AI productivity tools are governed AI systems, copilots, agents, and workflow automations, that help employees complete knowledge work faster and more accurately. In enterprise contexts they integrate with systems of record (CRM, ITSM, HR, code repos) through secure connectors, run under MLOps/LLMOps discipline, and are measured against business SLOs rather than demo quality.

An AI agent workflow uses LLM-based reasoning and tool use, while RPA follows deterministic rules. RPA breaks when a form field moves or input format changes; AI agent workflows handle unstructured inputs, exceptions, and multi-step decisions. The two are complementary. We often orchestrate RPA bots as tools inside an agent workflow for maximum reliability and coverage.

Typically 6-8 weeks from discovery to first production go-live. Weeks 1-2 cover workflow mapping and architecture, weeks 3-5 cover platform build and integrations, and weeks 6-8 cover evaluation, controlled rollout, and handover. Later workflows on the same platform deploy much faster because foundations, connectors, and guardrails are reused.

Security is built in by design. We enforce secrets scanning, license policy, prompt audit trails, and RBAC on every tool the agent can call. Code context never leaves approved boundaries, deployments run in your cloud tenancy, and all model calls are logged for audit. We align with SOC 2, ISO 27001, GDPR, and HIPAA where applicable.

Yes. We integrate through MCP connectors and native APIs, with RBAC and data residency controls. Typical integrations include Salesforce, HubSpot, ServiceNow, Jira, GitHub/GitLab, Workday, SAP, SharePoint, Confluence, Slack, and Microsoft 365. Custom internal APIs are integrated the same way, with governance on which tools each agent is allowed to call.

We instrument every AI agent workflow with task-level telemetry: tasks completed, success rate, time saved, cost per task, and deflection rate. Baselines are captured in discovery, and dashboards compare pre/post performance per workflow. This converts AI productivity from a perception into a measurable operational KPI reviewed alongside other business metrics.

No. Our architecture uses model routing, so each task runs on the most cost-effective model that meets quality SLOs: frontier models for complex reasoning, smaller or fine-tuned models for high-volume tasks. This avoids vendor lock-in and cuts inference costs by 40-70% at scale without sacrificing quality.