Start with one business process. Turn it into a governed AI workflow.
JintellarCore combines workflow authoring, governed execution, technical workspace routing, and custom tools so teams can automate real work without losing control.
Workflow package
Business process to controlled run
How a workflow package works
Package the process before scaling the platform.
A workflow package turns one repeatable business job into a controlled path with inputs, tools, approvals, technical steps, outputs, and evidence review.
Map the business process
Define the inputs, reviewers, systems, exceptions, and output expectations.
Author the DAG profile
Turn the process into a reusable workflow with clear steps and handoffs.
Add tools, approvals, and evidence rules
Connect only the approved capabilities and decide where review is required.
Run in Workbench
Operate the workflow with progress, outputs, exceptions, and review state visible.
Route technical steps when needed
Send code, file, notebook, script, or data work to Coding Workspace under boundaries.
Review outputs and improve the workflow
Use evidence and feedback to tighten the workflow without losing control.
Workflow package examples
Start where the business work is already repeatable.
Each package begins with a clear input set, a bounded AI workflow, explicit controls, and output evidence that teams can review.
Finance research workflow
Input
Filings, transcripts, prior notes, datasets
AI steps
Retrieve, compare, extract KPIs, summarize, draft memo
Controls
Source requirements, approval gates, sensitive data policy
Output
Cited memo, evidence bundle, review trail
Operations exception workflow
Input
Tickets, breaks, failed process records, customer or internal requests
AI steps
Classify, investigate, match policy, propose resolution
Controls
Escalation, approval, blocked action rules
Output
Recommended resolution, case file, audit evidence
Filing and document workflow
Input
Documents, forms, records, templates, business rules
AI steps
Normalize, extract, reconcile, assemble package
Controls
Validation, reviewer approval, evidence capture
Output
Filing package, exception list, approval record
Governed coding and data workflow
Input
Repo context, CSVs, notebooks, scripts, bug reports, analysis requests
AI steps
Inspect, generate code, run tests, create artifacts, summarize results
Controls
Workspace boundaries, command restrictions, approval for mutations
Output
Patch, notebook, report, spreadsheet, test log, evidence trail
Product surfaces
The modules stay focused on the work outcome.
JintellarCore product surfaces support the package without turning the buyer story into an implementation map.
DAG Studio
Author the workflow profile and review the structure before it runs.
DAG Workbench
Run and review the workflow with progress, outputs, approvals, and evidence.
Coding Workspace
Execute technical nodes for code, files, notebooks, tests, and data work.
Skill Hub
Connect approved tools and company-specific capabilities to the workflow.
DAG Workbench execution view
Run packaged workflows with step progress, outputs, approvals, and evidence in one review surface.
Technical steps stay attached to the workflow
Coding and tool-heavy steps can produce artifacts, logs, and outputs without leaving the workflow record.
Delivery model
Build the first workflow, then expand the control layer.
The first package should be narrow enough to pilot, real enough to matter, and controlled enough for reviewers to trust.
Choose one workflow
Build a starter profile
Connect tools and data
Add controls and approval rules
Run pilot with evidence review
Expand to more workflows
Build the first workflow, then expand the control layer.
Start with a workflow package that produces real outputs, visible controls, and evidence reviewers can trust.