AI automation company replacing scripts, RPA & manual ops with agents.

An AI automation company replaces brittle scripts, RPA bots, and manual back-office work with LLM-grounded systems that read documents, hold context, call tools, and act on real workflows. The work spans document intelligence, voice automation, agentic ops, and approval routing — built with eval harnesses, audit logs, and human-in-the-loop gates where regulatory stakes demand them.

For tasks involving unstructured input — documents, conversations, free-text — LLM-grounded AI automation outperforms rule-based RPA by a wide margin and is more maintainable. For deterministic, high-volume, structured workflows, RPA is still the right answer. The right architecture often combines both. We'll be honest on the discovery call about which fits your specific workflow.

Six weeks from kickoff to a working v1 is the target. Document-intelligence pilots ship in 10 business days. Voice automation typically lands in 8–10 weeks because of telephony integration and multi-language QA. Agentic multi-step workflows are 6–12 weeks depending on tool-integration complexity and regulatory scope.

Three layers. Confidence scoring on every model output — low-confidence cases route to a human review queue rather than auto-post. Refusal layers reject out-of-scope inputs explicitly. Eval harnesses run continuously on production traffic and alert when accuracy drifts. Every action is audit-logged for forensic review and rollback.

Most v1 automation engagements at Aiinfox land between $35,000 and $150,000 fixed-price. The number depends on tool-integration count, compliance scope (HIPAA, SOC 2), and whether voice/telephony is involved. ROI is usually measured in staff-hours saved per month — most customers see payback in under 6 months.

Yes. We deploy to your AWS, Azure, or GCP VPC, to on-prem hardware for regulated workloads, or to our managed cloud. Self-hosted Llama 3 on vLLM is supported for zero-egress environments. Regional data residency (India, EU, US) is configurable. Audit logs are exportable for compliance review.

Overlap is large but the framing differs. AI automation focuses on replacing existing manual workflows with AI-grounded pipelines — usually with explicit step routing. AI agents are more autonomous: they decide which tools to call and how to sequence them. Most production deployments mix both — automation for predictable steps, agents for open-ended decisions.