AI On Your Own Data · At Scale

AI that runs on
your data.

A small spreadsheet in a chatbot gives you an answer. But your business data lives across many systems, far more than you could paste into a chat box, governed by your rules, and full of customer data you are not allowed to hand to a public model. That cannot go in a chatbot.

See the live demo Build one on my data
The result

Inside Meridian PM's live data

Not a question box. This is the company's own customer data, connected and following its rules, turning into a ranked to-do list your team can act on this week.

4,231,887 records, pulled together from 6 live systems Live

In plain terms: every customer record, invoice, login, support ticket, contract, and campaign the business has, spread across the tools it uses every day. Far too much, and too private, to ever paste into a chatbot.

All the company's data, by where it lives

Runs continuously on your infrastructure · the moment a record changes, the results update · nothing ever leaves your environment.

The rules it runs by · tap to turn on or off, or add your own
See both answers side by side: ChatGPT with the list pasted in, and the same data run through your workflow. Toggle a rule or add your own. Only the right side changes, because ChatGPT doesn't know your workflow.
ChatGPT · the list pasted in, no workflow

It only sees the raw list, never your rules. Re-runs each time, but your rules can't reach it.

Your data · your workflow applied
Synthetic sample · computed across 4,231,887 records and every account note · nothing stored.

Why this can't come from ChatGPT

It is genuinely useful for a pasted file. It cannot do the job above.

Scale

Whether you have thousands of records or millions, they do not fit in a paste. A chat box holds only a few hundred.

Live data

Whatever you paste is a dead snapshot. Your data moved since you copied it.

Your rules

It does not know your churn thresholds, renewal windows, or pricing logic. This does.

Continuity

It answers once. Results have to run every morning, automatically, forever.

Privacy

Customer names, contracts, and financials legally cannot go to a public model. This stays on your infrastructure.

Integration

An answer in a chat window is not a workflow. Results have to land where the work happens.

The Impact

What AI on your
own data delivers.

Tell us your use case

A result every day

A fresh, ranked to-do list every morning, made automatically. Not a question someone has to remember to ask.

Private by design

Runs on your infrastructure with zero-retention or self-hosted models. Customer data never goes to a public model. No private data leaves your stack.

Every record reviewed

Every record and every account note checked against your rules on every run, so the quiet at-risk accounts surface too, not just the loud ones.

Hours saved every week

The manual pulling, joining, and eyeballing of data across systems your team does by hand, gone.

Inside this build

What it takes to turn your data into results.
The same building blocks power yours.

The demo runs on a fictional company, but every piece below is what a real Entexis build assembles around your data. The model is rented; this layer is what we build.

Unified at scale

Every record from CRM, billing, product usage, support, contracts, and account notes pulled into one layer the AI can read, without anyone exporting anything.

Your rules as logic

Churn thresholds, renewal windows, upsell triggers, the policies your team runs by, become logic the model applies on every record.

Private by design

Runs on your infrastructure. Zero-retention or fully self-hosted models so customer data, contracts, and financials never reach a public model.

Runs continuously

Not a one-off answer. It runs again on a schedule from live data, so the to-do list is always up to date.

Backed by real data

Every item shows the actual numbers and the rule behind it. No made-up names, no generic filler, no guessing.

Lands in your tools

Results push into your CRM, your inbox, or Slack, so they become work that gets done, not a chat window someone forgets to open.

How we build it

Three moves turn your data
into results that run every day.

The same path behind the demo above is the path Entexis runs for a client build. The model stays a utility. The work is in the layer underneath.

01

Connect your data, at scale

All your records across CRM, billing, product, and support, unified into one governed layer on your infrastructure. Nothing gets exported or pasted anywhere.

See the demo
02

Add your rules

The limits and policies that define how your business works become rules the model follows, so the results match how you actually work, not generic advice.

03

Deliver results continuously

A ranked to-do list, backed by your real data, remade every morning and sent into the tools your team already uses. On your systems, with your data staying yours.

04

Same layer, one use case at a time

Start with one decision on live data. Add the next once it is landing. The unified layer, the rules, and the delivery paths are already in place.

Frequently Asked Questions

Can't I just paste my data into ChatGPT and get this?
For a small file, yes, and it is genuinely useful. But a business runs on far more data than a paste can hold, spread across live systems, governed by your rules, and full of data you legally cannot hand to a public model. That will not fit in a paste, will not stay current, will not know your policies, and will not run again tomorrow. Turning that into a result that runs every day is a different job, and it is the one we build.
Is our private data safe? Where does it run?
It is your choice, based on the privacy level you need. We can run it on a leading commercial AI model (such as OpenAI or Anthropic) through its standard business API, which does not train on your data; on the same model under a zero-data-retention agreement when nothing may be kept at all; or on an open-source model hosted entirely on your own infrastructure, so customer names, contracts, and financials never leave your environment. In every case, your data is never used to train a public model.
How much data can it actually handle?
Far more than a chatbot context window. We connect the full source systems, however many records you have, and the model reasons over the relevant slice for each result rather than trying to swallow everything at once. The demo here uses a small synthetic sample; a production build runs the same logic across your real volume.
Does it run once, or keep working?
It keeps working. It runs again on a schedule, every morning is typical, from live data, so the to-do list is always up to date. That is the difference between a one-off answer and a result your team can rely on.
Where do the results show up?
Wherever the work happens. We send the to-do list into your CRM, your inbox, or a Slack channel, so it becomes a task your team acts on, not a chat window someone has to remember to open. An answer in a chat is not a workflow.
How long does a custom build take?
A first result running on your data, on your stack, typically goes live in about two weeks. Deeper integrations, more decisions, and monitoring layer on after that. You start with one decision, prove it, then expand.
What systems and tools can it connect to?
The ones you already use. We connect your CRM, billing, product analytics, support desk, spreadsheets, and data warehouse into one layer the model reads. If a system has an API or a database, it can be connected, so nothing has to be re-keyed or exported by hand.
Does our data need to be clean and organized first?
No, and that is part of the point. 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 data. Connecting and cleaning the sources is part of the build, not something you do first.
How accurate is it? Can we trust the results?
Every result is grounded in your actual data and 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, and your team stays in control. Where the data is thin, it says so instead of inventing an answer.
Can we change the rules ourselves?
Yes. The rules are yours to set and adjust, just like the toggles in this demo. As your business changes, the logic changes with it, without rebuilding from scratch.
Can Entexis build this on our own data?
Yes. That is exactly what we do. We connect your sources into one governed layer, encode your rules, and ground a model on it so it produces continuous, ranked results from your reality, on your infrastructure, with your data staying yours and you owning the code. Tell us the one decision you would most want running automatically and we will show you how we would build it.
AI On Your Own Data · Production-ready

Tell us your data and your rules.
We'll build the AI on it.

The demo above is one shape: a ranked to-do list from your data. We also build churn and renewal radars, who-to-contact-next lists, assistants that answer from your own documents, and other custom automations. About two weeks for a first build, running on your own systems with your data staying yours.

12+
Years
5
Continents
2,100+
Engagements

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