IQ Sample work
Real deliverables, not promises.
Anonymized case studies across GTM strategy, revenue systems, lead generation, websites and AI automation. Client names withheld; results shared with permission.
GTM Strategy & Diagnostic
Built using The IQ Method: Diagnose → Recommend → Execute
Case Study · B2B AI SaaS · United States
Series A AI startup reposition — pipeline tripled in 90 days
Before
8%
Demo → proposal rate
→
Meetings booked
3×
Qualified pipeline in 90 days
→
Pipeline created
$680K
Qualified opportunity value
→
ARR closed
$420K
New ARR attributed in Q1
The situation
An AI SaaS startup (Series A, $3.2M raised) had a strong product but was targeting too broad an ICP — pitching to anyone in "operations." Demo-to-proposal was under 8%. Founder spending 60% of time on unqualified calls.
Trigger: VP of Sales hired → needed a clear GTM system before scaling headcount.
Trigger: VP of Sales hired → needed a clear GTM system before scaling headcount.
What we did
Diagnose: Analyzed 3 months of lost deals, interviewed 6 customers, scored 200 leads.
Recommend: Repositioned ICP to RevOps leads at $10M–$80M SaaS. Rewrote value prop.
Execute: Signal-based outbound engine + discovery framework. Sequences live in week 2.
Result: 31% demo→proposal rate by week 6. VP now has a repeatable playbook.
Recommend: Repositioned ICP to RevOps leads at $10M–$80M SaaS. Rewrote value prop.
Execute: Signal-based outbound engine + discovery framework. Sequences live in week 2.
Result: 31% demo→proposal rate by week 6. VP now has a repeatable playbook.
"We were spraying and praying before. Within 6 weeks we had a clear process and our conversion rate jumped from 8% to 31%. The diagnostic alone was worth the entire engagement fee."
Sample Deliverable — GTM Revenue Diagnostic (Redacted)
Step 02: DiagnoseRedacted
| GTM Area | Finding | Status | Priority Action |
|---|---|---|---|
| ICP Definition | Too broad — targeting "ops teams" across 12 verticals | Critical | Narrow to RevOps at $10M–$80M SaaS |
| Value Proposition | Feature-led, not outcome-led — no ROI framing | Critical | Rewrite around "hours saved + revenue impact" |
| Outbound Motion | Generic sequences, 0.4% reply rate | Weak | Signal-based personalization framework |
| Discovery Process | No MEDDIC qualification — losing deals late | Weak | Install 6-question discovery framework |
| Pipeline Velocity | Avg 94-day cycle, 8% demo → proposal | Critical | Mutual action plan + proposal template |
| Content / Social Proof | 3 case studies, all under NDA — no public proof | Weak | ROI-based anonymized one-pager for deck |
| CRM Hygiene | Deals stuck in "Demo" stage for 60+ days | Fixable | Stage definitions + weekly pipeline review |
Revenue Engine Build
Built using The IQ Method: Diagnose → Recommend → Execute
Case Study · SaaS Platform · United Kingdom
Built full outbound sales engine — $0 to $380K pipeline in 6 weeks
Before
0
Outbound capability
→
Meetings booked
12
Discovery calls in week 1 live
→
Pipeline created
$380K
Qualified pipeline by week 6
→
Deals progressed
2
In proposal stage — week 6
The situation
A B2B SaaS platform (HR tech, UK-based) was 100% inbound. With a new funding round, the board demanded a repeatable outbound engine. No sales team, no playbook, no CRM.
Trigger: New board pressure + 3-month runway before headcount freeze.
Trigger: New board pressure + 3-month runway before headcount freeze.
What we built
Diagnose: Win/loss audit of 18 inbound deals; found ICP signal patterns.
Recommend: HubSpot CRM setup, ICP scoring model, 5-touch email+LinkedIn sequence, objection guide.
Execute: Sequences live week 2. 12 discovery calls booked week 4.
Result: First SDR hire productive on day 3 using the playbook.
Recommend: HubSpot CRM setup, ICP scoring model, 5-touch email+LinkedIn sequence, objection guide.
Execute: Sequences live week 2. 12 discovery calls booked week 4.
Result: First SDR hire productive on day 3 using the playbook.
"We went from zero outbound capability to 12 discovery calls in the first week of going live. The playbook was detailed enough that our first SDR hire was fully productive on day 3. Best ROI we've had on any external partner."
Sample Deliverable — 5-Touch Outbound Sequence
Step 06: ExecuteTemplate
Day 1 · Email 1
Quick question, [First Name]
Noticed [Company] just [hiring signal / funding news]. Most HR leads at your stage hit the same wall — onboarding takes 3× longer than it should. Worth a 15-min call to show you how we cut that in half?
Day 3 · LinkedIn DM
Connection + value drop
Hi [Name] — sent you a note by email. Dropped a quick case study — [Anonymous company] cut onboarding from 14 days to 4 after switching. Happy to share the full breakdown if useful.
Day 6 · Email 2 — Proof
How [Industry peer] cut onboarding time by 70%
Quick one: anonymized case study from a UK HR SaaS team similar to yours — same headcount, same pain. They went from 14-day to 4-day onboarding in 6 weeks. Open to a quick look?
Day 10 · Email 3 — Breakup
Last note from me, [First Name]
Not going to keep following up after this — I know the inbox is full. If it ever becomes a priority, just reply "yes" and I'll send a calendar link. Here's what we'd look at: [3-bullet value list].
Lead Generation
Built using The IQ Method: signal-based targeting in Step 06, Execute
Case Study · B2B SaaS · UAE + India · 4-year track record
800K+ leads generated across campaigns — $60M+ in client revenue attributed
Leads generated
800K+
Across 50+ campaigns
→
Avg reply rate
4.2%
LinkedIn (industry avg 0.8%)
→
Cost per meeting
$18
Via LinkedIn outbound
→
Revenue attributed
$60M+
For clients across 4 years
Approach
Across 50+ clients in B2B SaaS, edtech, HR tech, and fintech — built trigger-based lead lists using funding signals, hiring intent, and tech stack data. Combined LinkedIn outbound with personalized cold email and retargeting.
For each client: ICP list build → signal scoring → multi-touch sequence → weekly reporting dashboard.
For each client: ICP list build → signal scoring → multi-touch sequence → weekly reporting dashboard.
Channels & tools
LinkedIn Sales Navigator · Apollo.io · Lemlist · Clay · Google Ads · Meta Ads.
Internal benchmark: as Business Consultant at Disney+ Hotstar, generated $4.5M in advertising revenue in six months, including $3M within three months.
Internal benchmark: as Business Consultant at Disney+ Hotstar, generated $4.5M in advertising revenue in six months, including $3M within three months.
"We handed over our ICP and Anand's team came back with a 400-lead list that was more targeted than anything we'd built ourselves. The reply rate on the first sequence was 3.8% — we'd never seen anything above 1% before."
Sample — Lead Channel Performance (Blended avg. across campaigns)
Aggregate Data
Website Design & Build
Conversion-first build — every site goes through Step 02, Diagnose (CRO audit) before design begins
Case Study · AI SaaS · UAE
Conversion-optimized SaaS landing page — 2.4% → 8.7% demo request rate
Before: bounce rate
74%
Visitors leaving without action
→
Load time
9.1s → 2.4s
Page speed improvement
→
Time on page
3.6×
Lift after redesign
→
Demo rate
8.7%
Was 2.4% — 3.6× lift
The situation
UAE-based AI automation startup had a feature-heavy website that explained what the product does — but not why a buyer should care. High bounce rate (74%), low demo conversion, no social proof above the fold.
Tools: Webflow, Figma, Hotjar, GA4.
Tools: Webflow, Figma, Hotjar, GA4.
What changed
Rebuilt hero around a pain-first hook. Added ROI-led sub-headline, client logo strip, 3 outcome-led use-case sections, and sticky CTA bar. Reduced page weight by 62%.
Platforms we build on: Webflow · Framer · Next.js · custom HTML/CSS (iqgrowthlabs.com is our own live example).
Platforms we build on: Webflow · Framer · Next.js · custom HTML/CSS (iqgrowthlabs.com is our own live example).
"Our old site looked fine but wasn't converting. The team did a proper CRO audit first, then redesigned around the buyer's pain — not our features. Demo rate went from 2.4% to 8.7% in the first month. That's pipeline, not just traffic."
Sample — SaaS Landing Page Hero (Live Preview)
Built by IQ Growth Labs
DealIQ
Product
Integrations
Pricing
Blog
Sign in
✦ Revenue intelligence platform
Stop guessing.
Start selling to buyers who are ready.
DealIQ surfaces real buying signals — funding rounds, exec hires, job postings — and turns them into account briefs your reps can act on in under 3 minutes.
🟢 Signal detected — Acme Corp
P1 — Act now
Hired VP Sales 8 days ago · Series B closed $14M · 3 open AE roles
📊 Novo Metrics Inc
P2 — Follow up
Expanding into EMEA · Job postings ↑ 42% · No incumbent vendor detected
⏳ Brio Health
P3 — Monitor
Last signal: 18 days ago · Board change flagged · Watch for RFP signal
Used by revenue teams at
Fintech SaaS
HR Tech
PropTech
AI Startup
B2B Marketplace
How DealIQ works
Signal-based targeting
Funding rounds, exec hires, job spikes — real triggers pulled from 40+ live sources, not static export lists.
3-min account briefs
AI-generated, source-cited, fact vs hypothesis. Everything a rep needs before the first call, in one card.
Priority scoring engine
P1–P4 tiering based on ICP fit, signal recency, and deal stage. Your reps always work the hottest accounts first.
CRM sync & routing
One-click push to HubSpot, Salesforce, or Notion. Signal freshness tracked automatically — no stale records.
What revenue teams say
★★★★★
"Replaced 3 hours of daily research. My reps now walk into calls actually prepared."
VP Sales · Series B SaaS · Austin TX
★★★★★
"The P1 signal queue alone booked us 11 meetings in the first month."
Head of Growth · Fintech · London
★★★★★
"Finally — a tool that tells my team who to call and why, not just a list of names."
Founder & CEO · HR Tech · Singapore
See DealIQ with your own ICP in 15 minutes.
We'll run your criteria through the engine live — no slides, no fluff.
Design approach: dark-first SaaS aesthetic · Webflow / Framer build · Figma prototype delivered first · GA4 + Hotjar integrated on launch · mobile-responsive from day 1.
AI Agents & Automation
Flagship build: Opportunity Intelligence Engine — Step 06, Execute (continuous signal enrichment)
Case Study · B2B Consulting · India · Internal + Client Builds
AI research agent — 30-min lead research reduced to under 3 min per company
Manual research time
30 min
Per company before
→
With agent
3 min
Per company — 90% reduction
→
Agents built
12+
For clients across use cases
→
Signal coverage
P1–P4
Priority tiers per lead
The agent
Built the Opportunity Intelligence Engine — an AI research agent that finds companies with current, evidence-backed reasons to need consulting. Not a cold email bot. A research intelligence layer.
Monitors: funding events, hiring signals, tech stack changes, exec moves, social triggers. Outputs a company-level opportunity brief — fact vs hypothesis separated.
Monitors: funding events, hiring signals, tech stack changes, exec moves, social triggers. Outputs a company-level opportunity brief — fact vs hypothesis separated.
Stack
n8n (workflow automation) · Apollo.io (discovery) · GPT-4o (evidence enrichment) · Notion/Google Sheets (output) · Custom HTML dashboard (queue + brief view).
12+ agents built across: lead research · content repurposing · CRM enrichment · invoice processing · support triage.
12+ agents built across: lead research · content repurposing · CRM enrichment · invoice processing · support triage.
"We were doing 30 minutes of research per prospect before a call. The agent does it in 3 minutes and the brief is better than what we were producing manually. We've now built 3 more automations on top of the same stack."
Sample — Multi-Agent Revenue Intelligence System (n8n + Claude + Apollo)
Step 06: ExecuteProduction BuildAgentic Loop
Orchestrator → 4 Specialized Subagents → Memory → Human Checkpoint → Output
Layer 0 — Triggers
⏱
Schedule
Daily 8 AM UTC
+
🔔
Webhook
CRM deal stage change
+
📧
Email parser
Inbound intent signal
→
🧠
Orchestrator Agent
Claude 3.5 · routes + delegates
Layer 1 — Parallel Subagents (spawned by orchestrator)
🔍 Research Agent
Apollo.io search
LinkedIn scrape
News RSS pull
Job board scan
LinkedIn scrape
News RSS pull
Job board scan
Tool: apollo_search
✦ Enrichment Agent
GPT-4o evidence brief
Fact vs hypothesis
Signal recency check
Confidence scoring
Fact vs hypothesis
Signal recency check
Confidence scoring
Tool: claude_tool_use
📊 Scoring Agent
ICP fit model
P1–P4 priority tier
Deal velocity calc
Competitor flag
P1–P4 priority tier
Deal velocity calc
Competitor flag
Tool: score_engine
✍ Outreach Agent
Personalized first line
Subject line variants
Follow-up sequence
Channel selection
Subject line variants
Follow-up sequence
Channel selection
Tool: claude_write
Layer 2 — Memory, State & Error Handling
🗄
Vector memory
Past briefs · seen signals
↔
🔄
Dedup check
Skip if seen <30 days
↔
⚠
Retry logic
3× on API fail · backoff
↔
🪵
Audit log
Every run · Airtable
Layer 3 — Human-in-the-Loop Checkpoint
📋
Brief review queue
Slack notification · P1 only
→
👤
Human approval
Approve / Edit / Skip
→
↙↘
Route decision
Approved? → send now
Skipped? → re-queue
Skipped? → re-queue
Layer 4 — Output Routing
📬
Apollo sequence
Auto-enroll P1 leads
+
🗂
HubSpot / Notion
Brief card + deal created
+
📊
Dashboard update
Signal board refreshed
+
🔁
Feedback loop
Reply data → retrain scorer
⚙ Stack
n8n · Claude API · Apollo.io · GPT-4o · Pinecone (memory) · Airtable (log) · HubSpot CRM
📈 Output metrics
80 accounts/day · 3-min brief · P1–P4 scored · 0 human research hours · $18 cost/meeting booked
🛡 Guardrails
Dedup window (30 days) · retry backoff · human gate on P1 · audit trail · confidence threshold > 0.72
Fractional GTM
Full IQ Method embedded: Frame → Diagnose → Quantify → Recommend → Roadmap → Execute — across a 6-month engagement
Case Study · B2B SaaS · Singapore → Global
6-month fractional engagement — from $0 ARR to $1.2M pipeline
Meetings booked
28
Discovery calls over 6 months
→
Pipeline created
$1.2M
Total qualified pipeline
→
ARR closed
$180K
Signed in months 4–6
→
Markets entered
3
SG · US · UAE
The engagement
Seed-stage proptech SaaS (Singapore) needed a head of growth without the $180K+ VP salary cost. Brought in as fractional GTM lead for 6 months, 20 hrs/week. Owned outbound, content, partnerships, and the sales process end-to-end.
The IQ Method in action
Month 1 — Diagnose: GTM audit, ICP finalization, positioning rewrite.
Month 2 — Recommend + Execute: Outbound live (US + UAE), 8 discovery calls booked.
Month 3–4 — Execute: 3 deals in negotiation. First $30K close.
Month 5–6 — Scale: Partnership pipeline. SDR onboarded. Full playbook handed over.
Month 2 — Recommend + Execute: Outbound live (US + UAE), 8 discovery calls booked.
Month 3–4 — Execute: 3 deals in negotiation. First $30K close.
Month 5–6 — Scale: Partnership pipeline. SDR onboarded. Full playbook handed over.
"We got the output of a full-time VP of Growth for a fraction of the cost — and a complete playbook we own at the end of it. By month 3 we had real deals in negotiation. We would not have scaled into the US and UAE without this model."
Sample — Fractional GTM 6-Month Engagement Map
Full IQ MethodEngagement Map
Month 1 · Diagnose
GTM audit + ICP design
Win/loss audit · ICP refinement · positioning rewrite · CRM setup · outbound strategy
Month 2 · Recommend + Execute
Sequences live + first meetings
US + UAE targets · weekly reporting · LinkedIn + email sequences
→ 8 discovery calls booked
Month 3–4 · Execute
Pipeline + first close
3 deals in negotiation · referral playbook activated
→ $30K ARR closed
Month 5–6 · Scale
Scale + handover
Partnership pipeline · SDR hired + onboarded · full playbook documented
→ $1.2M total pipeline · $180K ARR signed