Adding intelligence to a CRM
A CRM is full of structured data and unstructured history that nobody has time to read — which makes it ideal for AI features that summarise, enrich, and suggest. The four highest-value additions are contact enrichment, deal summaries, suggested next actions, and pipeline health scoring. Each follows the same pattern: take real CRM data, hand it to an LLM with strict instructions to use only that data, and return a structured, reviewable result. The features are powerful precisely because they are grounded — they make existing information usable rather than inventing new information. Build them as assistive layers a salesperson trusts and verifies, not as autonomous agents acting on their own.
How it works
Contact enrichment takes data you are permitted to use — public company pages, licensed enrichment APIs, your own notes — and uses the model to structure and summarise it into clean fields, never to fabricate facts. Deal summaries pass a deal’s actual activity history (emails, calls, notes, stage changes) to the model and ask for a concise summary of where things stand and what has happened, with the instruction to use only the provided history. Suggested next actions generate from the deal’s stage and recent activity a short list of sensible follow-ups for a rep to approve. Pipeline health scoring feeds structured signals — stage, age, last-contact recency, engagement — and asks for a risk score with a rationale that cites those signals, calibrated against your closed-deal history so the numbers carry meaning.
Architecture and prompt notes
Run anything non-blocking as a background job — nightly enrichment and pipeline scoring keep cost and latency under control — and generate on demand only when a user explicitly asks, such as summarising a deal they have open. Cache every result and regenerate only when the underlying data changes, so you do not re-pay for unchanged deals. Always surface sources and rationale next to AI output: a summary that links to the activities it drew from, a score that lists the signals behind it. This makes the feature trustworthy and lets reps challenge it. A solid summary prompt reads: “Summarise this deal’s current status in three sentences using ONLY the activity history below. Note the most recent meaningful interaction and the obvious next step. Do not infer anything not present in the data.” Keep the human in control of actions, ground every generation, and the AI layer becomes the part of the CRM people actually rely on.