Speed and precision can make or break a deal in today’s high-stakes customer interactions. Field reps need information and verified, on-demand, and contextual answers. That’s where Generative AI (GenAI) steps in, not as a buzzword, but as a transformative force for sales enablement.
In a recent webinar, Joseph Jude (CTO, Net Solutions) sat down with Sanjaya Abheysinghe (CTO, Optisolve) to unpack a GenAI-powered solution that’s turning product complexity into sales clarity. The conversation was packed with real-world insight, practical architecture, and data-backed proof that GenAI can actually deliver business impact.
The Trigger: Sales Teams Drowning in Docs and Delays
The conversation focused on why this solution was built in the first place—and it wasn’t because AI was trending. The motivation stemmed from a recurring problem faced by field sales teams.
Joseph kicked things off with the problem:
“One of the common struggles of field sales reps is finding the right answer in the middle of a customer conversation.”
And Sanjaya confirmed that it wasn’t a GenAI trend that started them. It was pain.
“We didn’t get into GenAI because it was trendy. We saw customers wasting hours just to find a simple, verified answer. That was the trigger.”
Sales reps relied on PDFs, emails, and frantic SME calls. The solution? An intelligent assistant that could deliver instant, grounded, and verifiable responses, even to a photo or voice query.
Under the Hood: Not a Chatbot. A Modular AI Engine.
Once the problem was clear, the discussion shifted to the architecture and technical thinking behind the solution. Both leaders wanted to discuss how this wasn’t a “black box” solution but something intentionally designed for agility, trust, and control.
At the core is a RAG (retrieval-augmented generation) pipeline trained on proprietary SOPs, datasheets, and technical documentation. The system is built to be verifiable, privacy-safe, and modular.
“We didn’t build for OpenAI. We built an engine. If one model slows down, we can swap it without breaking the system.”
The architecture integrates vision models, vector databases, and structured prompting to handle multimodal inputs—text, images, and voice. This instantly allows field reps to get context-aware responses, boosting confidence and reducing support overhead.
QA in the GenAI Era: From Pass/Fail to Confidence Scoring
With the architecture covered, Joseph turned to a question that many enterprise leaders wrestle with: How do you ensure GenAI output is trustworthy? The answer lay in rethinking traditional quality assurance.
Sanjaya’s team built a hybrid framework:
- SMEs provided 100+ real-world questions
- Multiple LLMs (GPT-4, Claude, Gemini) were tested
- Responses were scored 1–5 on accuracy, tone, and verifiability
“We didn’t throw away our QA process. We hybridized it. We trained our team to move beyond binary checks and think like users.”
This mindset helped them tune prompts, iterate fast, and ensure response quality over hallucination.
Prompt Fluency = UX Superpower
During the conversation, Sanjaya shared one of the most overlooked aspects of successful GenAI adoption: prompt design as a user experience (UX) discipline.
“Prompt fluency is the new UX. We trained our team to think like writers, not coders.”
Sanjaya narrated an interesting incident when he even tested prompts by making bedtime stories for his child—a creative, context-rich way to validate personalization and tone. These experiments helped the team refine system behaviors that users would trust and enjoy.
Guardrails Before Features: Privacy & Governance at the Core
Before diving into flashy AI features, Sanjaya clarified that safety and governance had to come first. This section highlighted how they ensured enterprise-grade readiness from day one.
From masking PII to using the NIST AI framework, Sanjaya’s team embedded safety into the architecture via logging, role-based access, and system-level behavior constraints.
“Guardrails weren’t an afterthought—they were part of day one design.”
This sets a strong foundation for trust, critical for adoption in mid-sized enterprises that handle sensitive customer or product data.
Early Wins: Data That Proves It Works
Theory and architecture are grand, but what about results? Joseph and Sanjaya shared early pilot data proving GenAI enablement is viable and impactful.
- 40% faster query resolution during field demos
- 50% higher content reuse across teams
- 25% improvement in rep confidence scores
All without changing the CRM or sales stack. It was augmentation, not disruption.
From Information to Action: What’s Next?
With proven results, Joseph steered the conversation to the future of GenAI-powered tools. Where does this go next?
Sanjaya hinted at the next evolution:
“We’re moving from answers to actions—automating reports, generating proposals, even triggering workflows.”
That’s not just support. That’s strategic enablement at scale.
Key Takeaways
If you’re short on time, here’s your playbook from this GenAI case study. These aren’t just technical lessons—they’re strategic moves for anyone building sales or product support systems today.
What You Should Be Doing Now:
- Start with a real use case—sales friction, not shiny tools
- Build modularly—don’t tie yourself to one model or stack
- Treat prompt engineering as product design
- Use hybrid QA with SMEs in the loop
- Bake governance into GenAI systems from day one
- Shift from reactive answers to proactive automation
Ready to Bring AI-First Sales Enablement to Your Teams?
Whether you lead product, innovation, or sales operations, the message is clear: GenAI is not experimental anymore—it’s operational.
“AI is not here to replace developers. It’s here to amplify those who learn it first.”
You can catch the full webinar recording to learn more, or contact our AI enablement specialists to see how this solution could be tailored to your GTM stack.
