We supported a fast-growing customer support platform in redefining its AI strategy and product roadmap to fully leverage the emerging AI Agents paradigm. The engagement focused on aligning business goals, technology capabilities, and near-term delivery, while laying foundations for scalable, partner-friendly AI architecture.
Meet our client
Client:
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Market:
Technology:
In a Nutshell
Client’s Challenge
The client recognized that their industry was on the verge of disruption driven by AI agents and generative AI. Rather than reacting to emerging competitors, they wanted to build a sustainable competitive advantage by embedding AI deeply into their core product. The challenge was to identify where AI would create real, defensible value and prioritize investments without slowing down the existing business.
Our Solution
In a 3-week AI advisory project, we audited the existing platform, aligned business and tech stakeholders, validated use cases with customers, and designed a future-proof architecture with a prioritized, sprint-ready roadmap.
Client’s Benefits
- Clear, updated AI roadmap and priorities
- Implementation-ready architecture for AI agents
- Faster time-to-market with reduced delivery risk – MVP ready in just 3 months
- Strong foundation for scalable, partner-friendly growth
A Deep Dive
Client
The client is a customer support platform focused on helping SMB and mid-market companies streamline and scale their customer service operations. The product combines AI-powered automation with access to human consultants, positioning the platform as a hybrid solution for both efficiency and quality.
The company operates in the customer support software space and is a recognized player in its regional market, with ambitions to scale internationally following a recent acquisition by a private equity fund.
Challenge
Business Challenge
The client is entering the AI Agents era and began a strategic redesign of its product to fully benefit from recent advances in generative AI and agentic systems.
The platform already operates a successful multi-channel customer support solution and has foundational AI capabilities, including transcription, summarization, and access to customers’ internal knowledge bases. This creates strong preconditions for building advanced AI agents that support human consultants and fully autonomous customer support agents, handling standard issues and offloading human consultants’ capabilities.
However, the client had a strong product vision but needed deeper AI expertise to:
- Design AI agents that would actually work and deliver a superior user experience
- Design an architecture that would be efficient, vendor-independent, and future-proof in a rapidly evolving AI environment
- Prepare a roadmap and delivery plan that would deliver value quickly and enable incremental product development
Technology Challenge
- Designing an architecture that supports agentic workflows without locking into a single vendor
- Balancing near-term MVP delivery with long-term scalability
- Integrating AI agents into an existing production platform without disrupting current customers
Solution
Our Approach
We delivered a 3-week AI advisory engagement combining strategic, product, and technical perspectives:
- Architecture & technology audit to assess current capabilities, constraints, and gaps
- Design Thinking workshop with Technology, Product, Sales, and Operations to define product vision, value propositions, and user stories
- Interviews with selected end customers to validate assumptions and understand real-world expectations
- Detailed user story definition based on workshop outputs and customer feedback
- Technical architecture design for the MVP phase, including recommended infrastructure and technology stack
- Post-MVP vision (2026) outlining how the solution can evolve into a mature, agent-driven platform
The resulting architecture explicitly supports future integrations with multiple external partners and AI vendors.
Process
Steps Taken
- Audited existing system architecture, data flows, and AI components
- Aligned business and technology stakeholders around a shared AI product vision
- Identified and prioritized high-value AI agent use cases
- Translated product vision into concrete user stories and system components
- Designed an MVP-ready technical architecture with a clear evolution path
Expertise Involved
- 2× Tech Leads – technical assessment, architecture design
- 2× Business Consultants – business alignment, value prioritization
- Ad-hoc domain experts – supporting specific AI and product topics
- Delivery Manager – coordination, scope control, and stakeholder communication
Outcome
Qualitative Results
- Immediate product improvements driven by quick-win recommendations, some implemented within hours
- Well-structured product architecture, split into logical components, deliverable within single development sprints
- Predictable and communicable development roadmap, supporting discussions with customers and investors
Quantitative Results
- The advisory phase led to tangible product enhancements before development formally began
- Reduced implementation risk and avoided months of potential rework
Summary
By combining strategic AI advisory with hands-on technical design, we helped the client confidently transition toward an AI-agent-driven product vision. The engagement delivered clarity, alignment, and execution readiness, laying strong foundations for rapid delivery today and scalable growth tomorrow.
