Foundation · Strategy & Capture
Data Readiness for AI
Before AI builds capability, you need the right private data. We deliver the strategy, the process and a standalone capture application for your key roles. Orthogonal to day-to-day work. On your own servers. In your ownership.
- Strategy + capture app + guidance
- Orthogonal to existing IT
- Data on your own servers
- Open formats, no vendor lock-in
- Day rate CHF 2,200
Most AI initiatives do not fail at the AI. They fail at the data foundation. Models are plentiful, swappable, and get better every month. What is scarce is what you cannot buy: your own context.
The decision logic of your senior people. Institutional memory. The heuristics that grew over twenty years and exist nowhere in writing. That is where the leverage sits.
Data Readiness for AI is an enablement offering. Strategy, build, guidance, so that your company captures exactly the data that later carries real AI capability. We deliver a lean, standalone capture application for your key roles. Orthogonal to day-to-day work. Independent of your other IT. And the process that makes sure it actually gets used.
The data lands on your server. Nobody else sees it.
The principle in one sentence
Models age in months. Data ages in decades. Whoever privately captures the right data today has a foundation in two years that nobody else can buy.
What you get
| Component | Delivery | Note |
|---|---|---|
| Data strategy | written assessment | which data, from whom, in which form, for what |
| Role mapping | key roles identified | 3 to 15 people whose knowledge makes the difference |
| Capture application | tailored app | voice, form, plugin or CLI, depending on role |
| Architecture | orthogonal, own servers | no ERP intervention, no cloud mandate |
| Data formats | Markdown, JSON, Parquet | open, portable, platform-agnostic |
| Roll-out guidance | 90-day practice support | so it gets used, not just installed |
| Handover | documentation and training | fully owned by you, no black boxes |
| Day rate | CHF 2,200 per day | build and guidance. Strategy at fixed price. |
Why orthogonal is the load-bearing word
Classic data projects try to extend existing systems. A new field in the CRM. A mandatory comment in the ticketing tool. An extra module in the ERP.
You know the result. User resistance. Half-filled fields. A distorted corpus.
An orthogonal capture application lives alongside the existing world. It does not compete with ERP or CRM. It replaces nothing. It is tailored to one role and one purpose, usually under two minutes of interaction per entry, and harvests knowledge that arises anyway. What the key person is thinking anyway gets captured cleanly for the first time.
Three-stage flow
- Discovery and strategy (1 to 2 weeks, fixed price). Which data are you actually missing? Who can deliver it? Which capture mode fits which role? Output: written assessment and 90-day plan.
- Pilot capture app (3 to 6 weeks, day rate). One role. A lean v1. Productive immediately. Goal: first usable entries in week 4, not in month 12.
- Roll-out and guidance (6 to 12 weeks). Extension to the remaining key roles. Weekly practice check-ins. Adapting the app to real usage. Handover into internal ownership.
When this fits — and when it doesn\'t
Fits, if:
- You have key people whose knowledge carries the company.
- Your data reality is "lives in heads", not "sits in tables".
- You want to run AI sovereignly in the medium term, not just consume it short-term.
- Data protection and IP protection are non-negotiable.
Does not fit, if:
- You are looking for an off-the-shelf SaaS.
- Your relevant data is already cleanly structured. Then go straight to AI implementation.
- You do not want to capture knowledge, only output productivity. Then AI automation fits better.
References
We have pilot deliveries with working data capture flows. Concrete cases — industry, numbers, outcome — we share in the architecture conversation, under NDA.
Inventing anonymised examples on a public page feels dishonest to us. We prefer to talk directly.
Related services
- On-premise AI — when the capture setup should plug directly into local inference.
- AI potential assessment — when you want to clarify first where AI makes economic sense.
- Implementation guidance — when you want to build yourselves and need an accompanying hand.
Frequently asked questions
Why not just point ChatGPT at the data we already have?
Because most companies have the wrong data sitting cleanly. ERP, CRM, accounting are structured. But the knowledge that makes a company valuable lives elsewhere. In heads. In emails. In Word documents. In decisions that were never written down.
That data often does not exist in machine-readable form. Pointing AI at existing data produces nice demos and weak results. Data Readiness for AI builds the missing foundation first.
What is a capture application?
A lean piece of software, tailored to one role. It picks up knowledge, decisions and context as part of the working day. Voice memo. A short form. A browser plugin. A CLI for technical roles. Whatever fits the role wins.
The output is a searchable, versioned corpus owned by the company. Nothing leaves the building.
Who counts as a key role?
People whose decisions make the difference. Founders. Senior consultants. Senior engineers. Caseworkers with institutional memory. Sales veterans.
Typically 3 to 15 people per company whose knowledge currently flows out nowhere. And disappears completely when they leave.
What does "orthogonal to your environment" mean?
The capture application lives independently of your tool landscape. No ERP connection. No Microsoft 365 tenant. No cloud migration.
Its own server, on-prem or in a Swiss cloud. Open formats (Markdown, JSON, Parquet). Later pluggable into the rest of your architecture without migration. You keep control.
Why capture privately instead of using SaaS?
Because the knowledge that builds AI capability is the same knowledge that constitutes your competitive advantage. On a SaaS platform, the data ends up with the vendor. Encrypted at best. In a shared pool realistically.
Owning the data on your own hardware means: you are not the training corpus for someone else's model.
How long does a build take?
Strategy and discovery: 1 to 2 weeks. Pilot capture app for one role: 3 to 6 weeks. Roll-out to 5 to 15 roles: another 6 to 12 weeks.
First usable data state typically after 90 days. We deliver a lean v1, not a perfect system. Data capture is a practice, not a project.
What happens to the data afterwards?
It belongs to you. Fully. No clause. You can use it for RAG setups. For fine-tuning a local model. For classic analysis. Or simply as institutional memory.
We build the corpus so it docks onto any future AI platform.
What does it cost?
Strategy phase at a fixed price of CHF 5,000 to 15,000, depending on depth. Build of the capture application and roll-out at the day rate of CHF 2,200.
A typical pilot with one role and 90 days of guidance lands at CHF 30,000 to 60,000. Server costs come on top, depending on setup. More precisely in the architecture conversation.
Do you have references?
Concrete cases — with real numbers and outcomes — we share in the architecture conversation, under NDA. Inventing anonymised examples on a public page feels dishonest to us. We prefer to talk directly.
What if we later move to a cloud AI anyway?
The orthogonal architecture is built precisely for that. The corpus lives in open formats and is platform-agnostic.
Move to a hyperscaler or another on-prem solution: the data comes with you. No re-capture. No vendor lock-in.
Capture today what nobody can sell you in two years.
Book a 60-minute architecture conversation. We clarify whether the offer matches your maturity. Free, qualifying, no sales pitch.