# Fidence-AI: Data connected. Model delivered.

Predictive models for data teams.

You run data and models that serve other teams at scale. Every team brings you their data and a deadline. Fidence-AI trains and validates on your history, then delivers scores or forecasts to your people and agents.

Updated 2026-11-09.

## How to read these figures

- Figures for the old way come from published research and list prices, with arithmetic shown.
- Worked examples are arithmetic on starting numbers a reader can change. They are not customer results.

## The four promises, with their figures

- **Fast. From data to a tested model:** Weeks per request against Minutes. Against the weeks a request takes your team today.
- **Cheap. Scoring a million rows:** A fee per row against Under 1¢. The saved model's computing, against a language-model call or an always-on endpoint.
- **Always ready. The next batch:** A pipeline to maintain against One request. Saved models stay ready and run as batch jobs when you ask, with no pipeline to maintain.
- **Yours. What you are left with:** A fee per answer against Your model. Trained only on your data and kept in storage of its own, not weights on a provider's platform.

## You serve the models. Every team is your client.

Customer success wants churn. Sales wants lead scores. Risk wants fraud. Finance wants a forecast. Each arrives with its own data and its own deadline. You do not need a project for each one. You need one way to answer all of them.

### Train

**What it costs today:** 3.3 to 4.5 months on average for data science, MLOps and low-code platforms to go live (G2, 2026), and each request still takes your team's weeks.

**What Fidence-AI does:** Connects to your data, profiles it and trains the model. The same process for a yes or no, a category, a number or a forecast.

**The result:** Minutes. From connected data to a tested model.

### Evaluate

**What it costs today:** Your board wants proof of return, and most teams lack a mature way to measure it.

**What Fidence-AI does:** Tests every model on records held out of training, grades it Decision-grade, Directional or Not usable, in words the team that asked can read.

**The result:** Graded. Decision-grade, Directional or Not usable.

### Score

**What it costs today:** A scoring pipeline to build and keep alive for every model your teams use.

**What Fidence-AI does:** Applies the saved model exactly as saved, with no language model in the path. It checks itself against rows it has scored before, and writes nothing if they do not match.

**The result:** Under 1¢. Of computing per million rows.

### Serve

**What it costs today:** Endpoints to keep running or batch jobs to schedule, for every team that asks.

**What Fidence-AI does:** Keeps saved models ready and runs them as batch jobs when you ask. Scored rows land wherever you define, for your people and agents to read.

**The result:** One request. For the next batch, with no pipeline to maintain.

## You already have AutoML. You still have the queue.

Your platform trains, tunes and runs batches. A person still does the work between those steps, for every request. That is the work Fidence-AI takes.

| The work | Today, on your team | With Fidence-AI |
| --- | --- | --- |
| Frame the request | Find the data, the keys and the right target | You name the outcome. It profiles your data and sets up the rest |
| Check the data | Often found out after a training run | A verdict before anything trains, with what to fix |
| Build and review | Adapt a notebook, then have someone review it | Done for you, in minutes |
| Trust the result | Hunt for leaks, then explain the metrics to the team | Leaks flagged, and a grade the team can read |
| Deliver | Build the path back to the team's system | Predictions written back once you confirm |
| Score the next batch | A pipeline to keep alive | The saved model, one request |
| Handle a failure | Someone gets paged | A tested pipeline takes over, and it tells you which one built the model |

## Weeks become minutes. Here is the arithmetic.

Three savings across the layer you run: building, scoring and keeping models served. The old figures come from published research and list prices, with the arithmetic shown.

### Time to a tested model

Your team's weeks per request. Set your own number below. And before the first request, data science, MLOps and low-code platforms take 3.3 to 4.5 months on average just to go live (G2, 2026).

Minutes. Fidence-AI: from connected data to a tested model with its first rows scored.

One square is a working day, one block a week, filling as you scroll. Fidence-AI's whole run is the thin line at the start of day one.

What those weeks cost in salary: $24,000 to $41,000 in a data scientist's salary and benefits, if a model takes 8 to 14 weeks. An estimate: one person, full time, at the US median with benefits. Most models also need a data engineer and someone to deploy them, and bonuses and overhead come on top.

### The fee on every answer

Asking a language model to judge each row means paying for tokens on every row, every time you score. In a 2026 Futurum Group survey, 46.9% of enterprises said their AI spend, of all kinds, was over budget.

$17.50 to $1,750 in language-model fees to score one million rows once, at list prices, with the shortest prompt that can work.

Under 1¢ of computing for the saved model, and no language model is called. A small model scores a million rows in seconds on a single processor core.

| How the rows are scored | Cost for one million rows |
| --- | --- |
| Most capable language model | $1,750 |
| Mid-tier language model | $350 |
| Small language model | $17.50 |
| The saved model | under 1 cent |

One call per row, 150 tokens in and 5 out, at list price. Larger models also bill their own reasoning as output, which raises these fees.

A 2026 preprint found that, on 18 tables, a trained classical model beat a small language model in 86% of cases with the data already on hand.

## Every team at your door has a number to hit.

You do not need to care about any one use case. The team that asked does. So here are seven requests that reach a data team first, with the pain as an outside source measured it, what Fidence-AI does about it, and a worked example to re-run with that team's numbers.

A worked example. Every model is tested on records it never saw, so you see how yours does on your own data before you act on it.

### Who is about to leave? (Churn, asked by Customer success)

**Pain:** 88% to 84% is how far gross revenue retention fell, in a benchmark of 342 software companies. Its authors call it “a market-level structural dynamic, not company-level execution.” Source: Benchmarkit and Aleph, 2026.

**What Fidence-AI does:** Trains a classifier on your own history of who left, and gives every current customer a probability and a high, medium or low band. The week starts with the list already sorted by risk.

**Worked example, The same number of calls, more of the customers who are leaving:** $144,000 of revenue kept, a year. 8,000 of 50,000 customers leave each year. Contacting 5,000 in no particular order reaches 800 of them. Contacting the 5,000 the model ranks highest reaches 2,000. That is 1,200 more. If 10% of them stay because of it, that is 120 customers kept, worth $144,000 a year.

**Starting numbers:** Customers: 50000; Who leave each year: 16 %; Customers your team can contact in a year: 10 %; Leavers in the model's top tenth: 25 %; Contacted leavers who stay because of it: 10 %; Revenue per customer, a year: 1200 $.

**Assumptions:** The top-tenth share and the save rate are starting figures. Replace them with what your own hold-out test shows.

### Which leads will convert? (Lead conversion, asked by Sales)

**Pain:** 43.6% was quota attainment across 252 software companies in a recent quarter, while sellers’ rating of inbound lead flow fell to its lowest recent reading. Source: RepVue, 2026.

**What Fidence-AI does:** Trains on the leads you already won and lost, and scores every open lead with its probability of converting. Reps work the pipeline from the top.

**Worked example, The same number of calls, more of the buyers:** $420,000 more deal value in front of your reps, a year. 25 of 1,000 leads a month would buy. Working 250 in no particular order puts reps in front of 6 of them. Working the 250 the model ranks highest puts them in front of 13. That is 7 more buyers a month, or $420,000 a year in deals. It assumes a lead converts only if someone works it.

**Starting numbers:** New leads a month: 1000; Who would buy if worked: 2.5 %; Leads your reps can work: 25 %; Buyers in the model's top fifth: 50 %; Value of one deal: 5000 $.

**Assumptions:** The top-fifth share is a starting figure. Replace it with what your own hold-out test shows.

### Which transactions need a second look? (Fraud, asked by Risk)

**Pain:** $5.13 is roughly the total cost of each $1 of direct fraud loss at US retail and ecommerce companies, in a survey of 513 risk and fraud leaders across the US and Canada. Source: LexisNexis Risk Solutions, 2026.

**What Fidence-AI does:** Trains on the cases you have already confirmed, and returns every new transaction with a probability and a risk band, flagging the riskiest. Reviewers open those first.

**Worked example, Less fraud through, and what each dollar of it really costs:** $256,500 of fraud cost avoided, a year. $500,000 of direct losses × $5.13 is $2,565,000 a year in total cost. If the model stops 10% of that fraud, and the other costs fall in step with it, that is $256,500 a year.

**Starting numbers:** Direct fraud losses a year: 500000 $; Total cost of each $1 lost: 5.13 $; Of that fraud, what the model stops: 10 %.

**Assumptions:** The multiple comes from published research. The 10% is a starting figure: replace it with what your own hold-out test shows.

### What is each customer worth? (Lifetime value, asked by Marketing and finance)

**Pain:** 16 months is how long the median software company takes to earn back what it paid to win a customer. The bottom quarter takes 24 months or more; the top quarter, 6 or fewer. Source: Aleph and Benchmarkit, 2026.

**What Fidence-AI does:** Trains a regression model on what past customers went on to spend, and gives every customer an expected value. Budget and attention can follow the number.

**Worked example, A value for every customer, kept current:** 160 hours of analyst time returned, a year. 4 refreshes × 5 days × 8 hours = 160 hours by hand. At ten minutes a run, 4 refreshes take about 40 minutes.

**Starting numbers:** Times a year you refresh the values: 4; Analyst days per refresh, by hand: 5 days.

**Assumptions:** The days are your own estimate.

### What will each customer buy next? (Next purchase, asked by Marketing)

**Pain:** 56% of shoppers often or very often receive irrelevant messages, and 80% are more likely to ignore brands that send them, in a survey of 1,050 shoppers. Source: Attentive, 2026.

**What Fidence-AI does:** Trains a multi-class model on what customers bought after what, and names the most likely next product for each one.

**Worked example, The same campaign, pointed at the right product:** $54,000 more revenue from the same campaigns, a year. 12 campaigns to 50,000 customers bring 30,000 orders from a one-size offer. 3% more from offers matched to each customer is 900 more orders, worth $54,000.

**Starting numbers:** Customers in one campaign: 50000; Who buy from a one-size offer: 5 %; Campaigns a year: 12; More sales from a matched offer: 3 %; Value of one order: 60 $.

**Assumptions:** The 3% is a starting figure, well below what published tests report: at Allegro, a new recommendation model lifted attributed sales by 21.25% in the web cart and 15.73% in the app.

### How much of each product will we need? (Demand, asked by Operations)

**Pain:** 44% of operators stock out at least once a month, and 74.3% run inventory on spreadsheets as their sole or primary tool, in a survey of 400 US operators. Source: inFlow, 2026.

**What Fidence-AI does:** Trains a forecaster on your sales history, product by product, and backtests it on recent months it was not shown. A quality gate checks the backtest and is built to refuse a forecast that fails it.

**Worked example, Less forecast error, less stock held just in case:** $300,000 in cash released once, then $30,000 a year in holding cost. By the textbook rule, safety stock is sized in proportion to forecast error. A 15% smaller error means 15% less safety stock: $300,000 released once, and $30,000 a year no longer spent holding it, at 10% a year.

**Starting numbers:** Safety stock you hold: 2000000 $; Cut in forecast error: 15 %; Cost of holding stock, a year: 10 %.

**Assumptions:** The 15% is a starting figure. Replace it with what your own backtest shows.

### What will each account spend next? (Spend, asked by Finance)

**Pain:** 18% of organisations can produce a forecast within two working days, and 44% can plan with high confidence for no more than three months. Source: FP&A Trends, 2026.

**What Fidence-AI does:** Builds a forecast for every account from its own history and backtests it on the most recent months. Where it cannot beat a simple baseline for an account, the baseline is used.

**Worked example, A forecast for every account, every round:** 1,000 hours of analyst time returned, a year. 500 accounts × 10 minutes × 12 rounds = 1,000 hours by hand. At ten minutes a run, 12 rounds take about 2 hours.

**Starting numbers:** Accounts to forecast: 500; Minutes per account, by hand: 10 min; Forecast rounds a year: 12.

**Assumptions:** The minutes are your own estimate.

## No model ships without a grade.

Not just a score. The evidence behind it, with nothing extra to configure.

- **How far to trust it.** A grade from records the model never saw. Churn scores and forecasts also carry a measured confidence: how often the model was right at that level.
- **Why this score.** Ask Fidence-AI in chat about any prediction, and it walks through that record for you.
- **What drives the model.** The inputs that matter most to the model, ranked, so a domain expert can challenge it.
- **Where it came from.** Which model, trained on what data and when. It flags suspicious scores and says when a retrain is due.

Your people and agents read from the same results, so everyone is looking at the same evidence.

## Estimate a workload.

Compare your current AutoML and batch workflow with Fidence-AI, at the same volume. Every field is yours to change.

Fidence-AI's fee: Quote required.

## One run, start to finish.

Sample data, in the shape Fidence-AI reports it, including what happens when the answer is no.

- **The request.** Customer success asks who will cancel in the next 90 days.
- **The data.** A customer CRM, connected. The outcome column records who cancelled.
- **Readiness.** Ready after a few automatic changes. One column was flagged for giving the answer away, and left out.
- **Held-out result.** AUC 0.84 on 2,000 records held out of training, against 0.50 for guessing. Graded Decision-grade.
- **Effort.** Minutes of run time. Your team named the outcome, read the verdict and confirmed the write-back.
- **Delivered.** A score, a band and a confidence on every customer in the app, and the score and band on the CRM record once you confirm.
- **A score that looks too good.** A near-perfect result is held at Directional, because it usually means a column leaks the answer.
- **A forecast that cannot beat the baseline.** For that account the simple baseline is used, and you are told which one you got.

## Rent every answer, or own the model.

Until now a prediction came with one of two bills: a long build for each model, or a language-model call for each answer. Fidence-AI builds the first kind of asset in minutes.

| | By hand, one model at a time | AutoML plus batch | A language-model call per answer | Fidence-AI |
| --- | --- | --- | --- |
| Time to a tested model | Weeks | Weeks of work around each run | There is no model to test | Minutes |
| What running it costs | Salaries for every loop | Platform, compute and team time | Tokens on every call | No language model in the scoring path |
| Same input, same answer | Yes | Yes | Not guaranteed | Yes, every time |
| Learns from your own history | Yes | Yes | Only what fits in a prompt | Yes, all of it |
| How you check an answer | However the team built it | Metrics your team reads and explains | A reason it writes afterwards | A grade from records it never saw |
| Where the result lives | With you | In your platform | On the provider's platform | In your own Fidence-AI account, in storage of its own |

## What CIOs and data leaders have recently said

- **46.9%** of enterprises report AI spend over budget. 5.6% came in under it. Source: Futurum Group, 2026.
- **71%** of senior executives say switching their primary AI vendor or model would be difficult. Source: IBM Institute for Business Value, 2026.

### The endpoint left running

An online endpoint bills for every hour, used or not. Letting it sleep is not always the answer: one platform's documentation says scale to zero “is not recommended for production endpoints”.

$4,088 to $20,440 a month in list compute for 100 models with one always-on endpoint each, on the smallest setting across five platforms.

Nothing idle. A saved model waits until you call it. A batch job starts when you ask and stops when it ends.

At list prices. Compute only: storage, data charges, redundancy and staff come on top.


## The whole lifecycle, in minutes.

Profile, prepare, train, validate, score, and do it again when the data moves. The loop your team knows by heart, run the same way for every request.

1. **Take the request, connect your data.** Your team: Name the outcome the team asked for and connect the data that records it. Start from one of eight outcomes or define your own. Fidence-AI: Profiles your data: its shape, keys, dates and candidate outcome columns. It sets identifiers aside and shows which outcomes your data can support.
2. **Get a verdict before anything trains.** Your team: Read the verdict and make the decisions only you can make, such as which value counts as the outcome having happened. Fidence-AI: Runs the exact configuration end to end on a sample. It warns when a column gives the answer away, makes safe repairs on a copy and leaves the original untouched.
3. **Build and validate.** Your team: Nothing. Watch five steps go by, or leave and come back. Most runs finish in minutes. Fidence-AI: Chooses the approach from the profile and trains the model. The model is then tested on records it was never shown, and graded.
4. **Score, deliver, repeat.** Your team: Send predictions wherever you define, and score again when new data arrives. Fidence-AI: Saves the model and applies it exactly as saved: no retraining and no language model in the path. It tracks the age of every model and says when a retrain is due.
## It would rather say no than guess.

A confident wrong number is worse than no number. So Fidence-AI checks its own work at three points and tells you plainly when the answer is no.

- **Before it trains.** Runs your configuration end to end on a sample. Looks for a usable outcome column, enough past examples, and columns that give the answer away. Verdicts: Ready to train, Ready after a few automatic changes, One choice needed, Not ready. If something is missing, it says what to fix.
- **While it builds.** The finished model is tested on records it never saw. Verdicts: Decision-grade, Directional, Not usable. A grade based on how the model did on records it never saw, readable by the people who will act on it.
- **After it saves.** Forecasts are backtested on the most recent months and have to beat a simple baseline, account by account, or the baseline is used. Saved forecasts and lifetime values are checked again. Verdicts: Validated, Flagged. Each with its reasons. A flagged result stays visible instead of being hidden.

What it sounds like when the answer is no:

- "Not ready for Churn. The automatic flow would not finish on this data as it is." Before training, when the data cannot support the outcome. It comes with what to fix.
- "Score is near-perfect, which usually means a feature leaks the answer. Held at directional until the leakage check clears it." On any model that scores 0.99 or higher. A perfect score is treated as a warning, not a win.

## Served to your people and agents.

Whichever team asked, every model answers to both. People work in the app. Agents call the same models through a published connector and get the same scores.

- People: See what needs attention first: how many rows sit in the top band, and how that moved since the last run.
- People: Filter, sort and export every prediction, or ask a follow-up question about the results.
- People: Score fresh data with the exact saved model.
- Agents: List the models, start a training run and follow its progress.
- Agents: Pull scores for the rows that matter and write them back, with a confirmation step.
- Agents: Get the same answer a person sees, every time, because scoring uses no language model.
- Predictions land wherever you define: Back in the system the data came from, in a file, or pulled by your own pipeline or agent. Fidence-AI reads files and the systems your data already lives in, and nothing is written back until you say so.
- Personal columns are masked: Columns that look like emails, phone numbers, names or addresses are masked before a summary of your data is used to plan the model.
- Your account, your storage: Each account's files, models and results are kept in storage of their own. Your models are trained on your data only.

## Security

- **Encrypted.** Your data is encrypted in transit and at rest.
- **Kept apart.** Each account has storage of its own. Your models are trained only on your data, and nothing is pooled across customers.
- **Signed in to one account.** Every request is tied to a signed-in account and reaches only that account's data.
- **Personal data masked.** Columns that look like names, emails, phone numbers or addresses are masked before a summary of your data is used to plan the model.
- **You stay in control.** Predictions are written back to your systems only after you confirm. You can disconnect a source or delete a dataset at any time, and on request we delete your account and its data.
- **Your systems.** A warehouse, a CRM or a file. You choose what to connect.
- **Your account's storage.** A copy of the data you choose, encrypted, in storage of its own.
- **Training.** The model trains on your data in an isolated session, planned from a summary with personal columns masked.
- **Scoring.** The saved model scores your rows. No language model is in this step.
- **Back to you.** Scored rows in the app, in an export or through the connector, and in your systems after you confirm.
- Snowflake: read Tables you choose; write back Yes, after you confirm.
- Databricks: read Tables you choose; write back Yes, after you confirm.
- HubSpot: read Contacts; write back Yes, after you confirm.
- Salesforce: read Contacts; write back Yes, after you confirm.
- monday.com: read Boards; write back Yes, after you confirm.
- Files: read Upload; write back Export every prediction.

For a security questionnaire or a data processing agreement, email us or book a call. support@fidence-ai.com

This page describes how Fidence-AI protects customer data today. It is a summary, not a contract: your agreement with Fidence-AI governs. We update these practices as the product changes.


## Questions a data leader asks first

### What does my data team still do?

The work that needs judgement: deciding which requests matter, owning the definitions and the data, and judging the results. Fidence-AI takes the loop: profiling, preparation, training, validation, scoring and the next retrain. Your team stays the one the business comes to, with a shorter list.

### How are predictions served?

Scoring runs as a batch job: you name a saved model and the data to score, and the job writes the scored rows. People, pipelines and agents then read those rows, or you write them back to another system. Nothing has to stay running between jobs, which is where the saving comes from.

### How does it scale?

Every request runs the same way, so the next model is one more run, not one more project. Small models like these score a million rows in seconds on a single processor core.

### Who owns the model?

You do. Each model is trained only on your data and saved in storage that belongs to your account. It is a small conventional model, such as gradient-boosted trees, not weights inside a language-model provider's platform. You use each model through the app and the connector.

### Does scoring depend on a language model?

No. Once a model is trained, scoring uses no language model at all. The saved model is applied exactly as saved, so the same input gets the same answer.

### Does it keep up with new methods?

Yes. Ask it to improve a model and it finds what works best today, tries it against the model you have, and keeps a new version only when it proves better.

### How is this different from AutoML in my data platform?

AutoML fits and tunes a model once someone has framed the problem and prepared the data. Your team still does the work around it. Fidence-AI does that too: it checks the data before training, builds the model, tests on held-out records, grades the result, delivers the predictions and scores the next batch. The evidence comes with the result, with nothing extra to configure. Keep your platform. Fidence-AI reads from it and clears the queue in front of it.

### How is this different from foundation models for tables?

A foundation model scores the table your team has already built. Fidence-AI does the work around it: it reads your data, sets up the prediction, tests the result on records it never saw, grades it, and delivers the scores back to the team that asked.

### How far can I trust one prediction?

Every model is graded on records held out of training. Churn scores and forecasts also carry a confidence measured on those records: how often the model was right at that level, not the score restated. You also see the inputs that matter most to the model, its grade, which data trained it and when, and any flag raised along the way. Ask Fidence-AI in chat why a record scored as it did, and it walks you through it.

### What earns Decision-grade?

The grade comes from the task's own measure on records held out of training: AUC for yes-or-no models, R² for numbers. Forecasts are backtested on the most recent months against a simple baseline, account by account. A score that looks too good is not rewarded. A near-perfect result is held at Directional, because it usually means a column leaks the answer.

### Which systems does it connect to?

Data warehouses and lakehouses, CRMs, work-management tools and files. It reads from them, and writes predictions back to the system they came from once you confirm.

### What data do I need?

Past records where the outcome is already known. For a forecast, a dated history takes the place of the outcome. Fidence-AI reads one row per record, transaction logs, time series and month-by-month panels.

### How do I know a model is good?

Every model is tested on records that were held out of training, and you see how it did. It is then graded Decision-grade, Directional or Not usable. Forecasts are backtested on the most recent months before they are saved. A quality gate checks each backtest and is built to refuse a forecast that fails it.

### What happens to my data?

Your files, models and results are kept in storage that belongs to your account, encrypted in transit and at rest, and your models are trained only on your data. Columns that look like personal data, such as emails, phone numbers, names and addresses, are masked before a summary of your data is used to plan the model. Nothing is written back to another system until you say so.

### Where do the savings numbers come from?

The figures for the old way come from published research and list prices. The worked examples are arithmetic on numbers you can change. Your own result comes from your own data: every model is tested on records it never saw before you act on it.

### What does it cost?

Pricing follows how much you use. Book a call and we will size it on your own requests.