Know before the results are in
Calculates outcomes that haven't happened yet, like demand, defects, quality, and failures, from today's data.
ECHO Analyze
ECHO Analyze is Wavegram's machine learning platform. Build and validate AI models on production, equipment, quality, and sales data to predict demand, defects, and failures in advance and find optimal conditions for your goals. MES, dashboards, and the Orchestrator call the models directly.
- Quality prediction accuracy at live deployments
- R² 0.92
- Together on one platform
- Prediction + optimization
- The full model development process in one place
- Data → deployment
- Call it from any business system
- One API line
Key benefits
Get answers that fit your goals
Enter the outcome you want and the adjustable range, and it returns the closest candidate conditions and expected results.
Use your models anywhere
Validated models deploy via API, so MES, dashboards, and the Orchestrator share the same model.
Models you can keep trusting
Even in operation, it compares predictions with actuals, alerts you when performance drops, and swaps in a retrained model.
Who uses it
Start even without dedicated analysts
Data owners
Define prediction targets and training data, and compare evaluation results by model to choose which to apply.
Production · quality managers
Examine how production conditions relate to inspection values, using predictions as input for process checks and quality decisions.
Equipment managers
Compares equipment values' usual patterns with change periods, using the analysis to set inspection targets and maintenance order.
Functions
From data prep to deployment, all in one place
01
Prepare your data for training
Merges scattered equipment · production · quality data by time and key, and shows missing and wrong values first as a quality score.
02
Compare multiple models automatically
Train multiple machine learning models on the same data, compare them against the same acceptance criteria (R² · error, etc.), and pick the best fit.
03
Build prediction models right away
Build the right model for the problem on one screen: numeric prediction, pass · fail classification, anomaly detection, and time-series forecasting.
04
Calculate optimal conditions
Enter a target outcome, and the model runs in reverse to calculate candidate conditions and the expected result for each. Try changes in the simulator first.
05
Deploy as an API
Deploy a validated model with one click. Business systems and AI agents call predictions with a single API line.
06
Keep managing after deployment
Keeps model versions, training data, and evaluation results as history, and monitors performance in operation to flag when to retrain.
Screens
See training and evaluation on screen
Screen 1 · record count, train · validation split, trend by sensor, data quality profile
Training data at a glance
A real deployment screen. Graphs and quality scores first show how many training records there are and whether any values are missing or wrong.
- Data composition
- Sensor trends
- Quality score
Screen 2 · current model, accuracy metrics, model list, actual · predicted trend
Model list and evaluation
A real deployment screen. Trained models are placed side by side, and the best fit is chosen by metrics and actual vs. predicted trends.
- Model comparison
- Evaluation metrics
- Choose the model to apply
Screen 3 · current process values, measured quality, AI-predicted quality, recommendation panel
AI quality prediction and feed recommendations
A real deployment screen. It predicts quality in real time during operation and calculates recommended raw-material feed and flow when quality drifts from target. Autopilot mode applies recommendations immediately; Manual mode applies them after the owner confirms.
- Real-time quality prediction
- Feed recommendation
- Autopilot · Manual mode
Use cases
Problems Analyze solves
Quality prediction
Predicts quality from in-process values without waiting for final inspection, alerting you before it goes out of spec.
Process condition optimization
Recommends the next batch's feed and operating conditions to hit target quality, even as raw material varies.
Demand · sales forecasting
Forecasts demand by item from past sales and order data to set production plans and inventory levels.
Equipment failure prediction
Learns normal operating patterns, scores unusual behavior, and catches failure signs early.
Process simulator
Replays past production data to calculate results under new conditions, reducing tests that once required real batches.
Materials · research data analysis
Predicts material properties and lifespan from test data and recommends material combinations for the operating conditions.
Case studies
Predicting and tuning on the floor today
Food manufacturing · beverages
AI retunes the recipe for every batch
- Projects
- Even with the same recipe, raw material varied from batch to batch, so quality metrics in the final tank fluctuated. Feed corrections relied on the intuition of skilled operators.
- Method
- Raw-material feed counter, set and actual feed rate, water flow, heater temperature, and downstream load signals were aligned at 1-minute intervals and joined with work order, tank, and item data. Instead of one model per line, models are trained separately per line, tank, and item, and multiple AI models are compared on the same criteria; only passing models are used. Enter a target quality and it calculates 3 candidate feed and operating conditions.
- What changed
- The next batch's feed correction appears as a recommendation on the operator's screen. It starts as advice and moves to automatic application within validated ranges.
Food manufacturing · beverages
Run it in the simulator before you change the recipe
- Projects
- Testing new feed conditions meant using real batches, costing raw material and production time, and a bad result meant scrapping the batch.
- Method
- Past production data is replayed at 1-minute intervals, setting predicted quality side by side with actual quality to confirm reproducibility first. Control variables are then changed in the validated model to calculate the resulting quality, and recommendations reach the floor only after one more validation on real-time data.
- What changed
- See results before floor trials. Narrowing the conditions to test means fewer scrapped batches.
Food manufacturing · beverages
Alerts you before equipment stops
- Projects
- When key equipment stopped without warning, production plans and quality collapsed together. Maintenance began only after a failure.
- Method
- Vibration, current, and temperature sensors are connected and monitored in two layers. Upper and lower limits per sensor, set from normal-operation data, catch obvious risks instantly, while AI learns normal patterns to catch signs that drift slowly over time. Warning and danger thresholds are separated, and false positives and misses are tuned on site.
- What changed
- Shift from repair-after-failure to maintenance planned on early signs. Dashboards and alerts put maintenance staff a step ahead.
Secondary battery materials
Countless filter combinations, narrowed to 3 by AI
- Projects
- Every customer has different operating conditions, so staff had to try countless filter combinations one by one and calculate sizes and quantities by hand.
- Method
- Enter the customer's operating conditions and AI recommends 3 candidate filter combinations, automatically calculating specs and quantities for the required size. It also predicts when each combination's performance will begin to drop.
- What changed
- Work spent finding and calculating combinations fell to 1/10. Sales and engineering propose immediately on the same evidence.
Use-case scenarios
Combined with other products, prediction becomes action
Add products one at a time as needed, and expand like this.
- LINKER Receive equipment status data.
- Analyze Analyze anomaly periods.
- Orchestrator Drafts the inspection report.
- MES Gather production and inspection records.
- Analyze Compare predictions with inspection values.
- TEAMS Staff share results with the quality team.
- Catalog Register training data and model info.
- Analyze Deploy the model to use.
- Orchestrator AI receives the predictions.
Alongside existing systems
Leave data where it is
Deployment model
Configuration and cost vary with scope, user count, and installation environment. Tell us your situation and we will recommend the right approach.
Contact salesInstallation environment
Design where training and calls run to fit data export rules: on-premises, cloud, or internal network.
Existing system integration
Uses MES production records and LINKER equipment values as training and evaluation data.
Open standards
Delivers predictions to business apps and AI through a model call interface.
FAQ
FAQ
What data do I need?
All you need is the outcome you want to predict stored alongside the values that affect it. MES · ERP records, equipment values collected by LINKER, and even Excel data work as is.
Do I need a data scientist?
Yes. Data prep, model comparison, evaluation, and deployment are linked on screen, so business users just review results and choose. Wavegram builds the models with you in the early stages.
Do AI recommendations go straight into equipment?
It starts with recommendations, then applies them automatically in stages, beginning with ranges that pass the simulator and live validation.
How is it different from generative AI like ChatGPT?
Generative AI writes text and answers; Analyze predicts numbers and calculates optimal conditions from your company's data. Both work together in the ECHO Orchestrator.
How do I know the predictions are right?
Acceptance criteria (R², etc.) are set for each model in advance, and only models validated by replaying past data and live comparison are used.
Do you use open source?
No. Wavegram has specialized in AI vision and AI products since 2020. Because everything is built on our own technology, we can optimize for any customer's unique processes.