The Problem: Inbound Calls and Back-Office Errors in a UK Fintech
A UK fintech with 2,000+ employees is drowning in inbound calls. Sales reps spend 40% of their day on the phone, qualifying leads that are often unqualified. The back office spends 30% of its time manually entering data from these calls into Salesforce, with an error rate of 8%. The cost per support ticket is £12, and the company is losing deals because reps are not available to follow up on qualified leads. The problem is not a lack of tools; it is a lack of automation. The company needs a system that can handle the first 60 seconds of a call, extract the relevant data, and update the CRM without human intervention. The constraint is PCI DSS: the system cannot store or process card numbers. The solution is a voice agent that runs on an on-premise open-weight model, integrated with Salesforce, and approved by a human before any data is committed.
The Mechanism: A Three-Stage Voice Agent Pipeline
The voice agent uses a three-stage pipeline. First, a speech-to-text engine (Whisper or Deepgram) transcribes the call in real time. Second, an on-premise open-weight model (Llama 3 70B or Mistral 7B) processes the transcript. The model is prompted to extract specific fields: company name, job title, budget range, and timeline. The model outputs a structured JSON object. Third, the JSON is mapped to the corresponding fields in Salesforce via the REST API. If the model is uncertain about a field, it flags it for human review. The human agent sees the transcript, the extracted fields, and a confidence score, and can approve, edit, or reject the entry before it is committed to the CRM. The entire pipeline runs in under 2 seconds, so the agent can respond to the lead in real time. The on-premise model ensures that no data leaves the building, which is critical for PCI DSS compliance.
Trade-offs: API vs. On-Premise, Automation vs. Human-in-the-Loop
The architect faces three key trade-offs. First, the choice between an API-based LLM and an on-premise open-weight model. The API is faster to deploy and cheaper for low volume, but it sends data to a third party, which is a PCI DSS risk. The on-premise model is more expensive to set up (around £20,000 for hardware) but keeps data in-house. Second, the choice between a fully automated system and a human-in-the-loop system. Full automation is faster but riskier; a human-in-the-loop system is slower but safer. For a fintech, the human-in-the-loop approach is non-negotiable. Third, the choice between a narrow use case and a broad one. A narrow use case (lead qualification) is easier to scope and deliver in 4 weeks, but it does not address the back-office error rate. A broad use case (all inbound calls) is more valuable but harder to deliver in 4 weeks. The recommendation is to start with a narrow use case and expand from there.
Recommendation: A 4-Week Pilot for Lead Qualification
The recommendation is to run a 4-week pilot focused on lead qualification. Week 1: process audit and baseline measurement. The team measures the current error rate (8%) and cycle time (15 minutes) for lead qualification. Week 2: build the voice agent, integrate with Salesforce, and set up the human-in-the-loop approval workflow. Week 3: closed beta with a small group of real leads. The team tunes the model and fixes edge cases. Week 4: full rollout to the sales department, with daily monitoring of error rates and cycle times. The success criteria are a 20% reduction in error rate and a 30% reduction in cycle time. If the pilot meets these criteria, the team moves to rollout, which involves scaling the solution to other departments and integrating it with additional systems. The pilot is scoped to a single department to keep the timeline realistic and the risk manageable.
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