AI runs on data. Your data runs on Pliable.
Pliable connects to every system you run and builds a data foundation that reflects how you see your business. Then it gives you an AI analyst that answers in seconds. No data team required.
New ARR was $827K in March, down 11% from February. Almost all of the gap is in mid-market, which fell 31% while enterprise grew 8%.
| Segment | New ARR | vs. Feb |
|---|---|---|
| Enterprise | $412K | +8% |
| Mid-market | $266K | -31% |
| SMB | $149K | -4% |
Two things happened in mid-market: you paused the paid search campaign on March 4, and the two largest deals in the segment slipped to April. Both are noted in your March pipeline review.
Operators at middle-market companies run on Pliable
Your systems have the data. They don't have the answer.
This isn't a tooling problem. You can hand an AI model a login and an API key. What you can't hand it is an understanding of your business, so it fills the gap with guesses.
No door in
Half the systems you run have no usable API. Some have none at all, and the data lives on a server in your building.
Same word, different meaning
A "customer" in your CRM is not a "customer" in your billing system. Nothing on the market reconciles them for you.
no system on your stack can fill this in
Your systems don't know your business
None of them know how you define pipeline, attribution, a qualified lead, or a fully loaded cost. That logic lives in your head.
No context, no story
The number moved. Only your team knows about the pricing change in March that moved it, and that context is nowhere in your data.
AI is only as good as the foundation underneath it. Most companies don't have one, so they get confident answers that are quietly wrong.
A complete data team, from your systems to your answer.
Three things have to be true before AI can answer a real question about your business. That used to be a job for a data engineer, a data leader, and a hefty budget. Pliable does all three with an agentic data team instead, and only the third one is something you have to think about.
01 · Integrations
Modern APIs, databases, on-prem systems, spreadsheets. We build the connection and we keep it working.
02 · Data Foundation
Everything a data engineer would do, done for you, then your business described back to you in your own words.
See how it works03 · Consumption
Four ways to use it. Only this column is something you have to think about.
Learn about Pliable MCPEvery other tool is built for the day everything works.
This isn't the day everything works. Questions come up that your foundation can't answer yet. A definition turns out to be ambiguous. A source changes shape without telling anyone. We built the product for that day, not the easy one.
Ellie flags it
She hits a question the foundation can't answer, says so plainly, and files it. No guess, no invented number.
An agentic process diagnoses it
Our system works out what's actually missing, a source, a definition, or a relationship, and proposes the fix.
A human expert reviews it
One of our data experts checks the proposed change before it ships. Oversight on the AI, not the other way around.
It ships
Sometimes you make one business decision along the way, like which of two definitions of "active customer" you mean. Then the answer is there for good.
There are only three ways to get here.
Out of the box. Build it yourself. Or get it fully managed. Same starting question, three completely different costs, and three completely different odds the answer you get back is actually right.
Out of the box
This is already happening, whether anyone signed off on it or not.
Someone on your team already does this: opens a personal ChatGPT or Claude account and pastes in whatever's on hand to get a fast answer.
No modeling. No definitions. No record of what left the building, and wrong more often than right.
Build it yourself
This is what building it yourself actually takes.
Someone on your team decides to fix this properly: hire a data engineer, buy an ETL tool, and start building a governed layer from scratch.
95% is the best case, right after launch. Skip a month of upkeep and it drifts back toward 65%, the same range Anthropic reported in its own published research. Even at the high end, about 1 in 20 answers is still wrong, with no way to know which one.
Pliable
This is what happens when we do it for you.
We integrate your systems, dedupe and clean the data, and model it into a managed semantic layer, so nobody on your team has to own the project.
Deterministic. The same right answer, every time, from a layer someone else is responsible for keeping right.
Source: "How Anthropic enables self-service data analytics with Claude," claude.com/blog. The 99.9% figure is Pliable's own measurement from live customer deployments.
Most companies find out they are already on the first path. We can have you on the third one in five days.
Talk to usEveryone talks about being data-driven. These teams actually are.
We tried five BI platforms. They looked nice, but each one broke down at the hard part: cleaning and structuring the data. Putting a pretty face on disorganized data doesn't solve the problem. Pliable was the only solution that could clean, structure, and display our data within budget.
Different teams were counting on different data, and there was no holistic view of the customer. Pliable solved all of that for us without any distractions for our core team.
We've eliminated 80% of the manual data engineering work. Instead of getting bogged down in data prep, our entire team is focused on value-added work for our clients.
Pliable equipped our business leaders with self-serve analytics they actually trust. It sped up our projects by months and lowered our costs to build and maintain our systems in the process.
You are talking to your data in five days.
Not a discovery phase. Not a six-month implementation. We connect your first sources, build the foundation, and put Ellie in front of your team inside the first week.
What we need from you: A few conversations. No engineers, no internal project.
The things people ask us first.
Still have one? Bring it to a demo.
How do you connect to a system that has no API?
The same way a data engineer would, except we do it and we own it. Direct database connections, on-prem agents, scheduled file drops, screen-level extraction where there is genuinely no other route. If your business depends on it, we will find a way in, and we will keep that connection working.
What does 99.9% accuracy actually mean?
It means that of the questions Ellie answers, 99.9% return the correct result. The number that makes it possible is the other half of the design: when a question falls outside what your foundation can answer with confidence, Ellie declines and flags it rather than producing something plausible. We would rather answer fewer questions perfectly than most questions approximately.
Do I need anyone technical on my side?
No. You need someone who knows how the business works and can answer questions about how you define things. That is usually a founder, an operator, or a finance lead. There is no engineering work for your team at any point.
How is this different from connecting ChatGPT or Claude to our data?
Those models are excellent at language and reasoning. They have no idea what your data means. Pointed at raw tables they will write queries against columns they have misunderstood and return answers that look right. Pliable gives the model a foundation built from your definitions, so the reasoning happens on top of something correct.
What happens when Ellie cannot answer something important?
She tells you, and it becomes a tracked gap. Our agentic process diagnoses what is missing, proposes the fix, and a human expert on our team reviews it before it ships. Occasionally we come back to you with a business decision only you can make. Then the answer is permanent.
Where does our data live, and is it used to train models?
100% of your data stays in your own environment, in your own cloud data warehouse, under your control. It is never used to train any model, ours or anyone else's. We are happy to walk your team or your auditors through the architecture in detail.
What does it cost, and what is included?
One subscription covers the integrations, the maintenance, the data foundation, the knowledge base, and all three consumption surfaces. Integrations are not billed as services and new sources are not change orders. See the pricing page for current tiers.
Your team is already asking AI these questions.
Give them a governed way to get the answer right, instead of a fast way to get it wrong. Bring a real question to the demo and we will show you Ellie answering it against data like yours.