Operational routine
Starting point
During growth
APRIXITY / THE METHOD
Turn your knowledge into a system that thinks with you.
Your judgment isn't just gut feeling. It's a highly precise rulebook that only exists in your head. We extract that DNA and make it scalable for your team.
Demo: Osiris Knowledge Graph · anonymized real data
Demo: Osiris Knowledge Graph · anonymized real data
Which deals have the highest risk of losing commission?
Knowledge graph calculates value-at-risk across all active seller deals
3 deals in your pipeline have a combined €63,450 value-at-risk. Main driver: owner contact overdue. Deals with a clean data path have a health score of 48 — blocked deals only 34. The difference: 5x in marketing friction.
What exactly separates the fast deals from the slow ones?
Knowledge graph compares 19 fast vs. 38 slow closings across activities, messages and deal phases
Clear deals: higher health score, richer activity history, reliable documents. Blocked deals: 30% less health, often BENCHMARK_GAP — asking price deviates from market. Your biggest lever: 11 open deals are missing a price adjustment. That's €4.2M in volume, standing still.
THE SHIFT
The strongest technology stack in business is compressing real work.
Measurable compression across image, text, app and data work.
Automate Images
Professional 3D Floor Plans
€10 / piece + manual handoff.
Flat rate. Automated.
Texts From Context
Listing Creation
Copy-paste from 5 tabs.
One single instruction.
Apps Without Code
Real Estate Tools by Instruction
"Build me a tool" = €10,000 agency invoice.
"I need..." = Finished app from a precise instruction.
Understand Data
Property Valuation & Renovation
Appraiser appointment + gut feeling.
Data check + instant estimate.
Each of these examples requires only two things:
Natural Languagea good prompt
+
Contextknowledge of your business
The question is: Why aren't you benefiting from this yet?
WHY NOW
Your knowledge is your capital. While it lives only in people's heads, it's a liability.
Two forces work against you — and both hit exactly what you sell.
Your knowledge decays quietly
What your team gets right — who to call, when, why — lives only in people's heads. It feels self-evident, so no one writes it down. That's exactly why it decays: with every departure, every overload cycle, every decision no one can reconstruct anymore.
Your Customers
Why should your customer pay for something they can do themselves with AI? Market analysis, copywriting, data processing — that's no longer a premium service. The question is: What do you deliver that AI can't?
Your Only Defense
Become irreplaceable. Not by working harder — by working better with your clients. When your team stops managing data and starts building relationships, you deliver something no algorithm can replicate.
Everyone has raw data. The difference is whether your knowledge is linked and usable — or trapped in people's heads.
THE PROBLEM
The industry sells you features. You need intelligence.
An optimized work step doesn't solve a process yet.
Powerful AI tools are available. But in your daily work, you barely feel any relief. Why? Because AI is chopped into isolated work steps instead of integrated into your processes.
MAIN PROCESS
Sub-process A
Sub-process B
Sub-process C
Work step 1
Write email
Work step 3
Generate text
CURRENT AI USE
BUSINESS IMPACT
EASE OF IMPLEMENTATION
MAIN PROCESS
Sub-process A
Sub-process B
Sub-process C
Work step 1
Write email
Work step 3
Generate text
CURRENT AI USE
BUSINESS IMPACT
EASE OF IMPLEMENTATION
The Dilemma
On the right you see the common pattern. AI generates texts or emails. That's easy to implement, but has almost no business impact. You've accelerated one task, but the process is still stuck.
The Hurdle
The real leverage lies in the middle: Automating entire sub-processes (e.g. "listing creation" instead of just "write text"). But to chain multiple steps, AI needs two things it currently lacks:
CONTEXT(How do the steps connect?)
TOOLS(Access to all necessary systems)
Without this connection, they remain isolated solutions.
THE GREATEST CHANGE IN OUR WORKING LIVES
We make what this means for your company concrete. AI does not only change individual tasks. It changes how knowledge moves through the operation, how decisions are prepared, and where people direct their attention.
We start where work quietly ties up your margin: gathering information, transferring values, resolving questions, and tracking approvals. When this work repeatedly passes through the same experienced people, it costs more than time. It slows decisions, holds capacity, and leaves customers waiting for results.
We find the first intervention, limit it to a real workflow, and implement it in your operation. Before we begin, we define how you will recognize its contribution. Sometimes that needs AI. Sometimes it first needs a clear rule or a better handoff. Consequential decisions stay where they belong: with a person who takes responsibility.
WHAT WE ASK TO BE MEASURED BY
An intervention must show which work disappears afterwards.
That is why we do not begin with the model, but with the work: searching, transferring, asking, and tracking. We remove what the result does not need. What remains receives a clear rule, a verifiable basis, and accountable approval. Before the intervention, we define how you will recognize the change. That may be less effort per case, a faster answer to a ready-to-buy inquiry, or a more complete information base before a consequential decision. The criterion follows your case; it is not a result promised in advance. We then choose the smallest intervention that can test this criterion meaningfully. It may be AI. A clear rule may be enough. If a step needs human judgment, it stays with a person while we reduce the repeated work around it. This makes leadership concrete: you know what the system takes on, which effect is tested, and which decision nobody makes in your name.
The bottleneck moves through the workflow.
Value stalls here.The offer waits for calculation.
Demand
Delivery
Your attention is consumed here.
Search & re-entryCoordinationReleased capacity
Capacity moves to the bottleneck.
↩ Recommendations feed new reach.
01
The stages stay fixed. The red marker shows where value or revenue stalls in the flow.
Trace an example back to its sources
Illustrative work object · no customer data · static
Which basis supports the next step?
This fixed example shows how source, finding, open question and human review belong together. It accepts no input and runs no AI system.
Select a finding
Sources in the same case
Source available in illustrative exampleA · Work note · SelectedSource excerpt
The draft is ready. Approval has been requested.
Reference: Example file / approval section
Source context: Work note with the draft status and requested approval.
B · Project plan · SelectedSource excerpt
Publication of the draft is planned.
Reference: Example file / next step section
Source context: Plan text describing the intended flow; it is not an approval record.
C · Process observationSource excerpt
When a basis is missing, a follow-up remains open until someone checks the item again.
Reference: Example file / observation section
Source context: Observation of the flow; it is not a performance metric.
Finding
- Observation
- Both sources mention a draft and a plan. Neither source confirms approval.
- Finding
- The draft exists; permission to publish it is not established.
- Interpretation
- A finished draft and a planned step do not establish approval.
- Open / unknown
- It remains unknown who can approve and whether the current version is final.
- Next human check
- The responsible person checks the version and records approval at the source.
- Example boundary
- Illustrative status. It says nothing about a real project or publication.
Responsibility stays with the person
A responsible person checks the source, version and approval. This example decides nothing and publishes nothing.
Proposed · not measured
Before / proposed after
This process view is explicitly illustrative. The after-state is a proposal, not measured customer impact.
- Intake
Request arrives
- Source basis
Reconcile data from several sources by hand
- Follow-up
Send a follow-up when a basis is missing and wait for another check
- Decision
A person checks the next step
- Marked change
- Source reconciliation and follow-up run in sequence and remain hard to see.
- Removed loop
- No check is silently removed. Only the invisible repetition should become traceable.
- Human responsibility
- A responsible person checks source completeness and approves the next step.
- How the change would be checked
- Observe whether the follow-up becomes traceable without skipping quality or approval checks.
Check your own process note locally
Automate logic. Unfold humanity.
Verifiable work moves through the right workflow. That leaves more attention for people and responsible decisions.
Most people use AI to remove people from the interaction. We use AI so the right person can be more present when it matters.
Products, services, and information are becoming easier to compare. The difference emerges on the path to a decision: through attentive listening, complete context, and someone who takes responsibility.
That creates value twice. It strengthens the experience customers choose today. And it builds the difference that matters more as results and offers become increasingly similar.
Recovered time does not automatically belong to the next task. Some goes into better preparation. Some into reflection and recovery. When many things change at once, good judgment needs more room, not less. We create that room before overload becomes the way of working.
Not everyone needs more customer contact. Some create their greatest value in conversation, others in analysis, design, or a careful decision. People should be able to do more of the work where they are strong and where their contribution is visible.
A recurring approval needs a clear rule first. Lost information needs a reliable handoff. AI enters where changing information must be understood, connected, and prepared for the next step. We do not sell as much technology as possible. We decide what genuinely carries the work.
Target state
Starting point
During growth
Verifiable system workHuman value work
Verifiable work stays in the right workflow while human attention stays with judgment and relationships.
Direct recovered capacity toward what your customers choose you for:- 01
Full attention in the client conversation
- 02
Calm judgment for consequential decisions
- 03
Quality stays visible and verifiable
- 04
Leadership that carries relationships
Technology works in the background. People remain visible where trust is built.
THE DIAGNOSIS
The missing organ of your company.
To automate logic, your company needs five capabilities. Two of them are missing — and that's exactly where your team gets stuck.
01
STORE
Store
Available
CRM / ERP
02
RELATE
Connect
Missing
Who connects?
03
REASON
Think
Missing
Who draws conclusions?
04
COMMUNICATE
Speak
Partial
ChatGPT (Isolated)
05
EXECUTE
Act
Manual
Who executes?
Of course you and your team master these disciplines. But should you really sacrifice your cognitive capacity for data linking?
THE ARCHITECTURE
THE MISSING LINK.
We don't replace your software. We give it a brain.
The LLM of your choice
on steroids
Understands your natural language. Executes your commands.
Available (Partially used)
INTELLIGENCE LAYER
THE MISSING PIECE
The context engine. Connects isolated data into real knowledge. Takes over RELATE and REASON.
I add it.
OPERATIONAL LAYER
The Body
Your CRM, ERP, Files. Single Source of Truth.
Available (Silo)
"Perfection is achieved when there is nothing left to take away."
APRIXITY IMPACTS FRAMEWORK
AUTOMATION IS THE TARGET.
NOT THE STARTING POINT.
IMPACTS began with five First Principles steps for physical production. Agentic and knowledge work need an added step: after removing and ordering work, supply the context required to decide and act where the work happens.
I
IDENTIFY
Define target and constraint
M
MINIMIZE
Remove unnecessary work
Pre-work
P
PERFECT
Make flow traceable
A
AUGMENT
Supply context at the point of work
Context
C
CONSTRUCT
Build smallest intervention
T
TEST
Check against target
Proof
S
SCALE
Embed after proof
01Remove what does not serve the target
Question each requirement, step and handoff. Record what can be removed before selecting a tool.
Use when:
the current flow contains duplicate work, queues or unclear handoffs
the current flow contains duplicate work, queues or unclear handoffs
02Supply context where the decision happens
Route or traceably derive the required data, rules, exceptions and evidence. Keep source, validity and check visible; missing context stays open.
Use when:
a dependable decision needs context that is late, scattered or held only by people
a dependable decision needs context that is late, scattered or held only by people
Only after the workflow and its knowledge are inspectable should a system act.
I build what I use myself.
My own first customer.
APRIXITY / CUSTOMER 00
Melvin Voigtländer
Founder of Aprixity
LinkedInAprixity runs on my own knowledge graph and automations. I apply the same principle to my operation: understand the workflow first, bound the intervention, and test it against the real result.
A decision from my development work: why a file check does not replace reviewing the result. →
Background and approach
In the automotive industry, I saw how much knowledge a company can hold and how difficult it can still be to turn that knowledge into a clear decision. My MSc in Management and Information Systems at Cranfield University deepened this question academically.
With Aprixity, I make operational knowledge usable: for a concrete workflow, a verifiable decision, and a first intervention that we also implement. That is why I do not begin with technology selection. I show which step a system can carry reliably, which assumptions remain open, and where your approval stays.
FAQ
Questions before the first step
NEXT MOVE
If you would rather assess the workflow directly, we will look at it together. You receive a reasoned direction for your operation, not a list of tools.
The greatest change in our working lives has begun. It should not push people out of valuable work. It should return the work that needs judgment, responsibility, and human closeness to them.
Apricity means warmth on a cold day. The X stands for execution. We do more than discuss this change. We implement it in your operation.
That is our standard: clear guidance, a workable first step, and more humanity through AI, not less.
NEXT STEP
Ready for the intelligence layer?
Not for everyone. For those who are ready.
Free potential analysis. 7 focused questions.
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