Turn regulated business work into governed AI workflows.
JintellarCore helps teams design repeatable AI workflows, run them across tools and approvals, and preserve evidence for every model call, tool action, output, and decision.
Control layer
From work request to evidence
Reusable workflow profile
Policy and approval checks
Allowed tool, file, code, or API work
Evidence attached to the run
Buyer proof
Why it matters
AI is moving from chat to action.
Teams are beginning to let AI retrieve data, use tools, touch files, run code, and automate operational steps. The risk is no longer only bad answers. The risk is unapproved action, sensitive data exposure, missing evidence, and workflows no one can reconstruct later.
Sensitive data exposure
Requests can include customer, financial, legal, or internal context that must stay inside approved paths.
Unapproved tool actions
AI can reach tools, APIs, and systems where the wrong action creates operational risk.
Destructive or risky changes
File, code, data, and workspace changes need clear boundaries before they run.
Missing audit evidence
Teams need a reconstructable record, not scattered screenshots, logs, and manual approvals.
Product overview
One system to design, run, control, and prove AI work.
JintellarCore keeps workflow authoring, runtime controls, tool access, approvals, and evidence connected so regulated teams can move beyond one-off prompts.
DAG Studio
Design reusable workflow profiles from real business processes.
DAG Workbench
Run workflows with node-level progress, outputs, approvals, and evidence.
Runtime Controls
Route every model, tool, file, code, and approval step through policy before execution.
Evidence Case File
Preserve what happened, why it happened, who approved it, and what changed.
Product proof
Show the control path, not just the final answer.
Operators and reviewers need to see how the work was controlled, where approvals were required, and what evidence was preserved.
Policy decision before execution
Review the control decision before work reaches approved tools, files, code, or systems.
Human approval for risky actions
Keep sensitive or high-impact steps visible to reviewers before the workflow moves forward.
Replayable workflow evidence
Preserve prompts, context, routes, approvals, outputs, and artifacts in a single reviewable record.
Use cases
Built for repeatable, evidence-heavy work.
Start where the work already has clear inputs, repeated steps, reviewer expectations, and evidence requirements.
Finance research and diligence
Retrieve sources, compare evidence, extract KPIs, and draft cited review material.
Operations exception review
Investigate tickets, failed records, and escalations with approval-aware resolution paths.
Filing and document workflows
Normalize records, reconcile details, assemble packages, and preserve reviewer evidence.
Governed coding and data work
Inspect repos, run tests, create patches, process data, and attach execution proof.
Legal or compliance research
Retrieve authority, compare clauses, draft summaries, and keep source requirements visible.
Regulated internal automation
Turn recurring internal work into controlled workflows with approval and evidence by design.
Start with one workflow. Scale into a governed runtime.
JintellarCore can begin with a focused workflow package and expand into a broader control layer for AI work across teams, tools, and systems.