How Agentic AI empowers your business

Ombrulla deploys goal-driven AI agents that execute end-to-end work—not just reply. They orchestrate steps across your systems and APIs, keep context with secure memory, and self-verify outputs to deliver repeatable, auditable results. With enterprise controls (SSO/RBAC, audit trails, human-in-the-loop) and deep integrations (ERP, CRM, ITSM, CLM, data platforms), you get secure, governable automation that cuts cycle time and errors, lowers cost-to-serve, and improves customer experience—without compromising compliance or risk.

Why leading enterprises choose Ombrulla Agentic AI

Illustration of AI governance with roles, approvals, and version tracking for secure enterprise control.

Built for governance

Keep control without slowing down. Set roles and approvals, track every change with version history, and audit any action in seconds.

Illustration of AI seamlessly connecting ERP, CRM, ITSM, CLM, and data platforms for smooth, unified workflows.

Integrated with your stack

Connect to ERP, CRM, ITSM, CLM, and data platforms—out of the box or custom—so work flows across systems with no copy-paste or data silos.

Illustration showing measurable AI outcomes with charts and metrics for faster cycles, higher accuracy, and cost reduction.

Outcomes you can measure

We design for your KPIs: faster cycle times, higher right-first-time rates, lower touch time, stronger policy compliance, and reduced cost per transaction.

Illustration emphasizing AI security with data encryption, key management, and isolated environments ensuring enterprise data protection.

Secure by design

Your data stays in your tenant. Encryption, secrets/key management, and environment isolation are standard—so security teams can say "yes."

Technical capabilities for building Agentic AI (what matters under the hood)

Illustration of coordinated AI agents in a graph or state-machine workflow managing skills, parallel tasks, retries, and timeouts.

Multi‑agent orchestration

Graph/state‑machine workflows, skill routing, parallelization, retries, and timeouts.

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Tool & API integration

Function calling, SDKs for custom tools, secure connectors, webhooks, and event buses.

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Illustration of AI with vector search and memory systems managing data retrieval, context awareness, and secure PII handling.

Retrieval & memory

Vector search, structured retrieval, short/long‑term memory, and PII‑aware context management.

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LLM/ML ops

Model selection, prompt/version control, fine‑tuning, distillation, eval suites, and guardrails.

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AgentOps & observability

Run traces, metrics, replay/simulation, test harnesses, and regression gates.

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Safety & governance

Policy engine, human‑in‑the‑loop checkpoints, content filters, and redaction.

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Performance & scale

Async execution, queues, concurrency controls, caching, and cost/latency budgets.

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Security foundations

SSO/RBAC, secrets/vaults, VPC peering, encryption in transit/at rest, and immutable logging.

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DevEx & CI/CD

Templates, scaffolding, IaC, environment promotion, and rollback strategies.

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Operator UX

Approval consoles, audit views, evaluation dashboards, and feedback loops to improve agents.

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Use Cases

Modernize the factory floor

Agentic AI modernizes factories by closing the loop between design, planning, production, and after-sales service, so decisions move from static SOPs to adaptive, data-driven actions on the shop floor. By orchestrating people, machines, and MES/ERP systems, AI agents reduce downtime, cut scrap, and keep schedules realistic in the face of real-world variability. The result is higher OEE, tighter quality, and faster response to demand changes—without adding headcount.

Digital Work Instructions & Compliance
  • AI agents transform SOPs and sensor signals into role-aware, step-by-step guidance that auto-captures evidence for audits.
Predictive Maintenance & Dispatch
  • Autonomous AI agents monitor telemetry, predict failures, raise work orders, and schedule technicians with parts and tools aligned.
Finite Capacity Scheduling
  • AI agents continuously balance labor, machines, tooling, and materials to re-sequence orders when constraints shift.
Supplier Quality Containment
  • Autonomous AI agents detect non-conformance early, launch 8D/CAPA workflows, and verify corrective actions across plants.

Faq

Agentic AI is software that uses goal-driven ai agents to plan tasks, call tools and APIs, keep context, and verify results—so work gets done end-to-end, not just answered.

AI agents break a goal into steps, retrieve relevant data, execute actions via integrations, check results against rules, and loop until success or human review.

Chatbots reply; autonomous ai agents execute. Agents orchestrate multi-step workflows across systems with memory, tools, and verification.

RPA automates fixed, UI-based tasks. Agentic AI handles variable, cross-system work using APIs, reasoning, and policy checks—often complementing existing RPA.

Traditional tools require rigid flows. Agentic AI adapts at runtime, selecting tools, handling exceptions, and escalating with human-in-the-loop.

Customer operations, KYC/onboarding, claims/disputes, IT helpdesk, order exceptions, procurement/CLM, financial close, regulatory reporting, and field service.

Yes, within guardrails. High-risk steps can require approvals, while low-risk steps auto-complete with full audit trails.

Guardrails (policies, allow/deny lists), retrieval to ground responses, evaluation checks, test sandboxes, versioned flows, and audit trails on all actions.

ERP, CRM, ITSM, CLM, data warehouses, identity (SSO/RBAC), messaging, payments, and custom APIs via connectors or adapters.

Data stays in your tenant; encryption in transit/at rest, secrets management, scoped permissions, environment isolation, and role-based access with logging.

Yes—through data residency options, auditability, retention controls, and PHI-aware configurations where required. (Confirm specific attestations with your vendor.)

Track cycle time, right-first-time rate, touch time, cost per transaction, SLA adherence, deflection rate, and revenue impact (conversion/save). Start with a baseline.

Pilot use cases often deliver 20–50% cycle-time reduction and 30–70% handle-time reduction in 6–10 weeks, depending on integration depth and policy complexity.

Pick a high-volume workflow with clear rules, measurable KPIs, API access, and manageable risk (e.g., onboarding, ticket triage, order exceptions).

Not necessarily. Many teams layer agents on top of existing RPA/BPM, using agents for reasoning, decisions, and exceptions while RPA runs stable steps.

By using retrieval-augmented generation, deterministic tool calls, validations against schemas/policies, and human approvals for sensitive actions.

Yes. Enterprises commonly deploy in private VPCs with private connectors, customer-managed keys, and egress controls.

Solution architect, API/automation engineer, domain SME, and an operations owner. Optional: prompt/flow designer and data engineer.

Usually a platform subscription plus usage (agent runs/API calls). ROI models tie cost to reduced handle time, deflection, or revenue lift.

Manufacturing, oil & gas, automotive, textile, energy & utilities, BFSI, insurance, healthcare, retail/eCommerce, logistics, IT/Shared Services.

Use SSO/RBAC, approval steps, test/stage/prod environments, versioned flows, policy guardrails, and audits with full replay.

Single agents own a workflow end-to-end; multi-agent systems divide work among specialists (e.g., planner, executor, reviewer) for scale and reliability.

Via connectors for read/write, governed queries, lineage, and cached retrieval to power context, analytics, and closed-loop reporting.