Stop wasting time searching for data
AI fetches in one go the data you used to gather across multiple systems.
ECHO AI
ECHO AI is an AI platform that connects ChatGPT · Claude · Gemini in one place. AI finishes repetitive work, and AI trained on your company's data checks automated equipment operation and equipment health in real time, every second. People simply review and approve.
- AI applications ready to apply today
- 10+
- AI built on our own technology
- Since 2020
- Live equipment health checks
- Every second
- Deployments in automotive · shipbuilding · semiconductors and more
- 110+
Key benefits
Decide by prediction, not by gut
Tells you the next outcome in advance from accumulated production · equipment data.
Get analyses your systems don't offer
AI gathers and checks data scattered across systems, and even runs analyses your current systems don't offer.
Verify the evidence behind every result
Each step records what data it read and what it executed.
Product lineup
Start with the AI you need
ECHO AI
Orchestrator
With one request, AI moves across your internal systems and finishes the job. It runs on schedule by itself; people only approve.
ECHO AIAnalyze
Build prediction models from accumulated production · equipment data and overlay them with actual values to see how well they fit.
ECHO AICatalog
Register your company's data, AI models, and connected functions in one place, and decide who can use them.
ECHO AIAI VISION
The camera sorts product types and inspects for foreign matter · damage and printed text. New product types are handled by retraining.
ECHO AIPredict
Learns equipment vibration · current · temperature patterns to flag failure signs early.
Functions
Delegate, predict, control
01
Just tell it
Ask in everyday language, and AI understands the task and remembers the conversation so far.
02
Even multi-step work, all the way
AI handles lookups, calculations, and drafting in order. If it hits a wall, it tries another path.
03
Connect straight to the systems you use
Connects MES · ERP · email · internal DBs for AI use without changing them. Only permitted functions run.
04
Predict what's next with data
Feed in accumulated production · equipment data, and it cleans missing and outlier values, finds the factors that most affect the outcome, and refines the prediction.
05
See how AI did the work
Every step records what was queried and which functions were used, and results are scored for whether they came out right.
06
Decide who sees what
Separate the data each person can see and mask sensitive values. Critical actions require approval, and everything is logged.
Screens
See the screens where AI works
Screen 1 · scheduled time, 3 recurring tasks, results to review, approve
On its own, at set times
At set daily · weekly times, AI prepares production reports, inventory checks, and delivery reply drafts. People just review the results and approve.
- Scheduled runs
- View collected results
- A person approves
Screen 2 · model list, evaluation metrics, actual vs. predicted trend
Prediction model evaluation
Overlay how well a model built on accumulated data hits actual values. Only well-fitting models go to the floor.
- Model comparison
- Overlay actual · predicted
- Applied after validation
Screen 3 · current process values, measured vs. predicted quality, feed recommendation
AI equipment control
AI previews the next batch's quality and recommends raw-material feed and operating conditions, applying them automatically within validated ranges first.
- Quality prediction
- Feed recommendation
- Autopilot · Manual mode
Screen 4 · health score by unit, normal · watch · danger by hour
AI predictive maintenance
Shows how far each unit has drifted from normal, by hour, in color. Inspect the units whose color deepens first.
- Health score
- Hourly heatmap
- Inspection priority
Use cases
Delegate work like this first
Production report
The production manager requests a report based on the previous day's work orders and output. AI compiles the variance from plan into a draft report.
Delivery reply
The sales team prepares answers based on consultation notes and shipping schedules. AI gathers ERP · CRM data and drafts the reply.
Equipment inspection
The equipment team examines periods where temperature and vibration changed, setting Analyze results beside maintenance records to choose what to inspect.
AI asset operations
The IT team registers the data and connectors used for work in the Catalog, decides who may use them, and connects them to AI tasks.
Case studies
AI is already at work on the factory floor
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
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.
Global consumer goods · overseas plant
Catches overseas process anomalies to the same standard as headquarters
- Projects
- At overseas plants far from headquarters, response to anomalies varied with local staff experience.
- Method
- SCADA and LINKER collect capsule process equipment data in real time, detecting out-of-spec patterns and sending alarms.
- What changed
- Detects process anomalies to the same standard as domestic plants. Local staff and headquarters see the same screen.
Large appliance manufacturing
One model for cosmetic inspection judgments
- Projects
- Visual inspection on the assembly line varied by inspector, and tracing which process a defect came from was difficult.
- Method
- Good and defect images were collected and labeled, scarce defect samples were filled with synthetic defect images, and a detection model was trained. Results are kept as history and linked to process records.
- What changed
- One standard for judgment. Find the line and time a defect occurred right away.
Abrasives manufacturing
A camera measures the temperature of flames you can't touch
- Projects
- Flame surfaces in grinding couldn't be measured by contact sensors, so temperature control was guesswork.
- Method
- AI finds and tracks the flame region in process video to estimate and record surface temperature.
- What changed
- Temperature is recorded as numbers, so process conditions and quality can be compared together.
Use-case scenarios
The more products you combine, the more you can delegate
Add products one at a time as needed, and expand like this.
- MES Query MES output.
- Orchestrator Draft the production report.
- TEAMS Staff share it in TEAMS.
- LINKER Collect equipment data.
- Analyze The model analyzes anomaly periods.
- Orchestrator Organize inspection items.
- Catalog Register data and functions for training.
- Analyze Build and validate prediction models.
- Orchestrator AI calls models and puts them to work.
Alongside existing systems
Keep your existing systems and just add AI
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
Installs wherever your data lives: on-premises servers, the cloud, or air-gapped networks.
Existing system integration
Connects ERP · MES · CRM lookup and processing functions to AI tasks.
Open standards
Connects internal functions through the standard protocol (MCP), so AI keeps working even when systems change.
Business screens
With RxControls, screens that display and handle AI data are easy to build.
FAQ
FAQ
Does data go out to external AI services?
Your choice. Connect external AI like ChatGPT · Claude · Gemini, or run only on AI installed on company PCs or on-premises servers. On-premises, your data never leaves the company, and it installs even in air-gapped networks with no internet.
Which AI models (LLMs) does it use?
Choose the right AI for the job from ChatGPT · Claude · Gemini and on-premises AI. Change models, instructions, connectors, and task-plan settings on one screen, and they take effect immediately.
Do I have to replace my current MES or ERP?
No. It connects as is through the standard connection protocol (MCP) and connectors for existing systems. Equipment data links in real time through LINKER SERVER.
How does it work technically?
It consists of 6 modules: conversation (Assistant), task execution (Orchestrate), system connection (MCP), prediction (Analyze), quality measurement (Monitor), and permissions · audit (Governance).
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.
How do I bring AI data into our business screens?
With RxControls, building business screens that display and handle AI data is easy.
How do I get started?
Validate with one core process over 4–6 weeks, build on real data over 4–5 months, then expand company-wide.