The Problem: Manual Back-Office Bottlenecks in a 100-Person Fintech
A 100-person UK fintech processes 400+ contracts and 1,200 invoices monthly. Finance staff spend 12 hours compiling monthly reports and 6 hours reviewing contract clauses. The manual process introduces a 3% error rate in data entry and a 48-hour cycle time for contract queries. The goal is to reduce cycle time to under 4 hours and error rate to under 0.5% without replacing the existing ERP, CRM, or Slack workspace. The solution is a RAG-powered conversational agent that drafts responses, classifies documents, and automates data gathering, with human approval for any output touching financial figures or contractual obligations. The deployment fits an 8-week timeline, starting with a process audit and ending with managed operations.
Mechanism: RAG Pipeline with pgvector and Conversational Agent
The architecture uses a RAG pipeline with pgvector for embedding search. Contract PDFs are ingested, OCR-processed, and chunked into 512-token segments. Each chunk is embedded using text-embedding-3-small into a 1536-dimensional vector and stored in a Postgres 15 instance with the pgvector extension. The HNSW index is configured with m=16 and ef_construction=64 for sub-50 ms retrieval. The conversational agent runs on Slack via the Bot API, listening for mentions in a #contract-review channel. When triggered, it embeds the query, retrieves top-10 chunks, and passes them to GPT-4o for drafting. If the response references payment terms or liability caps, it flags the message for human review in a #approval channel. The model-agnostic layer allows switching to Llama 3 on client hardware for regulated data.
Trade-offs: Model Choice, Human-in-the-Loop, and Timeline
The architect chooses between OpenAI/Anthropic APIs and open-weight models based on data sensitivity. API models offer higher quality but require data to leave the building. Open-weight models like Llama 3 run on client GPU hardware, ensuring data residency but requiring 2x the engineering effort for fine-tuning and monitoring. The human-in-the-loop design adds a 15-minute approval delay for flagged responses but reduces the error rate from 3% to 0.4%. The 8-week timeline is tight; adding a second department mid-pilot extends it to 12 weeks. The managed operations model shifts the burden of model updates and index maintenance to Forfis, costing a fixed monthly fee but reducing the client’s engineering overhead by 60%.
Recommendation: 8-Week Deployment Plan for UK Fintech
Start with a process audit in Week 1-2 to measure baseline cycle time and error rate. Fix the pilot scope to one department (Finance) and one channel (Slack) in Week 3. Build the RAG pipeline and conversational agent in Week 4-5, using pgvector for embedding search and GPT-4o for drafting. Run the human-in-the-loop pilot in Week 6-7, measuring the delta in cycle time and error rate. Roll out to the full Finance team in Week 8 and hand over to managed operations. Avoid adding departments or channels mid-pilot. Ensure the ERP and CRM API documentation is complete before Week 3 to prevent custom connector delays. The managed operations SLA should include 99.5% uptime, 4-hour critical response, and monthly performance reports.
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