Watch your own data beat a generic ChatGPT answer: it names the accounts about to leave, catches the conflict a filter misses, and ranks the revenue at stake. Built end-to-end by E...
Every day, your business records what is happening to it. A login that stopped. An invoice gone overdue. A support thread that turned tense. A one-line note that the main contact just left. The signal is already there. It just cannot speak.
A public chatbot cannot reach any of it. You cannot paste millions of live records into a chat window, and you should not want to. So the data sits in separate systems, in different shapes, and the risk surfaces weeks later, usually at renewal, when it is too late to act.
We wanted a different answer. AI that runs on your own data, follows your rules, and hands your team a ranked list of what to do today, with the revenue at stake spelled out on every line.
"The AI model everyone can buy is not the advantage. The data only you have is."
The idea behind this build
This is not one more dashboard to check. It changes how much of your business gets seen, how fast you act on it, and what that protects. The points below come from the live demo, run on Meridian PM, a synthetic B2B SaaS, so you can watch the difference yourself.
Four steps, on your terms. Your data stays where it lives, your rules drive the result, and the output lands in the tools your team already uses.
All your records across CRM, billing, product usage, support, contracts and notes are unified into one governed layer on your infrastructure. Nothing is exported or pasted anywhere.
The limits and policies that define how your business works become rules the model follows. At risk of leaving, renewal coming up, ready to upgrade, gone quiet, or your own, all in plain language.
The AI scores every account against your rules and reads the free-text notes a filter cannot. It catches the conflicts where the status says one thing and the note says another.
A prioritized to-do list, with the revenue at stake and a plain-language reason on each line, remade every morning from live data and sent into your CRM, inbox or a Slack channel.
Paste a list into a public chatbot and you get a polite, generic answer. It never sees your live systems, never knows your rules, and forgets everything by tomorrow. The difference is not the model. It is that this one is grounded in your data and governed by your logic.
A search needs tidy fields. This reads the messy reality too: the free-text notes, the comments, the things a filter cannot understand, and combines them with your structured numbers. Connecting and cleaning the sources is part of the build, not something you do first.
The risk hides in the gap between your structured fields and your free text. When a status says one thing and a note says another, the workflow surfaces the contradiction and flags it as a save-now risk, instead of letting it pass quietly until renewal.
The output is not a paragraph of advice. It is a sorted list of accounts to act on, each tagged with the rule that fired, the monthly revenue at stake, and a one-line reason. Your team starts the day knowing exactly what to do first.
Every result shows the rule it applied and the numbers behind it, so you can check it rather than just trust it. It is built to flag and reason, not to decide on its own. Where the data is thin, it says so instead of inventing an answer.
You choose the privacy level, and your data never trains a public model. Run it on a commercial model through its business API, the same model under a zero-data-retention agreement, or an open-source model hosted entirely on your own infrastructure.
The limits and policies that define how your business works become rules the model follows. Toggle the ones that matter, add your own in a sentence, and the result rebuilds. The logic stays yours, written in words your team understands, not buried in a black box.
The same engine that flags churn finds growth. Accounts near their seat limit, heavy users ready for a bigger plan, delighted customers who would happily refer. Your own data points to the revenue you would otherwise leave on the table.
Not a one-off answer someone has to remember to ask for. It runs on a schedule, every morning is typical, from live data, so the to-do list is always current. Yesterday's list is replaced by today's reality before your team logs in.
The ranked queue does not sit in another dashboard nobody opens. It lands where the work happens: as tasks in your CRM, a digest in your inbox, or a message in a Slack channel. An answer in a chat is not a workflow. A task on the right person's plate is.
A search needs tidy fields first. This does not. It reads the messy reality, the half-filled fields, the free-text notes, the comments, and combines them with what is structured. Connecting and cleaning the sources is part of the build, not a prerequisite you have to finish on your own first.
This is the moment the whole approach earns its keep. One account in the demo looks completely safe on paper. Its renewal is comfortably out, the invoice is paid, the status reads healthy. A dashboard filter would never flag it. The free-text note tells a different story, and the AI reads the note.
The demo shows both answers at once. The same accounts go to a public chatbot with the list pasted in, and to the workflow grounded in your data and rules. Toggle a rule or add your own, and only the grounded side reacts, because the chatbot cannot reach your systems or your logic.
A chatbot can only act on what fits in its context window: a few hundred rows you paste in by hand. Your business does not fit in a paste, and it changes every day. This is built the other way around. The data stays in your systems, and the model reads only the slice each decision needs.
For AI on your own data, the first question is always the same: where does my data go? The honest answer is, wherever you decide. You pick the privacy level your business needs, and in every case your data is never used to train a public model.
The intelligence is not a black box you hope behaves. Your rules are explicit, written in plain language, and you control them. Toggle one off and the result changes. Add one in a sentence and the next run respects it. The model applies your logic; it does not invent its own.
A chatbot will confidently make something up when it does not know. This will not. Every flag is tied to your actual data, shows the rule it applied and the numbers behind it, and where the data is thin, it says so plainly instead of inventing an answer.
Finding risk is not enough. The list has to be in the right order, or your team drowns in it. The workflow weighs how urgent a signal is against the revenue at stake, so the account you can least afford to lose sits at the top, not the one that emailed most recently.
The most useful question is rarely "what is true". It is "what changed". Because it runs every morning on live data, it can show you the accounts that crossed a line overnight: the renewal that just went quiet, the invoice that slipped, the usage that fell off a cliff.
Before any of this works, the data has to come together. We connect your source systems into one governed layer the model can read, the structured tables and the free text alike. Nothing is exported by hand, nothing is pasted, and access stays governed by your rules.
The demo reasons over a sample. A production build connects the full source systems, however many records you have, and the model reasons over the relevant slice for each result rather than swallowing everything at once.
Your data and your rules sit at the center. Everything flows through one governed layer, and the model is a swappable part, not the product.
Your systems connect once. Nothing is exported by hand, nothing is pasted, and access stays governed the whole way.
The model never swallows the whole database. For each decision it pulls only the relevant slice, then reasons over it with your rule and the data cited.
The live demo proves the loop on a synthetic dataset. A real build starts with the one decision you most want running automatically, then grows from there.
A synthetic B2B SaaS, 4.2M records framed across 6 systems, reasoned over by the model against your selected rules.
Toggle the rules that matter or add your own, and watch the ranked action queue rebuild with the revenue at stake on each line.
Both answers, side by side, on the same dataset, so the difference between a generic paste and a grounded workflow is impossible to miss.
Wire in your CRM, billing, product analytics, support desk and data warehouse. If a system has an API or a database, it can be connected.
Push the ranked queue into the CRM as tasks, into the inbox, or into a Slack channel, so the result becomes the work, not a separate report.
Add new decisions beyond churn and upgrade, and let your team's outcomes feed back in so the matching gets sharper over time.
Open the demo, pick the rules that matter, and watch the same data beat a generic ChatGPT answer: named accounts, a caught conflict, and a ranked queue with the revenue at stake. Then tell us the one decision you would most want running automatically on your own data.
We built this for Entexis. We can build it for you, same rigour, your domain.
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Hope that was helpful. Reach out anytime.