Service · Applied AI
AI Agents that collect.
No demos. No generic copilots. Operational agents that close tasks tied to a KPI: collections, qualification, support, and back office. With guardrails, observability, and ROI tied to business metrics.
The problem
Most AI pilots never make it to production.
And those that do don't show up on the P&L. The difference isn't the model: it's the rigor with which you choose the case, close the loop, and measure.
Traditional Pilot
Nice demo, no traction
- Case chosen for novelty, not ROI.
- No observability: you don't know when the agent hallucinates.
- Ad hoc guardrails, risks not reported to the board.
- No handoff to operations: the AI team "babysits it."
- Success metric: tokens consumed.
Typical cases
Where an agent pays for its own build.
We start with the case that has clear margin and low regulatory exposure.
Early-stage collections
Multichannel contact with early debtors using adaptive scripts and CRM logging. 10–20 point lift in 30-day recovery.
B2B lead qualification
Research, scoring, and routing of leads to the right SDR. Cuts response time to minutes.
Document back office
Reading, validation, and data entry of documents against the ERP. Closes the loop with human-in-the-loop when needed.
Methodology
From case to run in four phases.
The same editorial method: discovery, plan, execution, run.
Case Discovery
Case selection by incremental margin and regulatory exposure. Hypothesis and success metric signed off.
Design with Guardrails
Agent architecture, data policy, and risk matrix approved before writing code.
Build & Pilot
Construction, testing with real data, and controlled pilot with active observability.
Run & Scale
Handoff to operations, runbook, SLAs, and expansion plan to adjacent cases.
Next step
Is your AI still a pilot that nobody defends before the board?
In 60 minutes we pick a defensible case and map out a plan to production in 8 to 14 weeks.