A reliable, tailored AI advisor for your business decisions.
Would you take the last AI answer without re-checking it? The explai Data Advisor:
- knows your data and your company logic
- delivers root causes, forecasts and recommendations you can trust
- doesn’t guess when data is missing, unlike ChatGPT or Claude
- shows the risk before you commit
Where our people have delivered
A late answer is the same as no decision.
Nothing changes
Spend runs on, the margin leaks.
The answer comes too late
When the ticket comes back, the window has shut. The damage stays.
Decide within days
An answer in minutes, then four days of your review. You act while the window is open.
Warehouse at 85%: the window is still open
Warehouse utilization is at 85%. From here, delays and damage grow with every week you wait.
The explai Data Advisor shows, live:
- which carriers to pull volume from, in which regions
- that 85% is the limit
- which suppliers already miss their lead times, while you can still reroute
Operational Intelligence
A data scientist for everyone, not a chatbot that talks to your tables
ChatGPT and other chatbots know neither your data nor your definitions, and sound sure even when they guess. explai instead:
- finds exactly the data the decision needs
- explains the causes
- runs what-if scenarios
- stays on it after the decision
A data scientist for everyone
Why it moved, what happens if you act, what to do next: on demand, not after six weeks in the backlog.
20% lifts units, but the baseline already moved. The competitor launch is in the history. 10% stays in the tighter band; 20% is the wide amber path.
Ask what a competitor launch or a flash sale does to your forecast, and take cited scenarios straight into the meeting. The bands show how far the result can swing.
It finds why the number is wrong before you act
Channel A “wins” overall but loses in every segment. That is root cause, not a faster dashboard.
Looking at the aggregate is misleading. Channel B converts higher on both desktop and mobile.
Learn why1→Make the call, then measure and improve
The decision is implemented and checked as data and rules change, not a pilot that quietly drifts.
Every team, their own rules
Sales, Ops, and Finance each define “customer” differently: all correct. explai keeps every team’s “truth”.
Reporting tells you what happened. Important decisions need more.
A number, not a decision
Shows last month. Does not tell you what to do with the budget still on the table.
A fluent answer you can’t check
Doesn’t know your data or your definitions. When facts are missing it guesses, and sounds just as sure.
The decision already shipped
The analysis is sound. The meeting already ran without it.
Support so you can decide
Why it moved, what happens if you act, what to do next: cited, in time to make the call.
Decisions we help business teams make faster
Budget, mix, and ROI
Allocate spend with clearer lift, run scenario simulations, and see which campaigns actually protect margin, not just volume.
Explore →Pricing, demand, and promo
Price and promote with foresight: demand outlook, competitive gaps, and inventory trade-offs in one conversation.
Explore →Risk, variance, and forecast
Keep operational bottlenecks, supplier risks, and P&L drivers under control, with metrics that enable confident action the same day.
Explore →Answers to what people actually ask
How is this different from a generic AI chatbot or dashboard?
It’s not a chatbot answering from a public model’s generic training data. explai’s Analytical Autonomy runs transparently on your own company-private data and your definitions of revenue, margin, and customer, so Sales and Finance stop arguing past each other.
Why not just use ChatGPT or Claude?
General chatbots are strong with language, but they don’t know your data, your definitions or your domain. When facts are missing they fill the gap with a plausible guess (hallucination), and you can’t see where a number came from. explai answers from your systems, cites the row and definition behind each number, is tested on your own cases before go-live, and tells you when the data doesn’t support an answer.
Why not hire our own data scientist?
You can, and explai works alongside in-house data science. But a data scientist in Germany earns €57,200 a year on average before employer costs (Stepstone, 2026), takes months to hire, and still needs data engineering. explai gives every decision-maker data-science-grade analysis per seat, live in about 30 days.
How do I know an AI answer isn’t confidently wrong?
That’s what explai’s Trusted Challenger is built for: see the thinking, own the answer. It surfaces explainable results, including contrarian evidence, with a human in the loop. A confidently wrong answer (from mislinked data, the wrong metric, or a misleading overall view) gets caught before you act on it.
Do we get to keep the AI advisor after the engagement, or does the insight leave with the consultants?
You keep it. Results are exported, and after licensing you can keep running the AI advisor yourself, in a private cloud or your own data center, instead of the insight dying in a consultant’s deck or a chat thread.
Is it explai or explain?
explai, no “n” at the end. The name comes from explainable AI. Search engines sometimes correct “explai” to “explain”; if that happens, go to explai.com directly.
Bring your highest-value business question. Leave with a cited answer.
We connect your data and company knowledge so the explai Data Advisor answers in your terms, not a generic model.