Project snapshot: AI Intake Assistant for Sales + CRM Hygiene (anonymized)

Purpose: Show a practical “intake + routing” pattern that turns unstructured inbound info into structured CRM-ready data.

Primary CTA: Let's talk

TL;DR

An AI intake assistant captured lead details from emails/forms/calls, enriched and normalized fields, and routed opportunities to the right owner with clean context.

The workflow

Problem: Leads arrived from multiple channels with inconsistent detail; slow follow-up; messy CRM data; unclear ownership.

What we automated:

Extract structured fields from inbound inquiries (company, role, need, timeline)
Ask 1–3 targeted follow-up questions (email or form flow) when critical info is missing
Create/update CRM records and assign ownership based on rules
Produce a short “sales-ready” summary and next-step recommendation

What stayed human-led:

Pricing negotiation and discovery calls
Final qualification decisions for edge cases

Constraints

Data correctness mattered more than “automation rate”
Avoid duplicate CRM records
Provide auditability (what was inferred vs. explicitly provided)
Be conservative with enrichment; don’t introduce unverifiable data

Approach

Intake design

Define a minimum viable lead schema (the handful of fields sales actually needs)
Treat uncertain fields as unknown, not guessed
Keep follow-ups short and specific

CRM + routing

Deduping strategy (email domain + company + fuzzy match)
Routing rules (territory, industry, product line, round-robin)
Human review step for low-confidence matches

Guardrails

Don’t fabricate company details; use verified sources or leave blank
Log decisions and confidence signals for later tuning

High-level architecture

Inbound channels → normalization layer
Agent orchestrator with tool calls:
- CRM read/write
- enrichment (where permitted)
- messaging (email/Slack) for follow-ups/notifications
Observability: routing outcomes, correction rate, response-time

Launch + iteration

Rollout pattern (typical):

Start with one inbound channel (e.g., web form) and a strict schema
Add additional channels (email, chat, call summaries) once the schema and routing rules are stable
Weekly review: measure correction rate and iterate on extraction rules + follow-up questions

Operational readiness:

Monitoring: time-to-first-response, routing failures, dedupe collisions, correction rate
Human-in-the-loop: low-confidence fields flagged for review rather than silently written

Measurement

Success metrics (examples):

Faster first-response time
Reduced manual data entry
Lower duplicate rate in CRM
Higher conversion from inquiry → qualified meeting

Metric placeholders we’ll define with you:

Target reduction in manual field entry: set after baseline (by field coverage)
Target reduction in duplicate creation: set after baseline (dedupe rules + data quality)
Target improvement in response-time SLA: set after baseline (by inquiry type)

Outcome (MVP-safe)

The intake assistant improved data consistency and routing speed for standard inquiries, while keeping humans in control for ambiguous or high-value leads.

If you want this pattern

Ideal if your inbound inquiries are unstructured and your CRM is a bottleneck.
Works best when sales agrees on a shared schema and routing rules.

CTA: Let's talk