Problem
Digital marketing agencies excel at strategy and execution but drown in repetitive work: qualifying leads from inquiry emails, writing campaign briefs from client requirements, scheduling social posts, enriching CRM data, and sending automated nurture sequences. Each task requires judgment but follows patterns.
MarketingHub.ca (Dave Groups Inc, Ontario) operated efficiently but wanted to scale without proportional headcount increase. The challenge: LLM tools were emerging but integrating them into existing stacks (HubSpot, Zapier, Monday.com) required custom glue code, and maintaining such integrations was effort-intensive.
The goal: Make LLM-powered automation accessible to non-technical team members without vendor lock-in.
Data Preparation
We mapped the entire agency workflow: documented campaign templates (SEO, paid ads, content marketing), email sequences (cold outreach, nurture, sales), and decision criteria (lead scoring, deal qualification).
From HubSpot, we extracted:
- 500+ past campaigns (client name, vertical, objectives, results)
- Lead scoring rubrics (qualification criteria per vertical)
- Email templates and response rates
We created prompt libraries—reusable templates for common tasks:
- “Summarize this lead inquiry email in 2 sentences. Route to: [SEO / Paid Ads / Content]. Scoring: [High / Med / Low]. Reason:”
- “Draft an email response to this lead, matching our tone (helpful, not salesy)”
- “Create a campaign brief for a [vertical] client with budget $[amount]. Suggest strategy for [goal]”
Evaluation data: human review of 100 LLM-generated outputs per task type. Measured accuracy, tone-matching, strategic relevance.
Models Evaluated
| Model |
Result |
Use Case |
| Mistral 7B |
Kept |
Routine lead qualification, email drafts (60% of workflows) |
| Llama 3.1 8B |
Kept |
Complex strategy briefs, client proposals (30% of workflows) |
| GPT-4 |
Kept |
High-stakes strategy work, novel client verticals (10% of workflows) |
| RAG |
Kept |
Ground all outputs in client-specific data for consistency |
| Fine-tuning (LoRA) |
Cut |
Prompt engineering + few-shot + RAG achieved 95% of fine-tuning quality without overhead |
| Custom NLP |
Cut |
LLMs generalize better; custom models brittle across client verticals |
Architecture
A hub-and-spoke automation system:
[Trigger: new lead email]
v
Zapier -> Lambda (webhook)
v
Retrieve context from HubSpot (past client data)
v
Build RAG query (retrieve 3 similar past leads)
v
Prompt LLM (Mistral or Llama, conditional on complexity)
<- [Lead email] + [Client context] + [Past examples]
v
Generate output (lead summary, routing, score)
v
Post to Slack (human review/approval)
v
On approval: sync back to HubSpot + trigger next workflow
Intelligence routing: Simple tasks (lead qualification, email drafts) -> Mistral 7B (~0.5¢ per call). Complex tasks (strategy briefs for new verticals) -> Llama 3.1 (~2¢) or GPT-4 (~8¢) if high confidence needed.
RAG layer: Every LLM call retrieves 3 semantically similar past campaigns using Sentence Transformers embeddings. This grounds the model in MarketingHub’s voice and historical performance.
Production & Scale
Deployed as serverless AWS Lambda functions (scale to zero when idle; pay only for executions). Integrations span:
- HubSpot: Sync leads, update deal stages, enrich company data
- Zapier: Trigger workflows on form submissions, new email, etc.
- Monday.com: Auto-populate task details in project boards
- Slack: Human review checkpoints; final approvals
All LLM outputs logged for audit trail. Weekly human review (5% sample) ensures quality; high-scoring examples fed back into prompt library.
Key results:
- 40+ workflows automated across lead gen, email, strategy, CRM enrichment
- ~120 human hours saved per week (agency staff freed for higher-value work)
- Cost: ₹2–15 per workflow (vs ₹50–200 for manual work)
- Client satisfaction: 5.0 out of 5 on communication, quality, value
Client testimonial (from MarketingHub’s internal review):
“Thanks for the accurate work, keep it up! I highly recommend your service. I hope you’ll continue delivering the same excellent quality of work for me in the future.”
— onlineasset, 5.0 out of 5
Learnings
- Prompt engineering beats fine-tuning at this scale. Reusable templates + few-shot examples are faster to iterate than model training.
- Conditional model routing saves costs. 95% of tasks run on Mistral; only hard cases escalate to stronger models.
- Human-in-the-loop is essential for agency work. LLM outputs are starting points; client preferences and brand voice require human judgment.
- RAG enables consistency across 50+ client campaigns. Without it, LLM outputs felt generic.
Ongoing: Expanding automation to campaign performance analysis (weekly reporting), audience analysis (segmentation), and competitor tracking.