Turn complex business work into live AI workflows.
Jintellar gives teams a live AI workflow system that plans, executes, and adapts across their tools, approvals, and operating rules so they can focus on results instead of repetitive work.
What DAG Studio Provides
Workflow automation first. Governed AI execution underneath.
Users do not need another generic workflow builder. They need a way to turn repeatable work into governed AI runbooks: visually designed, validated, published, executed, and replayed with evidence.
Visual workflow profiles
Drag nodes, connect edges, set schemas, define success criteria, and publish reusable DAG profiles for execution.
Domain node packs
Start with finance, research, coding, approval, policy, evidence, and artifact nodes instead of blank-canvas automation.
Governed planner handoff
Profiles publish into DAG Planner / Workbench, where runtime execution, approvals, evidence, and replay take over.
Why Governance Matters
AI can now touch real business systems.
Agents can retrieve data, call tools, update files, run commands, trigger workflows, and interact with customer or operational systems. That creates leverage, but it also creates risk.
Risk categories
We do not just add AI to your business. We design the system around how AI should be allowed to act.
Custom Agentic Workflow Studio
DAG Studio turns one business process into a reusable agent workflow.
A workflow in DAG Studio is not a generic chatbot prompt. It is a tailored runbook for one operating process: the inputs it receives, the agent steps it runs, the tools it can use, the approvals it needs, and the outputs it must produce.
What the workflow actually is
Each workflow is a reusable operating profile for a specific business job. Teams define the steps, access, review gates, and output requirements once, then run the same flow on a schedule or on demand.
Map the business process
Start with one real job such as support triage, filing review, legal research, diligence, or internal ops, then define what should trigger the run.
Assemble the agent workflow
Connect agent steps, data retrieval, tool actions, conditions, approvals, and output rules into one reusable workflow profile.
Run, review, and refine
Schedule repeat runs, inspect outputs and evidence, handle exceptions, and improve the workflow as the business process changes.
Workflow building blocks
Workflow runbook
Each node can define its input schema, tool scope, output contract, success criteria, and whether approval is required before the workflow moves forward.
JintellarCore Runtime
The control layer beneath every AI-powered system.
Governance is not the headline customers buy first, but it is the reason AI automation can touch real work. JintellarCore classifies requests, routes model calls, gates risky actions, isolates execution, and records evidence.
- ClassifyDetect sensitive data and route work by policy before cloud or tool access.
- GovernEvaluate tenant, principal, data class, workspace scope, and capability claims.
- ExecuteRun tools, code, files, and workflows through Skill Hub and isolated runtimes.
- ProvePreserve decisions, model attempts, tool calls, artifacts, approvals, and audit chain.
Cloud route blocked with evidence
Use product proof to show classification, guardrail decision, route reason, and traceable audit metadata.
Non-overridable Destructive Action Blocks
Some actions should never be delegated to AI.
High-risk operations can sit behind absolute deny rules. The AI cannot perform them, even if the underlying account technically has permission.
Product Surfaces
How governed AI work appears to operators.
These views show what teams actually review: the control center for live state, the audit record for policy decisions, and the execution log for workflow progress.
Runtime control center
A live operating view for workflow state, policy decisions, model routes, execution status, and approval posture.
Policy decision record
Shows the request, data classification, guardrail result, route decision, model attempt, approval status, and trace identifiers.
Workflow execution trace
Follows each automated run across nodes, tool calls, retries, outputs, approvals, and exceptions.
Business Use Cases
Governed automation for repeatable, evidence-heavy work.
JintellarCore is designed for workflows where AI needs clear boundaries, observable execution, human approval points, and an audit trail that teams can review after the work runs.
Finance research
Buy-side research, PE diligence, quant research, cited analysis, and reproducible evidence bundles.
Support operations
Ticket intake, priority scoring, routing, escalation, daily summaries, and exception review.
Accounting filing
Document collection, reconciliation checks, filing package preparation, and approval routing.
Legal research
Source retrieval, clause extraction, precedent comparison, draft brief assembly, and citation tracking.
Security and coding
Repository work, vulnerability analysis, code changes, command execution, and approval-gated mutations.
Regulated operations
Healthcare, finance, insurance, internal enterprise workflows, and evidence-heavy review processes.
Deployment Paths
Start with the workflow, then scale the runtime.
The work can start as a focused strategy project, expand into a governed automation build, or grow into full JintellarCore runtime adoption.
Strategy project
Map the workflow, identify where AI creates leverage, define controls, and design the operating system around the business process.
Governed automation build
Build the workflow, connect tools and data, add approval gates, automate repeat runs, and review results with evidence.
Full runtime adoption
Adopt JintellarCore as the control layer for models, skills, memory, tools, workspaces, audit, and destructive-action blocks.
Build the system that solves the problem, then govern how AI acts inside it.
Jintellar designs the workflow, adds AI where it creates leverage, and uses JintellarCore to control how that AI acts when the workflow runs.
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