In six months a team of twelve backend engineers with no prior machine‑learning experience built and launched a voice AI assistant called "Sufler" in production. The group used FastAPI, PostgreSQL, a fine‑tuned BERT model and the local Qwen‑8B large language model, deploying everything on‑premise to meet strict information‑security requirements.
The product classifies services, detects objections and generates real‑time prompts through the Voximplant API. Response latency stays at 1.5–2 seconds, allowing managers to receive relevant scripts while calls are in progress.
As a result, average handling time for customer requests fell by 25 percent, and the company eliminated the need to hire costly external ML experts. The team completed training on retrieval‑augmented generation, fine‑tuning and LLM operations, proving that AI can be implemented without specialized staff.
What this means for you now is clear: you can develop a competitive AI tool inside your organization, cut consulting expenses and speed up sales cycles. This case shows how the right technology stack and internal upskilling let you scale AI solutions quickly even under tight security constraints.
Why this matters: building AI in‑house reduces external costs and gives you direct control over data compliance. You can achieve faster response times and higher efficiency, directly boosting revenue generation.