A disciplined approach to liquidity, not a black box
We built Kanjewiria around transparency, methodology, and fit for how German SMBs actually manage cash. Here is what sets our approach apart.
Most tools optimize for speed. We optimize for defensibility.
Many liquidity tools generate a number and ask you to trust it. We show the inputs, the assumptions, and the range of outcomes — so every recommendation can be explained to a controller, a lender, or a board.
That distinction matters more than it sounds. A forecast you can't defend in a review meeting isn't useful, regardless of how fast it was produced. Our process is built so the reasoning behind every figure is traceable back to the data that produced it.
| Consideration | Typical tool | Kanjewiria |
|---|---|---|
| Assumption visibility | Hidden | Disclosed |
| Scenario range | Single estimate | Multiple scenarios |
| Fit to SMB structure | Generic | Tailored inputs |
| Methodology documentation | Rarely provided | Provided on request |
What we hold ourselves to
Methodology over marketing
Every output is tied to a documented method. If we can't explain how a number was derived, we don't present it as a recommendation.
Built for operating reality
We design around the cash cycles, payment terms, and reporting constraints typical of small and mid-sized businesses — not enterprise treasury departments.
Scenarios, not single answers
Liquidity planning involves uncertainty. We present ranges and conditions rather than a single misleadingly precise figure.
A second look before anything reaches you
Outputs from our models pass through a review step before they're presented as recommendations. The goal isn't to slow things down — it's to catch assumptions that don't hold up under scrutiny before they reach a decision-maker.
We'd rather deliver a well-reasoned recommendation a little later than a fast one that can't withstand a follow-up question.
Four things every engagement includes
Documented assumptions
Every input used to generate a recommendation is listed, so you can check it against your own view of the business.
Readable output
Recommendations are presented in plain language with supporting figures, not dense model exports.
A defined review step
Outputs are checked against known edge cases before delivery, reducing the chance of silently wrong recommendations.
A direct line for questions
If a recommendation doesn't match your expectations, you can ask why — and get an answer tied to the actual inputs.
What we won't promise
We won't claim a guaranteed return, a fixed percentage improvement, or a specific ROI timeline. Liquidity outcomes depend on variables outside any model's control — market conditions, customer behavior, and internal decisions among them.
What we will do is be clear about assumptions, show you the range of plausible outcomes, and flag where confidence is lower. We think that's a more useful basis for decisions than a confident-sounding single number.
If a recommendation changes materially between runs, we investigate why before passing it along — rather than presenting whichever number came out first.
See how this approach applies to your business
Talk with us about where your liquidity planning currently stands and what a more defensible process could look like.