Scaled From 1 Brand to 5 Without Operational Chaos

30+ hours/week removed by designing AI-enabled operational systems, automation workflows, and scalable internal infrastructure

Client: Guru Labs

When I joined, the startup was operating with a single brand while its operational foundations were still evolving. Speed had been prioritised over structure, which worked initially but became fragile as complexity increased. I was brought in to design systems that could stabilise operations and support scalable growth. The company later expanded to five brands, with these systems supporting that scale.

Operational Impact

The systems reduced manual coordination and increased the team’s operational capacity.

Key impacts included:

  • ~25+ hours per week of social media publishing work automated across five brands

  • 10–20 minutes saved per sales call through centralised CRM data

  • 1–2 hours per week eliminated from manual reporting maintenance

  • Faster response times to inbound leads through real-time Slack notifications

  • Shared, real-time visibility into sales activity across the team

  • Consistent sales and onboarding experience across five internal brands

  • Systems capable of absorbing growth without becoming fragile

The Core Problem

Scaling was structurally constrained by:

  • Knowledge and information primarily lived in people's heads rather than in systems. This created inconsistent execution and made quality control dependent on specific individuals. As the company grew, this structure caused work to stall while teams waited for guidance, repeatedly pulling the founder into operational decisions and away from strategic priorities.

  • A significant portion of team time was spent on repetitive, manual tasks that could potentially be automated or systemised.

  • Operational information was scattered across multiple tools, documents, and platforms. This made it difficult for the team to quickly locate the information they needed, leading to frequent Slack questions and unnecessary time spent searching for answers.

  • Lead, customer, and reporting information existed across different tools and spreadsheets, requiring the team to manually reconcile information across systems. This made it difficult to keep sales activity organised and created additional administrative work.

  • An internal AI sales system existed, but the qualification and follow-up logic were not documented or structured into a reusable framework. This knowledge lived primarily in the Head of Sales and the founders head, which meant the team could not easily modify or build new AI sales workflows without their involvement.

  • Repeated questions flowed through Slack because knowledge and decision-making lived in people's heads and hadn’t yet been extracted into documented, reusable processes or systems.

My Approach

I began by mapping the operational journey end-to-end to create a clear picture of how work actually moved through the business. Rather than starting with optimisation, the first goal was operational clarity, documenting how work is currently executed across people, tools, and systems.

This involved mapping the full workflow to show:

  • How leads, customers, and information moved through the business

  • Who was responsible for each stage of the process

  • Where systems supported the workflow and where manual work filled the gaps

  • How tasks, hand-offs, and decisions flowed between teams

The goal was to create a shared operational view of the business, making it easier to understand how work was actually being executed.

From this map, it became easier to identify opportunities for improvement, including areas where processes could be standardised, manual work reduced, and systems introduced to support scale.

As the company later expanded to multiple brands, this operational mapping continued, allowing systems and workflows to evolve alongside the business.

Interventions were prioritised based on bottleneck severity and time leverage.

Selected Systems & Automation Architecture

1. AI-Driven Sales Conversation System

Outcome: Sales conversations scaled across five brands while preserving conversion quality and consistency.

Lead conversations were already partially automated through an in-house AI Instagram sales system, but the underlying sales logic existed only in the founder and head of sales’ head.

I worked directly with the head of sales to extract his sales methodology: qualification criteria, objection-handling, and conversation flow, then turned it into a structured, reusable prompt framework.

The system operated in defined states:

Sales Mode → Customer Mode

Once a lead converted, the system dynamically shifted prompts, tone guidelines, and onboarding logic, allowing AI to manage conversations end-to-end while preserving intent at scale.

We continuously analysed performance, tested variations, and refined the prompt logic, ultimately consolidating everything into a reusable Sales Playbook so that the team was equipped to manage and optimise conversions independently.

2. Automated Social Media Publishing System

Outcome: 3× posting frequency across five brands with no additional manual workload.

Social media content creation and publishing was consuming ~25 hours per week across multiple brands, necessary for visibility, but deemed as a low-leverage and non-revenue-generating activity.

I designed a fully automated, end-to-end social media publishing system using Make.com that:

  • Managed content creation and publishing across five Instagram brands

  • Supported multiple content formats (captions, images, videos, carousels)

  • Enforced brand-specific rules derived from existing branding documentation

  • Maintained tone, formatting, and consistency without ongoing oversight

Each brand operated from its own documented guidelines, tone, boundaries, formatting rules, and prompt instructions, allowing the system to scale output without diluting brand identity.

Once stable, I documented the workflows and created clear SOPs so the team could safely update prompts, introduce new formats, and extend the system without breaking it.

I also trained team members on how the system worked so they could maintain and evolve it independently.

3. End-to-End Sales & Operational Systems

Outcome: Faster response times, and real-time visibility across the entire sales pipeline.

I removed the need for manual monitoring and inconsistent reporting by implementing core systems that improved speed-to-lead and gave the team a clear, real-time view of what was happening.

Core Components Implemented

  • Implemented real-time Slack notifications for cold email replies, booked calls, and closed sales, enabling immediate team response without manual monitoring

  • Designed and implemented the company’s first HubSpot CRM, centralising lead and customer data into a single source of truth. This eliminated the need for the sales team to gather information from multiple documents before sales calls and allowed call notes and pipeline activity to be managed in one system, reducing call preparation time by an estimated 10–20 minutes per sales call while allowing notes and pipeline activity to be managed directly in the CRM.

  • Replaced manually maintained reporting spreadsheets with automated, real-time data feeds into a centralised reporting environment, eliminating ongoing manual upkeep and saving approximately 1–2 hours of manual reporting work per week while improving data reliability.

  • As the team adopted ClickUp for operational management, I collaborated with a ClickUp developer to embed live sales data directly into the platform, giving the entire team real-time visibility into sales activity.

  • Automated client onboarding workflows to eliminate repetitive setup work and ensure consistent data flow across systems.

Result: Information moved automatically between systems, giving the team real-time visibility without manual updates and reducing the founder’s involvement in routine coordination.

Additional AI Prototyping

Explored and prototyped additional AI-driven operational workflows, including:

  • AI-assisted proposal generation workflow designed to reduce the time required to create customised client proposals before the company later transitioned to a different sales model.

  • AI “Chief of Staff” concept intended to surface operational insights, assist leadership with coordination, and support decision-making. I started to lay out the initial groundwork towards its future implementation.

  • Planned the architecture for a future internal AI knowledge system designed to centralise SOPs, operational knowledge, quality checks, and decision frameworks, enabling both the team and future plans for AI agents to access this guidance and execute operational tasks more effectively.