Case study
AI-Powered Sales Intelligence Platform
Sales intelligence MVP built around LLMs, AI agents, and a robust machine learning pipeline for B2B workflows.
Qualification efficiency
+65%
Sales cycle
-32%
Conversion rate
+41%
Challenge
The startup needed to prove a compelling AI product quickly, but the platform still had to feel credible enough for real go-to-market use. That meant handling model orchestration, retrieval, integrations, and user trust without collapsing into an overbuilt first version.
Solution
We designed an MVP around focused sales workflows: lead qualification, call insight generation, and follow-up support. The system architecture kept room for iteration while still treating model quality, observability, and product usability as production concerns.
System anatomy
01
Sales workflow interface
02
LLM and agent orchestration
03
Retrieval and context services
04
CRM and communication integrations
05
Evaluation and feedback loops
Constraints that shaped the build
Delivery approach
Architecture and implementation moved together.
This is the part that matters commercially: the system shape was translated into an execution plan that could survive rollout, iteration, and operational pressure.
Scoped the MVP around the workflows with the strongest commercial signal.
Designed agent behavior with guardrails, context boundaries, and observable outputs.
Built a system that could evolve after launch without rewriting the core architecture.
Next step
Discuss a similar system, architecture, or delivery problem.
If this case study looks adjacent to your own challenge, start with a discovery conversation grounded in the system itself.