Results
Case Studies
Real companies. Real systems. Real pipeline. See how organisations are using the AI Growth Engine to build predictable revenue without expanding headcount.
AI Growth Engine
Colab Cohorts Generates More Pipeline in One Month Than the Previous Two Quarters Combined
Colab Cohorts • Product Management Capability Development • AI Product Enablement
Client Overview
Colab Cohorts helps product teams at companies such as Visa, Xero, and Gojek embed AI into how they plan, build, and deliver products. Their programs combine AI readiness assessments, product maturity reviews, and hands-on cohort learning led by experienced product leaders.
Teams working with Colab have compressed product discovery timelines from months to weeks by integrating AI directly into their product workflows.
The Challenge
Despite strong demand for their capability programs, Colab Cohorts wanted to generate more consistent outbound pipeline with senior product leaders. Prospecting was largely manual and time-intensive. Researching accounts, building prospect lists, writing personalised outreach, and managing follow-ups. This limited the number of conversations the team could generate with Heads of Product and Product Directors.
The Strategy
David & Goliath deployed the AI Growth Engine, a system that combines AI-powered prospecting with human-led sales execution. The goal was to build a repeatable outbound system that identifies high-fit companies, generates personalised outreach at scale, and creates qualified conversations with product leaders. Instead of expanding SDR headcount, Colab deployed an AI-powered outbound motion supported by a Fractional GTM Engineer.
Implementation
The AI Growth Engine was deployed in stages. Target accounts were identified using ICP filters including company size, product maturity, and product leadership roles. AI-assisted research surfaced personalisation signals including product initiatives, hiring activity, and digital transformation efforts.
Multi-touch outbound sequences were launched across email and LinkedIn. When prospects engaged, the Fractional GTM Engineer conducted follow-up calls, qualification conversations, and meeting booking. Automation handled research and outreach while humans focused on high-value conversations.
Results
Senior product leaders across target companies began engaging with the Colab Cohorts team. The company significantly increased pipeline generation without expanding their sales team.
“Within the first month, we generated more outbound-sourced qualified meetings than the team had in the entire two previous quarters. Alongside tightening our sales process, Josh helped build several hundred thousand dollars in pipeline with key decision-makers during the pilot.”
Adam
CEO & Co-Founder, Colab Cohorts
Strategic Insight
Many companies attempt to scale pipeline by hiring additional SDRs. Colab instead deployed an AI-powered outbound system that automates research, targeting, and outreach while allowing sales professionals to focus on conversations. The result was a repeatable revenue engine capable of generating qualified pipeline without proportional headcount growth.
AI Growth Engine
Oligo Opens APAC Market and Converts Senior Security Leaders into Pipeline Within Weeks
Oligo • Cloud Runtime Security • Application & AI Runtime Protection
Client Overview
Oligo helps engineering and security teams secure modern cloud workloads by analysing how code, libraries, and AI systems behave at runtime, rather than relying on static vulnerability scans. Their platform focuses on Application & AI Detection and Response, allowing security teams to observe code execution in production and identify exploitable vulnerabilities, malicious behaviour, and unsafe AI activity.
This runtime approach allows security teams to prioritise real risk, block exploits as they occur, and gain visibility into AI systems operating inside modern applications.
The Challenge
Oligo was expanding internationally and wanted to generate stronger outbound pipeline in the APAC region. The company needed to connect with security leaders, platform engineering teams, and cloud infrastructure decision-makers. However, outbound outreach lacked a structured system for identifying high-fit companies and engaging decision-makers consistently.
The Strategy
David & Goliath designed and deployed a targeted outbound motion using the AI Growth Engine. The goal was to quickly generate market signal and open conversations with security leaders in APAC. This involved combining signal-based account targeting, personalised outbound messaging, and structured multi-touch outreach to convert conversations with senior stakeholders into pipeline opportunities.
Implementation
Target accounts were prioritised based on cloud infrastructure maturity, AI adoption, and engineering team structure. Outbound campaigns were launched using personalised messaging referencing runtime security challenges and emerging AI security risks.
Multi-touch outreach across email and LinkedIn enabled the team to engage senior security and engineering leaders. This structured outbound motion generated early traction with high-value target accounts.
Results
Oligo began engaging senior security and engineering leaders across the APAC region. Outbound conversations were converted into meaningful pipeline, and the team gained early validation of demand in a new geographic market.
“David & Goliath helped us quickly establish a focused outbound motion that delivered immediate signal in the APAC market for the first time. Within weeks, we were engaging senior stakeholders and converting those conversations into meaningful pipeline.”
Brandon
VP of International Sales, Oligo
Strategic Insight
Market expansion often fails when outbound outreach is inconsistent or unstructured. By combining AI-driven targeting with structured outreach and human follow-up, companies can generate early market signal and pipeline faster. For Oligo, this approach accelerated engagement with security leaders and created meaningful commercial opportunities in a new region.
Employee Amplification Systems
How a Property Management Firm Used an AI Operations System to Handle Tenant Repair Requests Faster Without Adding Staff
Commercial Property Management • Tenant Operations • Multi-Property Portfolio
Client Overview
The client is a commercial property management firm responsible for managing tenant relationships, building operations, and maintenance requests across multiple commercial properties.
Like many property management teams, the firm receives a large volume of inbound repair requests from tenants. These requests typically arrive through a shared email inbox and require property managers to review the message, gather relevant information, and coordinate repairs with contractors. The firm wanted to improve response speed and operational efficiency without expanding their operations team.
The Challenge
The property management team handled tenant repair requests manually through a shared inbox. Each request required reviewing tenant messages, identifying the relevant property or unit, checking lease and property information, drafting responses, and creating or updating repair work orders.
This created delays in responding to tenants, incomplete information in incoming emails, time spent retrieving property details, and growing administrative workload. As the portfolio grew, the team needed a way to handle more inbound requests without increasing headcount.
The Strategy
David & Goliath designed an Employee Amplification System to support the property management team. Rather than replacing employees, the goal was to augment their workflow by automating repetitive operational tasks.
The system was designed to monitor incoming repair requests, classify them automatically, retrieve missing property or lease information, draft appropriate responses, and assist with repair work order creation. Human review remained in place for all operational decisions.
Implementation
David & Goliath deployed a Proof of Concept AI operations system integrated with the client's existing workflows. The system was configured to monitor a central repairs inbox and analyse incoming tenant messages.
Using AI classification and retrieval-augmented generation, the system could read and categorise repair requests, identify missing information such as property address or unit, retrieve relevant details from the property and lease database, draft suggested response messages, and assist with repair work order preparation.
Property managers review and approve all AI-generated outputs before sending responses or initiating repairs. This approach ensures AI accelerates workflows while humans retain operational control.
Results
By automating classification, information retrieval, and response drafting, the system allows property managers to focus on coordination and tenant relationships rather than administrative tasks.
Strategic Insight
Many service businesses attempt to scale operations by hiring additional administrative staff. Employee Amplification Systems take a different approach. By embedding AI into operational workflows, companies can increase team capacity without increasing headcount. For property management firms handling large volumes of tenant communication and maintenance coordination, this approach can significantly improve responsiveness and operational efficiency while keeping teams lean.
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