Many companies face the same challenge: a great product that underperforms in trial conversion. In this case, a leading SaaS platform was losing valuable prospects during the trial phase, despite positive feedback on the product itself.
User research uncovered the root of the issue. Prospective customers were typically evaluating 5–10 competing tools in parallel, each with limited time to assess value. Long onboarding flows, scattered documentation, and video tutorials created friction. Users simply didn’t have the bandwidth to dig deep.
As a result, many trial users dropped off before reaching meaningful product value, often choosing competitors who offered a faster, more intuitive discovery experience.
To solve this, the company set out to build an AI assistant that could support trial users in real time, helping them discover the most relevant features and value — without having to watch lengthy videos or read through documentation.
The goal was to create a proof of concept (PoC) using a hosted LLM, deployed specifically to users already in trial. This assistant would:
Increase conversion rates from qualified trials
Answer product-specific questions instantly
Highlight relevant features based on user intent
Reduce reliance on support documentation and tutorials
Shorten time-to-value and improve the trial experience
- Prompt users with relevant next steps or actions at the right moment to guide them through setup and exploration
- Encourage license purchase when value is demonstrated through well-timed, contextual call-to-actions
Work with our architects to build AI assistants that guide users, surface value instantly, and drive real results from day one.
Building an AI assistant that delivers real-time, personalised onboarding requires more than just a language model. It demands a foundation that can handle ingestion, context-aware retrieval, seamless orchestration, and secure deployment — all without adding months of engineering overhead.
With Trismeg, teams can:
Ingest and vectorise product documentation from structured and unstructured sources
Host or connect to LLMs using managed or self-hosted infrastructure
Orchestrate prompts and retrieval using agentic workflows
Integrate the assistant into trial environments using MCP, Trismeg’s modular integration layer, with ready-to-use APIs and embeddable UI components.
Monitor usage and optimise performance with built-in observability tools
Whether you’re running a proof of concept or preparing for a full-scale rollout, Trismeg provides the infrastructure to help teams move from prototype to production—faster and with greater control.
Following a successful pilot, the natural next step is a production-ready rollout—ideally using a self-hosted LLM to reduce token costs and maintain full control over your data. With Trismeg, you can scale effortlessly: use the same vectorisation workflows and agentic architecture to extend the assistant to new visitors on your marketing site and support existing customers with onboarding, setup, and product education. What starts as a proof of concept becomes a fully integrated experience—boosting conversions, reducing support load, and improving every user’s first impression.