---
title: "FLIGHT Ai: Flight Operations Analytics Software"
description: "FLIGHT Ai is flight operations analytics software. It reads return documents with AI, cross-checks them against the flight, and measures fuel and punctuality."
canonical: https://www.flightatom.com/products/flight.html
last_updated: 2026-10-27
publisher: FlightAtom
---

# FLIGHT Ai · Flight operations analytics software: Check what was flown against the plan and the paperwork.

FLIGHT Ai is flight operations analytics software for the operations teams of airlines and aircraft operators: chase missing return documents, catch weight and fuel mismatches between the load sheet, NavLog, fuel slip, logbook and the recorded flight, and tune each aircraft's fuel planning bias from measured burn. It syncs flights, crew and files from ForeFlight Dispatch, and document AI reads crew uploads, including phone photos and handwritten logbook times. Cross-checks are rule-based, with each value labelled by its source document.

[Request a demo of FLIGHT Ai](https://www.flightatom.com/#demo-flight) · [Explore FLIGHT Ai](https://www.flightatom.com/products/flight.html#product-tour)

[Email me the brochure, FLIGHT Ai](https://www.flightatom.com/resources/brochure.html#product=flight&placement=hero)

Operations control teams, flight operations analysts, and fleet performance specialists · Independent product · Fits your operation as it changes

![FLIGHT Ai Compare data panel showing 1 discrepancy and the note that values are AI-extracted and must be verified against the original documents, above a table of LoadSheet, EPERF and NavLog figures in which the zero fuel weight row is flagged for a 380 kg difference between the final load sheet (27,594 kg) and the NavLog (27,214 kg), while takeoff weight and block, takeoff and trip fuel match and landing weight and centre of gravity differ only within tolerance, with synthetic demonstration data](https://www.flightatom.com/products/screens/current/flight-signature-20261011.webp)

*A 380 kg ZFW gap, flagged · Demonstration data*

## In brief: What is FLIGHT Ai?

FLIGHT Ai is FlightAtom's flight operations analytics software for airlines and aircraft operators that plan with ForeFlight Dispatch. It synchronizes flights, crew and attached documents, uses AI to classify and read return documents such as load sheets, performance cards, fuel slips and logbooks, and cross-checks weights and fuel between those documents and the recorded flight. It measures actual fuel burn from the signed NavLog, recommends a fuel planning bias for each aircraft, analyses schedule performance, and uses trained models to predict fuel burn and flag unusual flights with the reasons behind each flag.

- **Category:** Flight operations analytics software
- **Built for:** Operations control teams, flight operations analysts, and fleet performance specialists
- **Applied AI:** Document AI reads return documents; trained models predict fuel burn, rank delay risk and flag unusual flights. Cross-checks are rule-based.
- **Availability:** Available · independent product
- **Fits your operation:** As your operation grows or changes, we adapt FLIGHT Ai to fit, and you approve each change before it goes live.

## How it works · FLIGHT Ai: AI reads and predicts. Rules check. People decide.

Document AI reads the return documents and its readings feed the rule-based checks directly; trained models predict from the operation's own flight history. People act on what is flagged.

Read by document AI

- **Sort each upload** · Phone photos, scans and PDFs are sorted into the required types, such as load sheet, NOTOC, fuel slip and logbook.
- **Read the document fields** · Reads weights, centre of gravity, V-speeds, fuel figures and handwritten block times, and is told to omit unclear values.

Predicted by trained models

- **Next week's fuel burn** · Predicts burn for the coming week's flights with an error band, scored against the flight plan's own error on recent held-out flights.
- **Late-departure risk ranking** · Upcoming flights are ranked by risk of departing over 15 minutes late, beside the model's backtest AUC and base late rate.
- **Unusual flights, with reasons** · Recent flights are scored on fuel rate, taxi times, delay and load, and each flag shows what stood out against typical values.
- **Retrained about daily** · Models retrain about once a day from the latest history, and a status view shows each version's backtest metrics.

Checked by rules

- **Fuel from the signed NavLog** · Fuel and OUT, OFF, ON and IN times are parsed from each NavLog, with units reconciled and impossible readings discarded.
- **Compare data across documents** · Weights, centre of gravity, fuel, uplift and passengers are compared across documents and the recorded flight, flagging differences beyond tolerance.
- **Required return documents** · Each flight returning to base is checked against the required return-document set, with gaps shown by flight and by pilot.
- **Fuel bias per aircraft** · Each aircraft with ten or more measured flights gets a raise, lower or keep recommendation, using a 1% band against plan.

Decided by people

- **Tag unmatched documents** · A person previews each document the AI could not match and tags it; the latest hand-tagged photos guide later sorting.
- **Remind the crew** · One click emails the captain and first officer a list of the missing return documents, and the reminder is recorded.
- **Weigh what is flagged** · Bias recommendations, flagged differences and risk rankings are shown for review; authorized staff decide what changes in planning.

*Each band names what does the work: document AI, a trained model, a deterministic rule or a person.*

## Inside FLIGHT Ai: See the work, from start to finish.

Explore the current product interface with demonstration data. Open any screenshot for a closer look.

### Return documents still missing

Return flights to the KJFK base that are missing required documents, with each required type marked present or missing, the uploaded photos and their document type, the captain and first officer, and a Send email reminder. Synthetic demonstration data.

![FLIGHT Ai Return Docs view filtered to flights missing return documents for base KJFK, showing 270 return flights and 16 with gaps, above two flights: DEM612 from KDCA missing its security search and DEM522 from KBWI missing its logbook and fuel slip, each with green and amber required-document chips, uploaded photo thumbnails of fictional forms tagged General, Fuel Slip, NavLog, NOTOC, EPERF Takeoff or EPERF Landing, a Missing status, pilot names, a reminder-sent note and Send email buttons, with synthetic demonstration data](https://www.flightatom.com/products/screens/current/flight-1-20261011.webp)

*Return documents still missing · Demonstration data*

### Paperwork checked against the flight

The Compare data view lines up weights, centre of gravity, fuel and passengers from the final load sheet, performance data, NavLog, fuel slip, logbook and the recorded flight, and flags a 380 kg zero fuel weight difference. Synthetic demonstration data.

![FLIGHT Ai Return Docs row for flight DEM522 from KBWI to KJFK with all nine required documents complete, expanded into the Compare data panel showing 1 discrepancy, 8/8 documents read and a note to verify the AI-extracted values against the original documents, above a table comparing ZFW, TOW, LDW, CG at takeoff, block, takeoff and trip fuel, fuel uplift, arrival fuel and passengers across LoadSheet, EPERF, NavLog, FuelSlip, Logbook and Actual columns, with block time listed for reference and only the ZFW row flagged for a 380 kg difference, with synthetic demonstration data](https://www.flightatom.com/products/screens/current/flight-2-20261011.webp)

*Paperwork checked against the flight · Demonstration data*

### Uploads waiting for a tag

Uploads that the document AI left as General wait in a review queue, each with a preview and a one-click tag for every required document type, and a person's tag overrides the AI. Synthetic demonstration data.

![FLIGHT Ai Document Review page stating that six documents need a category, listing six uploaded photos of fictional forms marked DEMO, some dark, blurred or sideways, each with its flight number, route, date and file name and buttons to tag it as NOTOC, Load Sheet, Logbook, Security Search, Fuel Slip, LIR, EPERF Takeoff, EPERF Landing, NavLog or General, with synthetic demonstration data](https://www.flightatom.com/products/screens/current/flight-3-20261011.webp)

*Uploads waiting for a tag · Demonstration data*

### Planned and actual fuel

Planned and measured burn on the same 180 flights over 30 days, the flights that reached the gate below alternate plus reserve fuel with their crews, and daily fuel per nautical mile and fleet bias trends. Synthetic demonstration data.

![FLIGHT Ai Fuel Intelligence page for the last 30 days showing planned fuel burn of 504.8 t and actual burn of 508.4 t on the same 180 flights, a +3.55 t (+0.7%) variance and planned CO2 emissions, a Fuel Reserve Alerts table of three flights that landed below alternate plus reserve fuel with route, date, captain, first officer, fuel in, required fuel and negative margin, and Fuel Efficiency Trend (fuel per nautical mile) and Daily Fleet Bias line charts, with synthetic demonstration data](https://www.flightatom.com/products/screens/current/flight-4-20261011.webp)

*Planned and actual fuel · Demonstration data*

### Fuel bias for each aircraft

Each aircraft gets a rule-based recommendation to plan more fuel, less fuel or keep planning as it is, using a 1% band against its measured burn, and the same check over the past year broken down by cruise speed profile. Synthetic demonstration data.

![FLIGHT Ai Planning Bias Recommendations table for four aircraft over 30 days, with flights, average planned and actual burn, difference, standard deviation and bias, recommending 2.7% and 2% more fuel for two aircraft, planning accurate for one and 1.9% less fuel for another, above a Fuel Bias by Speed Profile table over 365 days showing the first two of three cruise profiles, LRC with all four aircraft and M.75 with three of its four, with synthetic demonstration data](https://www.flightatom.com/products/screens/current/flight-5-20261011.webp)

*Fuel bias for each aircraft · Demonstration data*

### Trained models and fuel predictions

Status of the fuel-burn, delay-risk and anomaly models trained on the flight history: the fuel model's backtest error, with a badge showing it beats the flight plan's own error on the same flights, and the delay model's AUC and base rate. Below, the predicted burn for upcoming flights sits beside the flight-plan figure. Synthetic demonstration data.

![FLIGHT Ai ML Analytics page with three model cards, a fuel burn model with MAE plus or minus 70 kg (2.5%) and a beats OFP plan badge, a delay risk model with AUC 0.65 and an 11.0% base rate, and an anomaly model trained on 2190 flights, above a Fuel Burn Predictions table for the next 7 days listing 13 upcoming flights from 11 to 13 October with route, aircraft, departure, predicted kilograms, plus or minus MAE, OFP kilograms and the difference from plan, with synthetic demonstration data](https://www.flightatom.com/products/screens/current/flight-6-20261011.webp)

*Trained models and fuel predictions · Demonstration data*

### Late-departure risk and unusual flights

The delay model ranks the next three days' flights by their probability of departing more than 15 minutes late, beside plain per-station taxi-time percentiles, and below them the anomaly model lists recent flights with the values that stood out against typical ones. Synthetic demonstration data.

![FLIGHT Ai ML Analytics page with a Departure Delay Risk table for the next 3 days (AUC 0.65, base rate 11.0%) ranking 13 upcoming flights by late probability, from DEM121 at 56% marked high down to 1% marked normal, beside a Taxi Time Allowances table of taxi-in and taxi-out counts, medians and 75th and 90th percentiles in minutes for seven airports, above an Anomalies (multivariate) panel of 180 flights scored over 30 days listing two flights with severity scores and reason chips such as departure_delay_minutes 40.0 vs typical 1.0 (z +6.6), with synthetic demonstration data](https://www.flightatom.com/products/screens/current/flight-7-20261011.webp)

*Late-departure risk and unusual flights · Demonstration data*

[Request a demo of FLIGHT Ai](https://www.flightatom.com/#demo-flight) · [Email me the brochure, FLIGHT Ai](https://www.flightatom.com/resources/brochure.html#product=flight&placement=product_tour)

## Capabilities: From the return-document packet to the fleet fuel report.

FLIGHT Ai keeps itself up to date. On a short, configurable cycle it syncs flights, crew and files from ForeFlight Dispatch, skips flights that have not changed, parses new NavLogs, and sorts and reads new documents, so the checks and analytics are ready when someone opens them.

### See which return documents are missing

Each flight returning to base is checked against the required return-document set: load sheet, NOTOC, loading instructions, takeoff and landing performance, NavLog, logbook, security search and fuel slip. One click emails the captain and first officer a list of what is missing and records the reminder. A monthly trend shows whether compliance is improving or worsening, and a pilot view shows who has gaps.

### Catch weight, balance and fuel mismatches

A takeoff weight on the performance card that disagrees with the load sheet, or a fuel slip uplift that disagrees with the logbook, is flagged on the flight it belongs to. Compare data lines up zero fuel, takeoff and landing weight, centre of gravity, block, takeoff and trip fuel, uplift, arrival fuel and passengers across the final load sheet edition, performance cards, NavLog, fuel slip, logbook and the recorded flight. Each figure has its own tolerance, such as 0.3% MAC for centre of gravity, and every value is labelled with the document it came from.

### Read crew paperwork without retyping

Document AI sorts each upload, including angled phone photos, scans and PDFs, into its document type for the missing-document check. It reads the weights, centre of gravity and fuel figures that Compare data lines up, along with V-speeds, flex temperature, hold loads and handwritten logbook block times. A failed model request leaves the document queued for retry rather than mislabelled, and every captured document is kept with preview and download.

### Tune each aircraft's fuel bias from measured burn

Actual burn comes from the fuel and OUT, OFF, ON and IN times on each flight's signed NavLog and is compared with planned trip fuel from the same flight plan, by aircraft, route and cruise speed profile. Each aircraft with at least ten measured flights gets a recommendation: raise its bias when burn runs more than 1% above plan, lower it when burn runs more than 1% below, otherwise keep it. Taxi-out fuel is reported by station and aircraft type.

### Find the flights that reached the gate low on fuel

A low-fuel arrivals list shows past flights whose fuel at the gate, read from the signed NavLog, was below planned alternate plus reserve fuel, with flights below reserve fuel alone marked critical and listed first. Each row shows the route, date, captain, first officer, fuel in, required fuel and margin, and when there are none the page states how many flights were checked.

### See where the schedule loses time

Delay propagation shows how departure delays carry into arrivals, alongside time made up between departure and arrival, turn times by station and day-of-week patterns, all measured only on flights with complete OUT, OFF, ON and IN times. Block time is measured against the signed flight plan, and departures and arrivals against schedule at 15 minutes. Connected to the operator's crew and operations system, FLIGHT Ai uses that system's scheduled block times as the on-time baseline.

### Crew, passengers and reports

Crew pages show each pilot's position, flights, hours and destinations, compare monthly flying with the average for the same position, and include a printable monthly duty and rest report from imported duty records. Passenger analytics report load factor on flights with known loads by route, aircraft, day and hour. A fleet fuel analysis PDF with a page for each aircraft, and CSV exports, carry the figures into reports.

### Follow the fleet live

A live map shows the fleet's positions, flown tracks, ETAs and the crew on board. Upcoming flights carry METAR and TAF for the destination and alternate, with the TAF period at the ETA highlighted. Network weather adds SIGMET, G-AIRMET and PIREP layers from NOAA's Aviation Weather Center, and an ACARS inbox lets users acknowledge messages and reply.

### Query it from your AI assistant

An MCP endpoint lets an authorized AI assistant run read-only queries on the flight database, preview documents in the review queue and tag them. Each query is a single read-only statement with a time limit and a row cap, and every tag the assistant applies requires a written reason.

## Applied AI: AI that reads the paperwork and shows its working.

FLIGHT Ai uses AI where rules cannot decide: sorting and reading varied paperwork, and predicting from flight history. NavLog parsing, cross-checks, fuel bias and schedule analytics are deterministic calculations you can trace to the flights behind them.

### Reads real paperwork, guided by your corrections

Document AI handles phone photos, scans and PDFs and is instructed to leave out any value it cannot read clearly. Documents it cannot match to a required type go to a review queue, where a person previews and tags each one and the time of the tag is recorded. The photos most recently tagged by hand become reference examples for later classifications.

### Predictions that show their own accuracy

A model trained on the operation's own history predicts fuel burn for the coming week's flights, with an error band and the difference from the plan, and its error on the newest flights it did not train on is compared with the flight plan's own error on those flights. A second model ranks upcoming flights by their risk of departing more than 15 minutes late, beside its backtest ranking score (AUC) and the base late rate. The models retrain automatically about once a day, and a model status view shows each one's current version, its backtest metrics and whether the fuel model beats the plan.

### Anomaly flags that show their reasons

An anomaly model scores recent flights on fuel burn per nautical mile and per hour, taxi-out and taxi-in time, departure delay and passenger load. Each flagged flight lists the features that stood out beside their typical values. Where plain statistics answer the question, FLIGHT Ai uses them: taxi allowances are the median, 75th and 90th percentile of observed taxi times at each station, and route profiles place each route pair above or below the median across routes for fuel per nautical mile and for load factor.

### Operational boundaries

FLIGHT Ai takes flights, crew and documents from ForeFlight Dispatch, and captures and checks return documents for flights arriving at the operator's base airport. Live tracking, ACARS replies, and crew duty records with scheduled times each come from their own connected service, and each deployment is configured for the operator's fleet, base and document forms. Document AI runs on an external model provider, and its classifications and readings are not individually checked by a person before use. Prediction models start once the synced history holds at least 300 flown flights, and fuel predictions for upcoming flights use route averages for winds and loads not yet known. Outputs support authorized review and do not replace an approved flight plan, dispatch release, performance calculation, flight-time limitation check or crew decision. CO2 figures are planned estimates.

## Evaluate FLIGHT Ai: Make the next step measurable.

Agree a representative sample and a baseline from your current process. These are proposed evaluation measures, not published performance results.

### Bring a representative example

Representative return documents and a held-out period of flight records.

### Inspect the complete workflow

Check extracted fields against reviewed values and forecasts against a simple baseline.

### Compare with your baseline

Field accuracy; missing-data coverage; forecast error; reviewer correction time.

[How to structure an evaluation](https://www.flightatom.com/resources/evaluation-guide.html) · [Compare product responsibilities](https://www.flightatom.com/platform.html#compare) · [Deployment and data handling](https://www.flightatom.com/trust.html#deployment)

## Related reading: Go deeper on the workflow.

- [Airline operations software](https://www.flightatom.com/solutions/airline-operations-control-software.html)
- [What is an operations control centre?](https://www.flightatom.com/resources/airline-operations-control-centre.html)

## Questions & answers: Before you book a demo.

### Does FLIGHT Ai work with ForeFlight Dispatch?

Yes. FLIGHT Ai synchronizes flights, aircraft, crew and attached files from ForeFlight Dispatch on a short, configurable cycle, then parses NavLogs and sorts and reads new return documents automatically, so its checks and analytics stay current. It keeps its own database, dashboard and accounts, and it does not require AURORA Ai or any other FlightAtom product.

### How does FLIGHT Ai check return documents?

Each flight returning to the operator's base is checked against the required return-document set. Document AI sorts each upload, including phone photos, scans and PDFs, by type and reads its fields. A Compare data view then lines up weights, centre of gravity, fuel, uplift and passengers across the documents and the recorded flight and flags differences beyond tolerance. A user can email the captain and first officer a list of anything missing.

### Where does FLIGHT Ai get actual fuel burn?

From the fuel readings and OUT, OFF, ON and IN times recorded on each flight's signed NavLog. Kilograms and pounds are reconciled and physically impossible readings are discarded. Planned trip fuel comes from the same signed flight plan, so plan and actual are compared on the same flights.

### Which parts of FLIGHT Ai use AI?

Document AI classifies return documents and reads their fields. Trained machine-learning models predict fuel burn, rank upcoming flights by late-departure risk and flag unusual flights with the features that stood out. NavLog parsing, document cross-checks, fuel bias recommendations, low-fuel arrivals, schedule analytics, route profiles and taxi allowances are deterministic calculations.

### How accurate are FLIGHT Ai's predictions?

The fuel and late-departure models are each tested on recent flights they did not train on. The fuel model's error is compared with the flight plan's own error on those same flights, and the late-departure model shows its backtest ranking score (AUC) and the base late rate. Models retrain automatically about once a day, and a model status view shows each model's current version and backtest metrics. Accuracy is measured on each operator's own flights rather than quoted as a general figure.

### Can an AI assistant query FLIGHT Ai data?

Yes. FLIGHT Ai has an MCP (Model Context Protocol) endpoint. An authorized AI assistant can run read-only queries on the flight database and help work the document review queue. Each query is a single read-only statement with a time limit and a row cap, and every tag the assistant applies requires a written reason.

### Does FLIGHT Ai generate an approved dispatch release?

No. FLIGHT Ai provides operational records, analysis, document review, and decision support. Authorized personnel retain responsibility for approved planning systems, release decisions, and required calculations.

### Is FLIGHT Ai a flight search or booking tool?

No. FLIGHT Ai is operations software for airlines and aircraft operators. It analyses flight records, fuel, schedule performance, and return documents for operations teams. It does not search for or sell passenger flights.

### What happens to FLIGHT Ai when our operation grows or changes?

As your fleet, routes, rules or teams change, we adapt FLIGHT Ai to match, and you approve each change before it goes live.

## See FLIGHT Ai in action: Start with your workflow.

A focused walkthrough, with the people and decisions that matter to your team.

[Request a demo](https://www.flightatom.com/#demo-flight)

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Source: https://www.flightatom.com/products/flight.html
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