AI Marketing Platform
AI Outbound Sales & Marketing Platform
An outbound sales and marketing platform that manages leads and HR prospects, drafts AI outreach, routes it through an approval queue, and tracks campaigns — architected to grow into a full CRM.
AI-assisted
Drafting
100%
Review
CRM-ready
Architecture
Roadmap
Channels
Business challenge
Sales teams waste hours researching leads and writing repetitive outreach, while generic automation risks brand trust and compliance — no message should go out unreviewed.
What I built
I built lead and HR-prospect management, AI-drafted outreach grounded in enriched data, and an approval queue that requires human sign-off before send. Campaigns are tracked, and the architecture is CRM-ready with a multi-channel roadmap.
Why it matters
Outbound at scale only works if it's personalized and compliant. The platform had to make reps faster without ever letting AI send unreviewed messages, and leave clean seams for CRM growth.
How it's designed
Interface
How the intelligence flows
- 01Enrich — assemble verified signals per lead/prospect.
- 02Draft — AI-generated outreach grounded in enrichment data.
- 03Review — approval queue with edit-before-send.
- 04Track — campaign status and engagement.
How data is modeled
- PostgreSQL: leads, prospects, campaigns, messages and approvals.
- Approval state machine (draft → pending → approved → sent).
- Redis queue for generation and send scheduling.
- Send-history audit for compliance.
Key endpoints
/api/leadsCreate and enrich a lead or HR prospect./api/outreach/draftGenerate a grounded outreach draft./api/outreach/{id}/approveApprove a draft for sending./api/campaignsCampaign tracking and status.Technologies
Key features
- Lead and HR-prospect management.
- AI-drafted, grounded outreach.
- Approval queue with edit-before-send.
- Campaign tracking.
- CRM-ready, extensible architecture.
- Compliance-aware send history.
Challenges & trade-offs
Personalization without hallucination
Grounding generation in verified enrichment data kept messages accurate and on-brand.
Human approval as a hard gate
An approval workflow ensures no AI-generated message reaches a customer unreviewed.
Code snippet
async def draft_outreach(lead: Lead) -> Draft:
facts = await enrich(lead) # verified signals only
body = await llm.generate( # local LLM, grounded
persona=lead.persona, facts=facts,
)
return Draft(
lead_id=lead.id,
body=body,
status="pending_approval", # never auto-sends
grounded_on=[f.source for f in facts],
)How it ships
- Containerized services on a managed platform.
- Queue-backed generation to keep the UI responsive.
Security
- — Role-based approvals with full send-history audit.
- — Encrypted storage of lead and campaign data.
Scalability
- — Generation workers scale with campaign volume.
- — Caching of enrichment results to reduce repeat cost.