Every use case starts the build again.
Connect sources. Map and clean. Define metrics. Build services and interfaces. Repeating the data-to-app work slows the next operational idea.
Reuse a configuration-driven path to the product
AI-ASSISTED DATA PRODUCT ENGINEERING
Generate the product. Take it to the edge.
Turn enterprise data into operational applications with AI-assisted definition, full-stack generation, and a path to local edge delivery. Your team shapes the product and reviews what runs.
For data teams and the operations that depend on them.
FROM ENTERPRISE DATA TO THE EDGEAUTHOR → DELIVER
01Connect your data
PostgreSQLSQL ServerAmazon S3
02Build the foundation
BronzeRaw data
SilverRefined schema
GoldProduct-ready data
Databricks orchestration
AI-assisted. Human-reviewed.Your definition → generated product
03Operational readiness
Readiness overviewSAMPLE VIEW
Asset statusAvailable
MaintenanceIn focus
Data viewsFrom definition to application
Illustrative workflowPackage → cloud / local edge
Natural languageAI recommendationsHuman controlFrom enterprise data to the point of work.
Built to work with
your data stack.
PostgreSQLMySQLSQL ServerAmazon S3databricks
TWO GAPS. ONE CONNECTED LIFECYCLE.
Stop rebuilding the path.
Start reaching the work.
For data and platform teams supporting maintenance, readiness, and remote operations, the last mile is more than another dashboard. It’s a usable product, in the environment that needs it.
The work continues. The cloud link doesn’t.
A cloud-only experience stops where connectivity stops. Deployed maintenance, remote sites, and field teams need selected data and services close at hand.
Package for local operation, then validate the boundary
THE PLATFORM
AI accelerates the build.
Your team owns the decisions.
Connect the data foundation, product definition, and application build in one lifecycle. Review recommendations, inspect the output, and shape what comes next.
AI with your schema.
Not a blank canvas.
Turn real source structure into recommendations for KPIs, personas, and dashboards. Review the mappings. See what’s missing. Shape the product before it’s built.
silver.asset_statusSCHEMA
asset_idstringstatusstringupdated_attimestamp
AI-GROUNDED DEFINITION
Asset availabilityRecommended KPI · ready for review
Missing fields are surfaced, not assumed.
A full stack.
A real starting point.
Generate the backend, frontend, migrations, and deployment artifacts. Download the code and let your engineers take it forward.
your-data-productGENERATED
├backend Spring Boot
├frontend React + TypeScript
├migrations Database
└deploy Docker · Helm · CI
Verify. Understand. Iterate.
Configurable compile and Docker checks, with visible repair outcomes. Know what passed and what still needs attention.
CompileDockerReview results
Keep the data moving.
Respect the human edits.
Sync with idempotent upserts, protection for user-edited records, and audit counts that make changes visible.
Source updatesUser edits protected
Human review belongs in the workflow.See how it comes together
THE PRODUCT, IN CONTEXT
One product.
A shared operational picture.
Readiness, maintenance, and parts in a persona-focused experience. Connect the headline metric to the work—and the data—behind it.
AIRCRAFT LIFECYCLE INTELLIGENCEView full-size
Views built around a role.
Bring readiness, open maintenance, and parts dependencies into the same decision context.
Explore the explanation.
The illustrated AI experience explains trends and supports product-scoped Q&A. Validate answers and data boundaries in your pilot.
Make readiness visible.
Review source mappings, freshness, and definition gaps before relying on a feature or recommendation.
AUTHOR. REVIEW. DELIVER.
One lifecycle.
Five concrete steps.
Use natural language to shape the need. Review AI recommendations against your schema. Generate, verify, and package a product your team can inspect.
01 / DATA FOUNDATION
Start with the data you already have.
Connect PostgreSQL, MySQL, SQL Server, or Amazon S3. Bring your source data into a Databricks-orchestrated Bronze, Silver, and Gold workflow.
YOU LEAVE WITHA connected data foundation
source-connection•••
01
source: PostgreSQL02
pipeline: Bronze → Silver → Gold03
schema: inspected from SilverSTEP OUTPUTS
Source connections and mappings
Bronze / Silver / Gold orchestration
Actual Silver schema for the next step
BUILT AROUND THE WORK
Data products with
an operational purpose.
ILLUSTRATIVE USE CASES
Start with the decision your team needs to make. Build the data experience around it.
A CLEARER OPERATIONAL PICTURE
See what is ready. Understand what is blocked.
Bring asset status, maintenance history, and open work orders into a product built around readiness decisions. Explore the constraints behind availability and give each role a focused view of the work.
- Readiness by asset
- Work queues and maintenance constraints
- Role-specific operational views
POTENTIAL DATA SOURCESAsset records · Maintenance logs · Availability data
Fleet readinessOperational workspaceEXAMPLE
Your operational viewContext for the
next decision.
next decision.
ASSETSTATUSNEXT ACTION
Aircraft 01AvailableReview schedule
Aircraft 02In maintenanceInspect work order
Aircraft 03Awaiting partsCheck inventory
Unite the signals behind the next decision.
Illustrative product concept. Sample data, not customer results.
ENGINEERING CONTROL, BUILT IN
Your environment.
Your team.
Your next move.
Generated shouldn’t mean opaque. Keep the code, delivery path, and operating controls within reach of the people responsible for them.
A closer look at the stackYour data product
CloudAWS · OVH
EdgeKubeEdge
Code you can inspect
Download the generated application. Review the implementation, extend it, and make it part of your engineering workflow.
Spring Boot / React / TypeScriptDelivery with a destination
Cloud and edge paths, with deployment artifacts your team can configure for the target environment.
AWS ECS + ECR / OVH / KubeEdgeAccess with accountability
Platform roles, module-level permissions, and audit export help teams manage who can do what.
Roles / Permissions / Audit exportUpdates with context
Idempotent synchronization protects user-edited records and reports counts for a clearer change history.
Edit-aware sync / Auditable outcomesDESIGNED FOR THE LAST MILE
Keep the operational product
close to the work.
Designed for packaged workloads at the edge. Prepare in the connected enterprise, then bring selected application services and data to the environment that needs them.
Connected enterprise
Databricks + approved
AI service / provider
Transfer & installVersioned product package
Local edge
Packaged application & API
Data, queries & dashboard
LAB-STAGE DEMONSTRATIONSEvaluate continuity
Evaluate continuity
on your target hardware.
The reported proof-of-concept in an AWS-simulated KubeEdge environment showed packaged APIs, the application, queries, and dashboards available after the enterprise link was removed. Your pilot validates local application and data availability under the disconnection conditions your team actually faces.
Local scope depends on packaged data, services, models, and device resources. Cloud AI, fresh enterprise data, and new cloud deployments or updates need connectivity. Local AI needs a configured model and sufficient resources.
A FOCUSED FIRST STEP
Start with one product.
Expand through reuse.
Bring a real decision, representative data, and a destination. Shape a pilot that gives your data and operational teams evidence they can evaluate.
- 01
Choose the decision.
Pick one operational workflow and the representative sources it needs.
- 02
Define what good looks like.
Agree acceptance criteria and a comparable baseline before the work begins.
- 03
Evaluate where it will run.
Review the generated product in the target environment, including hardware and disconnected behavior where relevant.
- 04
Measure. Then reuse.
Measure delivery effort, quality, reuse, and offline continuity. Use the findings to shape the next product.
PLANNED PILOT MEASUREMENTS
Delivery time · Manual effort · Quality · Reuse · Offline continuity
Agree the baseline and success criteria before your pilot begins.A FEW GOOD QUESTIONS
Let’s get
into the details.
Thinking about your own data?
That’s a good place to start.
01What is a data product in EdgeSightAI?
A purpose-built application that makes enterprise data usable for a specific audience or operational task. It can bring together APIs, data views, dashboards, and AI-assisted exploration, backed by a generated Spring Boot backend and React/TypeScript frontend.
02How is the AI grounded in our data?
EdgeSightAI uses the actual Silver schema to inform recommendations for KPIs, personas, dashboards, and source mappings. It surfaces missing data and readiness gaps. Your team reviews and edits the product definition before generation.
03Which data sources and technologies are supported?
Source connections include PostgreSQL, MySQL, SQL Server, and Amazon S3, with Databricks orchestration for Bronze, Silver, and Gold data layers. Generated application artifacts include Spring Boot, React, TypeScript, database migrations, Docker, Helm, and GitHub Actions.
04Can our engineers inspect and change the generated code?
Yes. Application code can be downloaded and inspected by your team. EdgeSightAI also provides configurable compile and Docker verification, with visible check and repair outcomes. Generated output still needs your engineering review, configuration, and deployment validation.
05What can run disconnected?
Packaged application services and local data are the focus of KubeEdge delivery. A reported AWS-simulated proof-of-concept showed local APIs, application views, queries, and dashboards available after the enterprise link was removed. Your pilot validates the intended hardware and disconnection conditions. Cloud AI, fresh enterprise data, and new cloud deployments or updates need connectivity. Local AI depends on configured models and device resources; automatic sync-back is not assumed.
06How does EdgeSightAI handle control and ongoing updates?
Platform controls include roles, module permissions, and audit export. Data synchronization uses idempotent upserts, protects user-edited records, and reports audit counts so teams can understand what changed.
07How do we start a pilot?
Start with one operational decision, representative sources, and the environment where the product will run. Together, define acceptance criteria and a baseline, then evaluate effort, quality, reuse, and any required disconnected behavior. Use ‘Scope a pilot’ to share your workflow, data, and target environment.
08How mature is EdgeSightAI today?
The end-to-end lifecycle and simulated edge deployment have been demonstrated in laboratory environments. A pilot is the next step to validate customer data, intended hardware, security boundaries, and operational acceptance. Production hardening, repeatable packaging, and deployment-specific validation remain part of the path to production.
FROM YOUR DATA. FOR YOUR OPERATIONS.
Your next product.
Closer to the work.
One workflow. A clear acceptance bar.
Let’s scope what comes next.