VerifiedEnd-to-end testing complete
The enterprise data platform for agents
Give the best agents
access to your most trusted enterprise data
EngineHub turns fragmented enterprise data into high-quality, consistently defined, access-controlled, and fully traceable data services for any agent through OData, Skills, and open APIs.
Agents can evolve or be replaced freely while your data, semantics, permissions, lineage, and audit trail stay in place.
Connect leading agentsImprove data quality with AI-ETLUnify business semantics and lineageTask-level access and end-to-end auditStandards-based OData servicesPrivate deployment available
enginehub.architecture
Data Sources
Data Warehouse
Relational Database
Files / APIs
Real-time Streams
ENGINEHUB
AI-ETL
Semantic Layer
Access Governance
Lineage & Audit
OData
AGENT LAYER
Analytics Agent
Decision Agent
Engineering Agent
Insights Agent
Business Outcomes
Data Preparation
Business Analysis
Business Decisions
Business Actions
A New Category
Enterprises do not need more reports,
they need better decisions
Traditional BI asks analysts to model data and build reports in advance, leaving business users to find answers in the numbers. Real business questions arise unexpectedly, span domains, and cannot fit a predefined drill-down path.
With EngineHub, users state the business question directly. An agent retrieves and prepares the right data, performs the analysis, explains the causes, and recommends what to do next.
Traditional BI turns data into reports. EngineHub puts data in the hands of agents.
Traditional Approach
EngineHub Approach
Submit a request
Ask the business question directly
Wait for the data team
The agent understands the data immediately
Develop ETL
The agent creates and runs a data plan
Build reports
The agent analyzes and visualizes on demand
Business users interpret numbers
The agent delivers conclusions, causes, and advice
Requeue every new question
Continue asking in the same conversation
Business Decision Journey
From a business question
to an actionable decision
User question
Why did gross margin fall in the East region this quarter? How much could it improve if we reduce ineffective promotions?
01Understand the business questionChecking access…
Interpret the business intent and scope
Why did gross margin fall in the East region this quarter? How much could it improve if we reduce ineffective promotions?
Progress1 / 8
Open Agent Ecosystem
Do not bet on a single agent,
always use the best one
Agent capabilities are evolving rapidly. Data access, permissions, and business semantics should not lock an enterprise into one agent or vendor.
EngineHub decouples agents from enterprise data. The same data services work across agents, so you can choose based on capability, cost, security, and use case.
The best agent today—and a better agent tomorrow—can use the same enterprise data.
Enterprise Data FoundationStays in place
Closed Agent Platform
EngineHub
Use the platform's built-in agent
Connect the leading agents you choose
Agent upgrades depend on one vendor
Benefit from progress across the agent market
Reconnect data when changing agents
Change agents without changing data services
Data-access rules live inside each agent
Govern permissions, semantics, and audit centrally
Capabilities are limited by the platform
Continuously add new agents, Skills, and tools
Agent compatibility
Standards compatibleSupports open protocols
Adapter requiredCustom connection required
Open OData Data Services
One trusted data service
for every agent
EngineHub serves quality-controlled, semantically organized, authorized enterprise data to agents through standard OData. Agents can inspect metadata, filter, aggregate, join, and follow up without a bespoke interface for every agent.
OData makes trusted enterprise data a common language that every agent can understand and use.
Standardized
Not a proprietary interface for one agent; any OData-capable agent can use it.
Self-describing
Metadata lets agents understand objects, fields, relationships, and query scope without manual documentation.
Task-level authorization
Agents use short-lived task tokens for authorized data; long-lived identity credentials cannot query OData directly.
Read-only and controlled
Separating reads from writes reduces the risk of external agents affecting source systems.
Replaceable
Switch agents without rebuilding data services, semantics, or permissions.
// OData ACCESS SEQUENCE
External AgentEngineHub
Request access for the current analysis task
EngineHubExternal Agent
Return a short-lived task token + data Skill
External AgentOData Service
Query with the task token
OData ServiceEnterprise Data Assets
Run a read-only query
OData ServiceExternal Agent
Return controlled results
AI-ETL
Trusted data is not prepackaged,
agents create it
Users describe the problem and target data. The agent reads schemas and samples, creates a reviewable transformation plan, cleans and joins data, checks results, fixes errors, and preserves reusable assets, definitions, and lineage.
Data quality before and after
Raw data
✗NULL values: 2,847 rows
✗Inconsistent formats: 16%
✗Duplicate records: 423
✗Missing definitions: Yes
✗Lineage: None
After AI-ETL
✓NULL values: 0 rows
✓Formats: Standardized
✓Duplicates: Removed
✓Definitions: Complete
✓Lineage: Complete
Core capabilities
Turn natural-language needs into structured data plans
Show source understanding, assumptions, steps, and risks before execution
Support incremental, full, idempotent, and write-back strategies
Correct automatically from execution errors and samples
Record status, logs, and lineage for every run
Make data assets reusable by future agents
Let data engineers focus on quality rules instead of repetitive scripts
OData answers how to open data to different agents; AI-ETL answers why agents can trust it—shrinking delivery from weekly queues to a conversation.
From Queries to Decisions
Trusted data moves agents from
answering questions to shaping decisions
Charts are evidence, not the final product. EngineHub + Agent delivers the why and what to do next.
Traditional Reports
Metrics and charts
Natural-language queries
Return numbers for a question
EngineHub + Agent
Conclusions, causes, impact modeling, recommendations, and next actions
Continuous conversation · Read-only demo data
Why did profit fall this quarter?
Driver analysis complete: lower promotion efficiency in the East (-2.8%) and higher raw-material costs (-1.4%) together drove a 4.2% quarterly profit decline.
What if we exclude new-product investment?
Recalculated: excluding new-product investment, core-business margin fell 3.1%, narrowing the decline by 1.1 points.
Give me three improvement scenarios
Modeling complete: ① Optimize promotion mix (+2.6%) ② Consolidate supply-chain purchasing (+1.2%) ③ Reduce long-tail SKUs (+0.8%). Recommend ① first: lowest risk and shortest payback.
Create an action list for regional managers
Action list created with seven tasks, suggested owners, and deadlines. Decision evidence is archived and ready for review.
Security
Agents can be more powerful,
while the enterprise stays in control
Every data access maps to a real user, a defined task, and an authorized scope—with a traceable history.
Who can ask which questions?
Check intent and responsibility before a question so users stay within their remit.
Which data can an agent access?
Short-lived task tokens limit data scope, isolate user sessions, and support fine-grained task authorization.
What may appear in an answer?
Check sensitive and out-of-scope data before responding, returning only authorized conclusions.
Can every access and decision be audited?
Audit data access, tool calls, and governance decisions with readable, versioned, reviewable rules.
Core capabilities
✓Check intent and responsibility before questions
✓Check sensitive and out-of-scope data before answers
✓Isolate sessions between users
✓Support shadow mode and staged rollout
✓Support private and intranet deployment
✓Keep rules readable, versioned, and reviewable
AUDIT LOG · LIVE
zhang.weiQuery East-region sales data✓ Authorized
zhang.weiAI-ETL executes transformation plan✓ Complete
zhang.weiRead promotion-spend table✓ In scope
systemReview answer content✓ Passed
Use Cases
Enterprise data × agents: three high-value use cases
Three common ways to address core enterprise scenarios.
01
Business DecisionsCross-domain drivers, straight to action
Connect sales, cost, inventory, supply-chain, and marketing data for driver analysis, impact modeling, and action. Agents deliver not just numbers, but why and what to do.
Sales analysisMargin driversScenario modeling
02
Agent Data EngineeringFrom messy raw data to reusable assets
Create high-quality assets with lineage, shifting data engineers from repetitive scripts to quality rules and governance.
AI-ETLData lineageAsset reuse
03
Proactive Business InsightsLet insights find people—not the reverse
Agents continuously scan for anomalies and opportunities, proactively surfacing the signals that need attention.
Anomaly detectionProactive alertsContinuous monitoring
Product Comparison
Not smarter reports,
a different way to use data
Not smarter reports—a different way to use data.
| Dimension | Traditional BI | Built-in Conversational BI | EngineHub |
|---|---|---|---|
| Primary deliverable | Reports and dashboards | Natural-language queries | Conclusions, explanations, recommendations, and actions |
| Question scope | Predefined | Limited by the semantic model and built-in agent | Agents break down long-tail questions on demand |
| Agent choice | None | Usually platform-provided | Connect and switch leading agents |
| Data interface | BI-specific connectors | Proprietary platform APIs | OData, Skills, and open APIs |
| Data processing | Depends on manual ETL | Still depends on existing data preparation | Agent-driven AI-ETL |
| Data assets | Built around reports | Built around the platform | Independent of agents and continuously reusable |
| Decision support | Requires human interpretation | Returns numbers or summaries | Causes, modeling, advice, and follow-up questions |
| Governance | Table, column, and row permissions | Depends on the product | End-to-end governance of identity, tasks, data, and answers |
Live Demo
Skip the reports and ask
a business question directly
Skip the reports and ask a business question directly.
Driver Analysis & Scenario Modeling
Ask a real business question. The agent discovers and prepares data, identifies drivers, models scenarios, and delivers an actionable recommendation.
Real-time analysisTraceable data sourcesDecision evidence included
No sign-in requiredRead-only demo dataNo real customer information
Try this demo now →demo · read-only
User: Why did profit fall in the East region this quarter?
Agent: Drivers: ineffective promotions -2.8%, raw materials -1.4%.
Scenario A: Optimize promotion mix → Estimated +2.6%
Scenario B: Consolidate purchasing → Estimated +1.2%
Data sources
sales_2024_q3promotion_costproduct_skuValidate With Your Data
Bring a real business question
and see whether an agent can solve it
Choose a de-identified dataset and a question that once required an analyst or data engineer. We will show how EngineHub connects and prepares the data, performs the analysis, and produces a traceable recommendation.
No sensitive raw data is required. The first conversation only confirms the business question, data scope, and validation approach.
✓Use your real business question—not a standard sales demo
✓Validate an end-to-end data task—not just features
✓Get a clear fit assessment and integration approach
Frequently Asked Questions
FAQ
Common questions before purchasing. Book an enterprise data validation for answers tailored to your technical requirements.