Grounding an AI Support Assistant in Real Help Content
Client B2B SaaS company (N.D.A.)Timeline 6 weeksTeam 2 ML-focused engineers + 1 product designer
35% reduction in L1 escalations
50% deflection in pilot programs
Policy and tone guardrails live on day one
The challenge
Support volume was scaling faster than headcount, and a naive chatbot would risk trust. The product needed accurate, sourced answers and clean handoff to agents.
Our approach
Step-by-step how we scoped, built, and shipped the work—together with the client team.
Content and retrieval
Chunking, re-ranking, and feedback loops to improve retrieval quality with each release.
Handoff and analytics
Threshold-based escalation with full context for the human queue.
Tech stack
Key features built
Citations
Every answer links to the source help article or policy.
Eval harness
Regression set for sensitive topics and brand tone.
Handoff
Seamless agent UI with the model’s draft and sources.
Content ops
Workflows to refresh and approve knowledge updates.
Rate limits
Org-level throttling to protect support SLAs and cost.
Observability
Tracing and topic clustering for continuous improvement.
Timeline
Milestones from kickoff to launch and handover.
Week 1–2
Design
Safety model and handoff design.
Week 3–5
Build
RAG, UI, and agent cockpit.
Week 6
Pilot
Production pilot and tuning.
The results
Outcomes
Deflection and quality targets met; roadmap includes multilingual support.
“We finally have an assistant that admits limits and points to the right policy.”
Next steps
Expanding to email drafts and a second product line.
Ready to achieve similar results?
Share your product goals and timeline—we can map a plan that fits your team and delivery window.