Banking
DevSecOps Platform + API Management
Case study — Tezo Digital · Director, Inside Sales
Building the GTM Engine
How I approached enterprise pipeline generation, sales execution, team leadership and GTM development at Tezo.
Context
At Tezo, I led and strengthened inside-sales execution across AI/ML, Data & Analytics and Product Engineering, with enterprise focus across BFSI, Manufacturing and other B2B markets.
Expanding GTM Across Markets
Helped expand Tezo's market reach by developing and supporting sales and GTM efforts across the US, UK, Middle East, Australia and the Caribbean.
14-Month Scoreboard
155Qualified meetings
85SQLsAgainst a 72 target
$4.2MPipeline
$840KRevenue — 1st SOW
Technology breadth
Selected Projects
Technology-led projects I helped bring into Tezo across Banking, Insurance and Retail.
Insurance
Data Warehouse + Prompt Engineering
Retail
Demand Forecasting + Dynamic Pricing
The build
What I Built
ICP & Segmentation
Defined target account characteristics and segmented prospects around industry, company profile, business needs and buying triggers.
ABM
Developed targeted account-based motions rather than relying purely on activity volume.
Qualification
Redesigned qualification and operating processes around measurable opportunity criteria.
AI-Enabled Sales Workflows
Made AI an operating layer across research, account identification, personalisation and prospecting, supported by CRM, sales intelligence and automation.
Team Leadership
Hired, coached and led representatives using KPIs, incentives, cadence and accountability.
GTM Expansion
Expanded GTM reach through strategic initiatives: ABM, events, Microsoft co-sell motions, ecosystem development and AI adoption across sales workflows.
Ecosystem development
Creating the Anthropic Opportunity
Identified the strategic value of expanding Tezo's ecosystem capabilities through Anthropic, presented the opportunity to senior leadership, secured approval to pursue it, initiated the partnership process, and transitioned execution to the AE team.
- 01
Identify
Saw strategic value in expanding Tezo's ecosystem capabilities.
- 02
Build the case
Framed the opportunity for senior leadership.
- 03
Secure approval
Obtained approval to pursue it.
- 04
Initiate
Started the partnership process.
- 05
Transition
Handed execution to the AE team.
Signature build
I don't just use AI to analyze sales data. I build systems that bring it together.
AI-Powered Inside Sales Intelligence
Built an AI-native sales intelligence system that consolidates CRM, prospecting, outreach and internal data into a unified view of team activity, meetings, SQLs, conversion, campaigns and pipeline performance.
Built with Claude · HubSpot · SharePoint/Excel · ZoomInfo · Sales stack
- CRM + Internal Data
- Prospecting Data
- Outreach Data
- Campaign Data
AI Sales Intelligence
- Activity
- Meetings
- SQLs
- Conversion
- Campaigns
- Pipeline
Architecture shown; underlying production data is confidential.
What I've Built With AI
AI Sales Intelligence
Inside Sales operating system
Ask Abhir
AI-powered recruiter career assistant
I use AI to build business systems, not just to write prompts.
AI-enabled prospecting
AI as an Operating Layer in the Sales Motion
AI was not treated as another tool in the stack. It was built into how the team researched accounts, identified ICP fit, personalised outreach and executed GTM — supported by CRM, sales intelligence and automation.
- AI-Assisted Research
- AI used for account and prospect research, summarising company context, business priorities and buying triggers before outreach.
- AI in ICP & Account Identification
- AI-supported analysis to identify and prioritise accounts that matched defined ICP criteria.
- AI-Assisted Personalisation
- AI used to draft and refine account-specific messaging so outreach reflected the buyer's context rather than a generic template.
- AI-Enabled Workflow Automation
- AI and automation embedded into prospecting workflows and operating cadence to reduce manual steps and increase productivity.
- Supporting Sales Systems
- CRM, sales intelligence and data enrichment supplied the structured account, contact and activity data the AI layer worked on top of.
The takeaway
At Tezo, the work was not simply about increasing sales activity. It was about building a more disciplined system for identifying accounts, creating qualified opportunities, developing the team and converting technology capabilities into commercial outcomes.