The numbers behind the line.
Every figure and the arithmetic. Change the inputs to fit your own model layer.
Estimate a workload.
Compare your current AutoML and batch workflow with Fidence-AI, at the same volume. Every field is yours to change.
Framing, checks, delivery, repeat scoring, failures.
Naming the outcome, reading the verdict, confirming the write-back.
US median data scientist with benefits. Use your own.
Enter yours to include it.
| A month | AutoML plus batch | Fidence-AI |
|---|---|---|
| Team time | $14,800 | $1,480 |
| Platform and compute | Enter yours above | Quote required |
Team time saved, a month
$13,300
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 forecast, tested on weeks it never saw
Sample: 3% error for Fidence-AI, against 9% for a simple baseline.
- What happened
- Fidence-AI forecast
- Simple baseline
View as a table
| Week | What happened | Fidence-AI forecast | Simple baseline |
|---|---|---|---|
| Week 1 | 1,180 | 1,215 | 1,320 |
| Week 2 | 1,240 | 1,200 | 1,320 |
| Week 3 | 1,310 | 1,275 | 1,320 |
| Week 4 | 1,205 | 1,260 | 1,320 |
| Week 5 | 1,420 | 1,370 | 1,320 |
| Week 6 | 1,510 | 1,455 | 1,320 |
| Week 7 | 1,385 | 1,430 | 1,320 |
| Week 8 | 1,290 | 1,335 | 1,320 |
| Week 9 | 1,460 | 1,405 | 1,320 |
| Week 10 | 1,605 | 1,540 | 1,320 |
| Week 11 | 1,540 | 1,590 | 1,320 |
| Week 12 | 1,475 | 1,440 | 1,320 |
And when the answer is no
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.
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
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
What Fidence-AI checks: 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.
What you see:- 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
What Fidence-AI checks: The finished model is tested on records it never saw.
What you see:- 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
What Fidence-AI checks: 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.
What you see:- Validated
- Flagged
Each with its reasons. A flagged result stays visible instead of being hidden.
What it sounds like when the answer is no
These are the product's own messages, word for word.
“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.
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.
Each saving is a worked example, on starting numbers you can change. Open a row for the pain, the fix and the working.
Customer success
Who is about to leave?
Churn
The pain88% to 84%gross revenue retention, latest benchmarkBenchmarkit and Aleph, 2026
$144,000of revenue kept, a year
Customer success
Who is about to leave?
Churn
The 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.”
The cancellation is the first warning anyone gets.
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.
- Adds to every row
- 87%, high risk
- A probability and a band per customer, tested on records held out of training.
The worked example
The same number of calls, more of the customers who are leaving
84% retention means up to 16%.
A starting figure. Use your own.
A starting figure. Use your own.
$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.
Contacted in no particular order
Contacted in the model's order
View as a table
| How the list is worked | Leavers reached a year |
|---|---|
| Contacted in no particular order | 800 reached |
| Contacted in the model's order | 2,000 reached |
The top-tenth share and the save rate are starting figures. Replace them with what your own hold-out test shows.
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.
Sales
Which leads will convert?
Lead conversion
The pain43.6%quota attainment, with lead flow at a recent lowRepVue, 2026
$420,000more deal value in front of your reps, a year
Sales
Which leads will convert?
Lead conversion
The 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.
The reps start at the top of a list nobody has sorted.
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.
- Adds to every row
- 91%
- A probability per lead, tested on held-out records.
The worked example
The same number of calls, more of the buyers
Your own number.
A starting figure. Use your own.
$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.
Worked in no particular order
Worked in the model's order
View as a table
| How the list is worked | Buyers reached a month |
|---|---|
| Worked in no particular order | 6 buyers reached |
| Worked in the model's order | 13 buyers reached |
The top-fifth share is a starting figure. Replace it with what your own hold-out test shows.
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.
Risk
Which transactions need a second look?
Fraud
The pain$5.13total cost of each $1 of fraudLexisNexis Risk Solutions, 2026
$256,500of fraud cost avoided, a year
Risk
Which transactions need a second look?
Fraud
The 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.
Every flagged order waits for someone to clear it by hand.
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.
- Adds to every row
- 96%, flagged
- A probability and a risk band, tested on held-out records.
The worked example
Less fraud through, and what each dollar of it really costs
$5.13 in the survey above. Use your own.
A starting figure. Use your own.
$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.
Total cost of fraud today
With the model stopping that share
View as a table
| Fraud | Total cost a year |
|---|---|
| Total cost of fraud today | $2,565,000 |
| With the model stopping that share | $2,308,500 |
The multiple comes from published research. The 10% is a starting figure: replace it with what your own hold-out test shows.
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.
Marketing and finance
What is each customer worth?
Lifetime value
The pain16 monthsto earn back the cost of winning a customerAleph and Benchmarkit, 2026
160 hoursof analyst time returned, a year
Marketing and finance
What is each customer worth?
Lifetime value
The 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.
Every new customer costs the same to win, whatever they turn out to be worth.
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.
- Adds to every row
- $18,400
- An expected value per customer, tested on held-out records and checked again after it is saved.
The worked example
A value for every customer, kept current
Your own estimate.
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.
By hand
Fidence-AI
View as a table
| How the values are refreshed | Time a year |
|---|---|
| By hand | 160 hours |
| Fidence-AI | 40 minutes |
The days are your own estimate.
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.
Marketing
What will each customer buy next?
Next purchase
The pain56%of shoppers often get irrelevant messagesAttentive, 2026
$54,000more revenue from the same campaigns, a year
Marketing
What will each customer buy next?
Next purchase
The 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.
Everyone gets the same offer, because nobody has time to work out who wants what.
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.
- Adds to every row
- Analytics add-on
- The most likely next product per customer, tested on how often its pick matched what happened.
The worked example
The same campaign, pointed at the right product
Your own number.
A starting figure. Use your own.
$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.
One offer for everyone
The model's pick for each
View as a table
| What each customer is offered | Orders a year |
|---|---|
| One offer for everyone | 30,000 orders |
| The model's pick for each | 30,900 orders |
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.
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.
Operations
How much of each product will we need?
Demand
The pain44%of operators stock out at least once a monthinFlow, 2026
$300,000in cash released once, then $30,000 a year in holding cost
Operations
How much of each product will we need?
Demand
The 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.
The forecast is last year plus a percentage, in a spreadsheet one person understands.
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.
- Adds to every row
- 1,240 units
- Units per product and period, backtested on recent months.
The worked example
Less forecast error, less stock held just in case
A starting figure. Use your own.
Your own figure.
$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.
Safety stock today
With the smaller error
View as a table
| Forecast | Safety stock held |
|---|---|
| Safety stock today | $2 million |
| With the smaller error | $1.7 million |
The 15% is a starting figure. Replace it with what your own backtest shows.
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.
Finance
What will each account spend next?
Spend
The pain18%of organisations can forecast within two working daysFP&A Trends, 2026
1,000 hoursof analyst time returned, a year
Finance
What will each account spend next?
Spend
The 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.
The quarter's number is built account by account, by hand, and is stale the day it ships.
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.
- Adds to every row
- $42,300 next month
- A forecast per account, month by month. The simple baseline where the model is no better.
The worked example
A forecast for every account, every round
Your own estimate.
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.
By hand
Fidence-AI
View as a table
| How the forecast is built | Time a year |
|---|---|
| By hand | 1,000 hours |
| Fidence-AI | 2 hours |
The minutes are your own estimate.
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.
A request that is not on this list?
If a column records what happened, Fidence-AI can train on it: a yes or no, a category or a number. It works out which from the column you pick, and it tells you when the data does not carry the signal.
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.
Step 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.
What would you like to predict?
- Customer Churn
- Lead Scoring
- Demand Forecast
- Fraud Detection
- Customer LTV
- Upsell & Cross-sell
- Spend Forecast
- Any custom outcome
Training data
Customer CRMConnected
Ready for ChurnStep 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.
Ready after a few automatic changes
Fidence can train on a prepared copy with these changes; your original data stays as it is.
- Rows with no outcome set aside
- “Yes”, “yes” and “Y” treated as one answer
One column is almost identical to the outcome
The model will score near-perfectly by copying that column, and predict nothing useful.
Use prepared copy
Step 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.
Training run
Your model
- Explore
- Plan
- Build
- 4Validate
- 5Deliver
Step 4 of 5Checking the model before it ships
Step 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.
Model trained and predictions readyFinished in minutes
Result at a glance
1,248 customers at high risk
- High risk
- 1,248
- Medium risk
- 2,906
- Low risk
- 8,326
Write backExport
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.
- Filter, sort and export every prediction, or ask a follow-up question about the results.
- Score fresh data with the exact saved model.
Needs attention
1,248customers at high risk
142 fewer than the last run
- Low
- 8,326
- Medium
- 2,906
- High
- 1,248
Agents
- List the models, start a training run and follow its progress.
- Pull scores for the rows that matter and write them back, with a confirmation step.
- Get the same answer a person sees, every time, because scoring uses no language model.
- agent
list_models - factory3 models: churn, fraud, spend forecast
- agent
start_training use_case: churn, training_dataset: customer_crm, target: churned - factoryModel saved. Graded Decision-grade.
- agent
get_scores top_n: 50 - factory50 rows, highest risk first
- agent
write_back confirm: true - factory50 records updated
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.
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.
- Time to a tested model
By hand, one model at a timeWeeks
AutoML plus batchWeeks of work around each run
A language-model call per answerThere is no model to test
Fidence-AIMinutes
- What running it costs
By hand, one model at a timeSalaries for every loop
AutoML plus batchPlatform, compute and team time
A language-model call per answerTokens on every call
Fidence-AINo language model in the scoring path
- Same input, same answer
By hand, one model at a timeYes
AutoML plus batchYes
A language-model call per answerNot guaranteed
Fidence-AIYes, every time
- Learns from your own history
By hand, one model at a timeYes
AutoML plus batchYes
A language-model call per answerOnly what fits in a prompt
Fidence-AIYes, all of it
- How you check an answer
By hand, one model at a timeHowever the team built it
AutoML plus batchMetrics your team reads and explains
A language-model call per answerA reason it writes afterwards
Fidence-AIA grade from records it never saw
- Where the result lives
By hand, one model at a timeWith you
AutoML plus batchIn your platform
A language-model call per answerOn the provider's platform
Fidence-AIIn 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.
Futurum Group, 2026
71%
of senior executives say switching their primary AI vendor or model would be difficult.
IBM Institute for Business Value, 2026
Put your own numbers in.
The same arithmetic as above, with your list of models and your data. Every field can be changed, and the working is written under each result.
The bill for building by hand
Every model the business is waiting for, times the weeks one takes, times what a week costs.
Use your team's own figure.
US median wage plus benefits. Use your own figure.
Salary tied up, at the weeks you enter
$236,000
10 models × 8 weeks × $2,950 = $236,000, and 80 working weeks of a data scientist. Fidence-AI builds the same models in about 1.7 hours of run time.
At your weeks
Fidence-AI
View as a table
| How the models are built | Time |
|---|---|
| At your weeks | 3,200 hours |
| Fidence-AI | 1.7 hours |
The fee for asking a language model
Every row you score, times the tokens a row takes, times the price of the model you would ask.
Language-model fees in a year
$4,200
12 million rows a year × (150 tokens in + 5 out) at list price = $4,200 in fees. The saved model scores the same rows in 50 seconds of one processor core: under 1 cent of computing.
A language-model call per row
The saved model
View as a table
| How the rows are scored | Cost in a year |
|---|---|
| A language-model call per row | $4,200 |
| The saved model | under 1 cent |
Both results are what the old way costs you.
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
From your data and a named outcome to a model that has been tested on held-out records and has scored its first rows.
Before: Your team's weeksper 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).
With Fidence-AI: MinutesFidence-AI: from connected data to a tested model with its first rows scored.
$24,000of a data scientist's salary and benefits so far
Day 40 of 40
Fidence-AI finished on day one, in minutes.
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.
| Median wage of a data scientist, a week (US BLS, 2026) | $2,312 |
|---|---|
| With benefits, a week (US BLS, 2026) | $2,950 |
| Weeks a model takes, as a starting figure | 8 to 14 |
| Salary and benefits, a model | $24,000 to $41,000 |
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.
Before: $17.50 to $1,750in language-model fees to score one million rows once, at list prices, with the shortest prompt that can work.
With Fidence-AI: 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.
Most capable language model$10 in, $50 out, per million tokens
Mid-tier language model$2 in, $10 out, per million tokens
Small language model$0.10 in, $0.50 out, per million tokens
The saved modelComputing only. No language model is called
View as a table
| How the rows are scored | Cost for one million rows |
|---|---|
| Most capable language model$10 in, $50 out, per million tokens | $1,750 |
| Mid-tier language model$2 in, $10 out, per million tokens | $350 |
| Small language model$0.10 in, $0.50 out, per million tokens | $17.50 |
| The saved modelComputing only. No language model is called | under 1 cent |
Where the fee starts to matter
A year of language-model fees, by how many rows you score. Drag along the chart or use the slider. Tap a model to follow it.
- under $1The saved model, computing only
- The model you follow
- The other two
Fees a year, on a scale where each step is ten times the last
View as a table
| Rows a month | Small | Mid-tier | Most capable | Saved model |
|---|---|---|---|---|
| 10,000 | $2 | $42 | $210 | under $1 |
| 100,000 | $21 | $420 | $2,100 | under $1 |
| 1 million | $210 | $4,200 | $21,000 | under $1 |
| 10 million | $2,100 | $42,000 | $210,000 | under $1 |
| 100 million | $21,000 | $420,000 | $2.1 million | under $1 |
| 1 billion | $210,000 | $4.2 million | $21 million | under $1 |
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.
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”.
Before: $4,088 to $20,440a month in list compute for 100 models with one always-on endpoint each, on the smallest setting across five platforms.
With Fidence-AI: Nothing idleA saved model waits until you call it. A batch job starts when you ask and stops when it ends.
What keeping your models served costs, a month
Always-on endpoints, highestOne per model, smallest setting on five platforms
Always-on endpoints, lowestOne per model, smallest setting on five platforms
The saved modelComputing only: a million rows a night, per model
View as a table
| How the models are served | List compute, a month |
|---|---|
| Always-on endpoints, highestOne per model, smallest setting on five platforms | $20,440 |
| Always-on endpoints, lowestOne per model, smallest setting on five platforms | $4,088 |
| The saved modelComputing only: a million rows a night, per model | $0.18 |
Questions a data leader asks first
Short answers. For the long ones, the product shows its work.
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.
Which team is next in your queue?
See a custom model trained on your own data.
25 minutes. Bring one request from your queue, and we will show how Fidence-AI would handle it.