Artificial Intelligence
AI workflows that run your operations, not just
Promover designs, builds and runs AI-powered workflow platforms. We model your processes in BPMN, put AI agents to work inside them, and keep people in control of every decision that matters, with a full audit trail from trigger to outcome.
Most AI projects stall between a promising prototype and a system people rely on. The gap is rarely the model. It is the process around it: who approves what, which system gets updated, what happens when the AI is unsure, and how anyone checks what it did. Promover closes that gap.
Agentic Platforms
We build the platform layer that lets your organisation deploy AI agents with confidence: an agent catalogue, configuration per business unit, guardrails, review queues and a value dashboard that shows what every agent delivers. Agents call your systems through secure, versioned tool interfaces (including MCP servers), work from your data
BPMN-driven Workflow Automation
Every workflow we automate is modelled in BPMN 2.0, the standard your business analysts, auditors and engineers can all read. AI agents become tasks inside the process, next to service tasks, timers, business rules and human approvals. You can see the whole flow, change it without rewriting code, version it,
AI Workflow Orchestration and Operations
We take workflows from pilot to production and keep them healthy: orchestration with state, retries and idempotency; evaluation suites that test agent accuracy before every release; tracing, token-cost and latency telemetry; and model routing so each task uses the most appropriate model for cost and quality.
Intelligence and Decision Dashboards
Workflows run on signals. We build the data pipelines, KPI layers and role-based briefings that tell people what changed, why, and what to do next, and we performance-engineer them so dashboards and drill-downs stay fast at enterprise scale.
AI does the work. BPMN keeps it Accountable.
Map
We model the current process in BPMN with your team: triggers, decisions, systems touched, approvals and exceptions.
Design The Agents
We decide which tasks an AI agent takes on (read a document, classify a request, draft a reply, reconcile records, recommend an action) and define each agent's tools, data access and confidence thresholds.
Add Guardrails
Business rules, approval gates and human-in-the-loop review are placed directly in the process. Low-confidence or high-impact cases are routed to the right person with the context and the agent's reasoning.
Run and observe
The workflow engine orchestrates every case. Each agent action and human decision is logged, traceable and exportable for audit.
Improve
Evaluation suites, outcome tracking and cost telemetry show where to raise automation rates, and the BPMN model is updated and versioned as the business changes.
Why BPMN matters for AI
- One shared picture of the process for business, risk and engineering teams.
- Agents operate inside defined boundaries instead of free-form loops.
- Every case can be replayed step by step: what triggered it, what the AI did, who approved.
- Processes change by editing the model, not rewriting the system.
- Works with standard BPMN engines (such as Camunda or Flowable) or a lightweight engine embedded in your platform.