01 — Problem
Senior staff reviewed every enquiry manually. Good leads waited behind poor-fit requests and first calls started without context.
Example formatThis is an illustrative example showing the case-study format. It is not a real client project and contains no real results.
Example — B2B services firm
An AI lead-qualification desk for a B2B services firm
Illustrative scenario: an AI agent researches every inbound enquiry, scores fit and gives sales a briefing before the first call.
- Industry
- Professional Services
- Timeline
- Illustrative: 6–8 weeks
- Year
- —
- Services
- AI Automation, AI Agents, Lead Generation
02 — Strategy
Automate research and scoring, keep humans responsible for the decision, and make every step traceable.
03 — Solution
An agent with scoped tools reads each enquiry, researches the company from public sources, scores it against the ideal client profile, writes a CRM summary and notifies the right person. Low-confidence cases go to manual review.
04 — SEO strategy
Service pages restructured around buyer problems to attract better-fit enquiries in the first place.
05 — AI implementation
Evaluation suite of historical enquiries, confidence thresholds, and full traces reviewed weekly.
Technology
- Anthropic Claude
- Tool calling
- CRM API
- Slack notifications
- Tracing
Target outcomes (illustrative)
Every enquiry researched before first contact
Senior time focused on qualified leads
Consistent scoring criteria
Next step