cash is before corporate tax and VAT. Funding includes 2.5% fees and a
three month operating reserve. The downside requires a funding or
spending reset.
Financials v4.2 · Forecast snapshots · ARR contains platform
subscriptions. Total revenue includes separately accepted mapping.
Build a record of skills, judgement and contribution
Export a personal profile; company confidential material stays
protected
FOR THE COMPANY
A reviewed Work Map.
Understand actual work and handoffs
Connect capabilities to outcomes
Design the organisation with evidence
Talk with Anna→Review the evidence→Build your record
Illustrative product experience. Employee review comes before evidence
enters the Map. Company use follows the relevant purpose and
permissions. Each additional reviewed conversation can strengthen the
employee’s own record.
01 / DELIVERY
Lower cost per outcome.
Redesign removes duplicated effort and assigns appropriate tasks to
AI and agents. Savings depend on adoption, oversight and operating
costs.
02 / CAPACITY
Put time back into growth.
Released capacity can support customers, product development and new
markets. Capacity becomes revenue when demand and commercial
execution convert it.
03 / CONNECTED LEARNING
Reuse what improves the work.
A reviewed result can inform several relevant Blueprints. Each owner
reviews the proposed change. Local outcomes become evidence for
subsequent designs.
How to model the opportunity
Company baseline+Realised work improvements−Adoption and operating costs
Use the company’s revenue, workforce and cost base. Compare a
business-as-usual trajectory with a Uniio scenario over the same
period. Model adoption, time released, reinvestment, revenue conversion
and recurring costs explicitly.
Released labour capacity is an allocation pool: redeployment, hiring
avoidance and cash savings represent different uses of that capacity.
Revenue generated from redeployment is shown separately; its
contribution to profit depends on margin.
Compounding is the mechanism.
Connected learning may accelerate improvement and broaden its reach.
Exponential revenue growth remains a possible scenario, conditional on
demand, execution and measured gains. Product validation and customer
evidence will establish the realised effect.
The intelligence behind the improvement
YOUR COMPANY
A foundation you own.
Company systems provide context. Conversations with employees reveal
tasks, judgement, capabilities and dependencies. People review the
evidence entering the Work Map.
A Blueprint turns this foundation into a proposed work design:
outcomes, allocation across people, AI and agents, authority,
handoffs and controls. Sufficient evidence, review and named
approval determine readiness for activation.
UNIIO
Intelligence specialised in work.
Uniio’s intended model layer specialises in understanding,
designing and improving organisational work. It brings models and
methods to each company’s context.
Company data remains owned by the company. Any training or
cross-company use requires explicit rights and permissions. The
shared model capability is a development ambition; its performance
and training scale remain to be demonstrated.
Live understanding. Governed change.
LIVE keeps work knowledge current. Connected, approved Blueprints give
calibration an explicit design to observe and improve. Anna proposes
changes using reviewed signals and observed results. Named people
approve changes before activation. Each subsequent result adds evidence
for the next decision.
YOUR COMPANY
A foundation you own.
Company systems provide context. Conversations with employees reveal
tasks, judgement, capabilities and dependencies. People review the
evidence entering the Work Map.
A Blueprint turns this foundation into a proposed work design:
outcomes, allocation across people, AI and agents, authority,
handoffs and controls. Sufficient evidence, review and named
approval determine readiness for activation.
UNIIO
Intelligence specialised in work.
Uniio’s intended model layer specialises in understanding,
designing and improving organisational work. It brings models and
methods to each company’s context.
Company data remains owned by the company. Any training or
cross-company use requires explicit rights and permissions. The
shared model capability is a development ambition; its performance
and training scale remain to be demonstrated.
Live understanding. Governed change.
LIVE keeps work knowledge current. Connected, approved Blueprints give
calibration an explicit design to observe and improve. Anna proposes
changes using reviewed signals and observed results. Named people
approve changes before activation. Each subsequent result adds evidence
for the next decision.
