AI Automation Consulting

We score candidates on three axes: volume × time-per-instance (raw labour saved), error rate of the current human process (automation often improves quality), and integration complexity. The first project should be high-volume, high-error, low-integration — that's where the ROI shows up fast and the risk is contained.

Mostly LLM agents calling deterministic tools — RPA where it actually fits, agents where reasoning is required. We're skeptical of pure 'autonomous agent' frameworks for production work; they're powerful for prototypes but fragile in production. Our default is constrained workflows with explicit tool boundaries.

Human-in-the-loop on high-stakes actions, output validation against schemas, dollar-value thresholds that escalate to humans, full audit logs, and replay-ability for every decision. We never give an LLM unchecked write access to systems that matter — the workflow design enforces 'propose then confirm' for anything irreversible.

Typical pattern: a workflow engine (Temporal, Inngest, or Step Functions) orchestrates LLM calls plus deterministic steps. Background queue for async work. Observability via OpenTelemetry. Each automation has its own dashboard with success rate, escalation rate, and cost per execution. We refuse to ship without these.

Cautiously. We capture human corrections as training data and surface them in an evaluation dataset, but we don't auto-update prompts or models from production traffic. Humans review and approve any prompt change. 'Self-improving' is a marketing claim that often hides quality regressions.

Discovery + first automation: 6–10 weeks, 1–2 senior engineers, plus a part-time lead from your team to own the integration with internal systems. Subsequent automations: usually 3–5 weeks each once the platform is in place.