Getting insights from your data shouldn’t feel like a full-time job.
Skip the technical grind. Upload your data, ask questions, and explore what matters. Tuklasin handles the rest.
A preview of what your workspace could look like. The product is still in development.
North Revenue Decline
What am I looking at?
Revenue down 9.6% since July while orders held flat. The gap between those two is the whole story — it is price, not demand.
Which of these should I look at first?
The July break. It is the larger of the two, and the only one with something behind it you can reverse.
Three steps, from a spreadsheet to a decision
The same path every time, so you always know where you are and what happens next.
Upload or connect data
CSV, Excel, or a SQL database. Tuklasin reads it the moment it lands — columns, types, missing values, and the problems worth knowing about before anyone acts on them. Preparation happens underneath; what comes back is something your team can read.
Ask what you want
A business question in your own words, with no method to choose. Tuklasin also proposes investigations you would not have thought to ask for, each with the reason it came up — and nothing runs until you pick it. You can reject the reasoning, not only the result.
Tuklasin analyzes and explains
Churn, demand, margin, the transaction that does not fit. The method is chosen, tuned and validated for you, then named on the record. The engines compute it with visible progress — the stage it is on, and how long is left — and the finding comes back in plain language, with its warnings and what to look at next.
Rules the product holds itself to
Analytical tooling is easy to make confident and wrong. These are the constraints that keep it honest.
Business people who need deeper analysis — not another technical job
You’re a marketer, operator, finance lead, customer success manager, or founder. You know your business. You know what questions matter. You shouldn’t have to become a statistician or data scientist just to answer them.
Tuklasin handles the complexity while you stay in control of what you want to investigate.
Find out what your data already knows
Upload your data. Let Tuklasin make sense of it. Discover what matters, choose what to investigate, and understand what it means.
How the product actually works
No. You bring the business question; which method answers it is decided for you and explained in plain language. The technical detail — the method, the validation strategy, the metrics — is available whenever you want to inspect it, but you are never required to understand it to use the product.
From analytical engines, never from the language model. The model decides what is worth investigating and explains what came back; the computing happens in dedicated statistical and machine-learning code. Every number can be traced to the run that produced it.
No. It reads your data automatically and recommends investigations automatically, but nothing executes until you select it. Recommending and running are deliberately separate steps.
Yes. Every investigation is stored as a reproducible record — the dataset and version, the question, the method, the validation strategy, the results and the warnings. Records can be compared against each other and re-run. Nothing is a black box you have to take on faith.
You see where it actually is — the current stage, the analysis being run, cross-validation progress, results so far, and an estimate of the time remaining. An indeterminate spinner is not considered acceptable.
Not yet. A workspace is for one person today — there is no sharing, no second seat, and no roles. “Teams” describes who Tuklasin is built for and whose questions it answers, not how many people can be in it at once. Working together on the same data is a direction we are interested in, not something we are promising.
That is the intended use. Every recommendation states its reasoning so you can reject it on the merits, and you stay in control of what runs, what gets revisited, and what gets ignored.