Solutions
AI across your business operations
A specialized agent for each business function. AI handles analysis and judgment; system functions execute deterministic business operations.
Clear responsibilities
How agents work across business functions
Models analyze and reason. System services execute financial calculations, inventory updates and state transitions.
| Business function | AI agents: analysis and judgment | Business systems: execution |
|---|---|---|
| Sales | Pricing evidence, follow-up timing and risk alerts | System rules determine prices and credit |
| Procurement | Price comparisons, delivery exceptions and alternatives | Systems enforce approvals and quantity checks |
| Warehousing | Shortage alerts, reconciliation differences and stocktake tasks | Systems maintain inventory and batch states |
| Production | Scheduling suggestions, delay impact and delivery risks | Systems manage work orders and completion states |
| Quality | Inspection evidence, suspect batches and historical responses | Systems enforce inspection decisions and release states |
| Finance | Reconciliation, variance explanations and unusual transactions | Systems calculate amounts and reconciliation relationships |
| Human resources | Policy questions, document preparation and task follow-up | Systems enforce permissions and approval steps |
| Management | Business questions, variance analysis and risk summaries | Systems define data scope and calculation rules |
Scenario 01
Intelligent sales operations
From relying on a salesperson's memory to giving every customer continuous attention.
Common challenges
- Follow-ups depend on personal memory and chats; relationships suffer when staff leave
- Quotes lack a clear basis in historical prices, credit limits and margin requirements
- Payment reminders, renewals and churn signals are not monitored systematically
- CRM records are disconnected from actions; standalone AI tools lack a complete customer view
The JitAI approach
- Sales agents work as elements inside the system , with access to the full customer, order, payment and credit context
- AI provides pricing evidence, follow-up timing and risk alerts ; system rules determine prices and credit
- Role-based data access gives salespeople their own accounts and managers an overview
- Actions stay controlled: AI cannot bypass approval to change prices or payment terms
Expected outcomes
- Automated follow-ups and payment reminders reduce dependence on individual memory
- Traceable quotes and automatic approval routing for unusual discounts
- Earlier visibility into churn and overdue-payment risks
Outcomes depend on the actual implementation and business context.
Who it fits
B2B businesses with many customers, long sales cycles and relationship-led sales; distributors, project sales teams and organizations seeking to make AI part of their CRM workflows.
Scenario 02
Procurement cost control
From intuition-led buying to evidence-based recommendations and enforced approvals.
Common challenges
- Historical prices are scattered across spreadsheets and chats
- Delivery, quality and schedule exceptions are often discovered too late
- Missing alternatives lead to production stops or expensive emergency purchases
- Approval workflows exist in the system, but decisions remain in people's heads
The JitAI approach
- Embedded procurement agents read supplier pricing, delivery and quality histories
- AI produces Price comparisons, delivery exceptions and alternatives ; systems execute approvals and quantity checks
- Supply-demand matching and cost rules execute deterministically through services and workflows
- Exceptions feed back to the development assistant for refinement
Expected outcomes
- Evidence-based comparisons and automatic approvals for exceptional purchases
- Earlier delivery warnings and suggested alternatives
- Consistent cost definitions and real-time visibility into variances
Outcomes depend on the actual implementation and business context.
Who it fits
Manufacturers and trading companies with diverse purchasing needs and distributed suppliers; cost-sensitive businesses seeking to preserve procurement expertise in their systems.
Scenario 03
Intelligent inventory management
From simultaneous overstock and shortages to early warnings and explainable discrepancies.
Common challenges
- Overstock and shortages coexist without early detection
- Month-end stocktakes reveal discrepancies without clear causes
- Manual batch, expiry and location updates propagate errors
- Replenishment is disconnected from sales, production and procurement
The JitAI approach
- Inventory elements expose batches, locations and state machines to AI
- AI provides Shortage alerts, reconciliation differences and stocktake tasks ; systems maintain inventory and batch states
- Replenishment connects sales forecasts with incoming purchases across functions
- All inventory changes use standard business operations for consistency and auditability
Expected outcomes
- Earlier shortage and overstock alerts support proactive intervention
- Stocktakes investigate causes, not just count differences
- Real-time visibility into turnover, slow-moving stock and expiry risks
Outcomes depend on the actual implementation and business context.
Who it fits
Manufacturing, pharmaceutical, food and retail businesses with many SKUs or sensitive expiry requirements; organizations managing multiple warehouses and inventory pressure.
Scenario 04
Production process optimization
From manual scheduling and phone calls to informed schedules and delivery warnings.
Common challenges
- Scheduling logic depends on the individual planner
- Delay impact is unclear and requires repeated calls to confirm
- Manual work-order and completion updates leave data behind reality
- Disconnected quality decisions and release workflows obscure responsibility
The JitAI approach
- Production agents read work orders, processes, equipment, materials and work in progress
- AI provides Scheduling suggestions, delay impact and delivery risks ; systems manage work orders and completion states
- Workflows and events enforce state transitions; AI does not change states directly
- Long-running agents can monitor production asynchronously and perform scheduled checks
Expected outcomes
- Visible, adjustable scheduling suggestions reduce dependence on one planner
- Calculated delay impact reveals delivery risks earlier
- Traceable process and quality milestones clarify responsibility
Outcomes depend on the actual implementation and business context.
Who it fits
Discrete and process manufacturers with complex workflows and delivery pressure; factories managing variable capacity, fluctuating orders and challenging schedules.
Scenario 05
Financial analysis and review
From manual month-end reconciliation to continuous accounting and variance analysis.
Common challenges
- Sales, procurement, inventory and finance use conflicting spreadsheets
- Month-end reconciliation requires manual investigation of every discrepancy
- Complex balances and reconciliation relationships hide exceptions
- Financial reviews depend on individual experience and inconsistent definitions
The JitAI approach
- AI handles Reconciliation, variance explanations and unusual transactions ; systems calculate amounts and reconciliation relationships
- Connect procurement, sales, inventory and finance through a single source of truth
- Management can ask about performance, variances and risks within defined data rules
- Changes use governed business operations for consistency, auditability and rollback
Expected outcomes
- Continuous accounting and accessible data reduce month-end rushes
- Explainable variances and consistent, reusable review criteria
- Earlier alerts for unusual balances and bad-debt risks
Outcomes depend on the actual implementation and business context.
Who it fits
Businesses with long transaction chains, high document volumes and expensive cross-team reconciliation; management teams needing frequent reviews on consistent data.
Scenario 06
Enterprise risk management
Risk management starts with complete business context and enforced permissions.
Common challenges
- Credit, contracts, approvals and financial risks are scattered across systems
- AI tools without role and data boundaries cannot safely enter core workflows
- High-risk actions lack mandatory human approval and traceability
- Logs alone cannot reconstruct the evidence behind AI decisions
The JitAI approach
- Unified RBAC and data scope: people and agents share the same permission model
- Check authorization before execution at role, workflow and data-scope levels; interface visibility is not authorization
- High-risk actions can require mandatory human approval
- Trace actions with reasoning evidence and data snapshots to support review and rollback
- Inject credentials through environment variables and keep secrets out of logs and sensitive data out of persistent traces
Expected outcomes
- Agents act only through authorized business capabilities that can be enumerated and audited
- Risk controls and business operations use the same system rules
- Evidence supports audits, rollback and governance discussions
Outcomes depend on the actual implementation and business context.
Who it fits
Enterprises with demanding audit and data-governance requirements; decision-makers bringing AI into core operations while retaining control and traceability.
Getting started
Start with your most pressing business problem
Try it free
Download the desktop edition and validate one business scenario before expanding.
Launch a core scenario
Choose a priority workflow and validate an initial business loop.
Expand adoption
Deploy the server edition and extend to more workflows and business agents.
Keep improving
Operational, agent and human feedback keep systems aligned with changing needs.
Tell us about your business scenario
We will explore which workflows are ready for AI and where to begin.