We are a development shop that refuses to start with development. First we make the process measurable, then we build the thing that has to beat it.
You own the code outright
We are a development shop that refuses to start with development.
Most implementation projects go wrong before the first line of code, because the brief describes a feature instead of a broken process. By the time the software ships, everyone has forgotten which number it was supposed to move.
So the first thing we deliver is usually not a model or an agent. It is instrumentation: making the process visible enough that you can tell whether the thing we build afterwards worked.
Not a chatbot on your website. An agent that reads the inbound queue and drafts the reply, or scans your sources and puts five candidates in front of you to approve. Human in the loop wherever the cost of being wrong is real.
If a spreadsheet is currently your integration layer, that is the work.
Per-seat pricing on a tool you use at 20 percent of its capability is one of the easiest business cases to make. You own what we build, so the cost stops.
React, React Native, Next.js, Supabase, and LowCode/AI platforms where they genuinely fit. We pick the stack to match the lifespan of the product, not our preference.
Often the highest-return item on the list, and almost never the one the client asks for first.
At Kreante, replacing a SaaS tool runs from about 10,000 USD for a focused build to 80,000 to 100,000 USD for a full product.
Beyond that range, the honest answer depends on one thing: whether the tool is expensive because of its price or because of how you use it.
The second case is more common than buyers expect. Zylo's 2026 SaaS Management Index puts unused enterprise software licences at 43 percent. Close to half of what a company pays for is capability nobody opens. Replacing a tool you never adopted does not fix that, it just moves the invoice.
| What we check | Why it changes the number |
|---|---|
| Price per seat versus seats actually used | Heavily under-used seats make replacement pay back fast |
| Whether the tool was ever properly adopted | An adoption problem is training, not a build |
| How much of the product you actually need | Replacing 20 percent of a tool is a different project |
| Integrations that have to survive the switch | Most of the cost usually sits here, not in the AI |
We look at both before quoting. If you are paying per seat for something three people touch, replacement usually pays back inside a year. If the real problem is that nobody was trained on the tool you already own, that is an AI training and enablement problem rather than a build, we will tell you so, and you will save the build entirely.
Either way, we quote fixed price against a frozen scope, not hourly against a moving one.
In what order AI gets implemented
Step 5 is the one almost everyone skips, and the reason so few pilots survive contact with a P&L.
Instrument first
Make the process measurable before automating it. No baseline, no verdict.
Deterministic before probabilistic
If a rule and a trigger solve it, use a rule and a trigger.
One process, end to end
The process with the clearest mechanism, from trigger to outcome.
Working prototype in weeks
Real output in your own data, while changing your mind is still cheap.
Measure against the baseline
The same number captured in step 1, read again after go-live. This is the step that gets skipped.
Step 5 closes back on the number captured in step 1.
Before automating a process, we make it measurable. If you cannot say what the current process costs, you cannot tell whether the new one is better.
If a rule and a trigger solve it, we use a rule and a trigger. Reaching for a model when an if statement will do is how projects get expensive and fragile.
Not everything at once. We pick the process where the mechanism is clearest and follow it from trigger to outcome.
You see something real early, in your own data, and you change your mind while changing your mind is still cheap.
This is the step almost everyone skips, and it is the reason so few pilots survive contact with a P&L.
A 133-person vehicle photography and merchandising company serving roughly 300 dealerships, producing 30,000 photos a day.
Kreante ran discovery before proposing anything and produced 194 validated findings. When we wrote the delivery roadmap, seven solutions came out of it. Two of the seven are not AI. One is a rule, one is a trigger, and we said so in writing rather than dressing them up.
The first deliverable in that roadmap is not an agent. It is instrumentation, because nothing in the delivery chain was being measured. Recommending a model first would have been easier to sell and impossible to prove.
We also declined to build an AI replacement for a platform they were frustrated with, because the constraint was the vendor relationship, not the software. That was a smaller invoice for us and the right call for them.
One more thing we flagged rather than buried: moving their quality control to 100 percent coverage would change the scores that drive staff bonuses. That is a people problem created by a technical decision, and it had to be decided before the first model, not after.
No license, no hostage situation, no per-seat fee to us. If you part ways with us, you keep everything.
265+ projects across 35 countries, delivered for startups through to Decathlon and Schneider Electric.
Fixed price against a fixed scope. When you want to add something, we price the addition rather than absorbing it and quietly slipping the date.
Maintenance in hour packages, because the first month after go-live is when the real requirements show up.
Companies that have already found the process worth fixing, either through our AI consulting or on their own. Owners and operations leaders at US services companies, 50 to 500 employees, multi-location or field-based.
Who this is not for
If nobody internally can say which number the build should move, start with consulting. Building on an unmeasured process is the most expensive way to learn this lesson.
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We find out what your current process costs before recommending anything, then hand you a roadmap ordered by certainty of mechanism.
See AI consultingWe work alongside your team, on your own processes, until the new way of working is the normal way of working. Then we leave.
See AI training and enablementA working prototype in weeks. A production build typically one to three months depending on scope, then testing, then launch.
Yes, and that is usually cheaper. We build a layer of execution on top of the platforms you run, rather than proposing to replace them. Replacement is a decision we take only when the numbers say so.
Almost nobody does. That is what the instrumentation step is for. We do not require a data warehouse before starting.
You do. Code, infrastructure, documentation.
Three months of warranty on what we build, plus a maintenance plan if you want ongoing capacity.
If you know the process you want fixed, book a working session and bring it. If you are not sure yet which process it should be, start with the 20-question assessment.
265+ projects for 110+ clients across 35 countries. Working prototype in weeks, fixed price against a frozen scope, and you own the code outright.
