Lead scoring and prioritisation agent

A machine learning model scores and ranks leads, augmented by a generative AI layer that enriches records with external indicators and maps your internal capabilities to each opportunity's requirements. The engine outputs a prioritised list to the business development team. In the grounding engagement an end-to-end run took about thirty minutes in place of the forty-plus hours previously spent collating and analysing data by hand.

The value is revenue and reclaimed selling time. In the grounding engagement the system was predicted to drive more than $120M in incremental revenue over twelve months at an ROI above 100, contributed to $127M in new business pipeline, cut time to outreach from weeks to thirty minutes and saved each sales representative five to eight days a month of research. Request-for-proposal value rose by around 25 percent and response rates by about 10 percent.

A history of past leads and outcomes to train the scoring model, a source of external market indicators to enrich records, and a structured description of your own capabilities for the generative layer to map against. Clean CRM data and a clear definition of a good lead matter more than raw volume.

The agent ranks; people decide who to approach. Treat the score as a prioritisation aid, not an instruction, and have the business development team confirm the top of the list. Watch for drift as markets move, retrain on fresh outcomes, and check that enrichment sources stay accurate so the ranking does not learn from stale signals.

You have more potential leads than the team can work by hand, a CRM with enough history to learn from, and a business development function whose time is the bottleneck. Leadership wanting clearer visibility of the pipeline is a strong signal.