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The client — a growing financial planning platform serving independent advisors — faced a critical information bottleneck. Advisors spent an average of 40 minutes per client session manually searching through regulatory documents, product guides, and compliance updates to answer queries. The volume of documentation made it impossible to stay current, and inconsistent answers were eroding client trust.

Vector Pillar designed a RAG architecture that combined a fine-tuned LLM with a vector database ingesting 120,000+ financial documents, updated in real time. We built a conversational interface that allows advisors to ask complex, multi-part financial queries in natural language.

• Fine-tuned a domain-adapted LLM on proprietary financial corpora and compliance frameworks
• Built a hybrid retrieval system combining dense vector search with keyword BM25 re-ranking
• Designed a citation layer that surfaces the exact source document and page for every answer
• Deployed on serverless infrastructure with sub-2-second latency at scale
• Integrated compliance guardrails to flag responses requiring human advisor review

OpenAI GPT-4 fine-tuning · Pinecone vector database · LangChain · FastAPI · AWS Lambda · React frontend · Postgres for audit logging

The client had built an innovative Voice AI platform but was spending nearly 40% of engineering bandwidth on deployment and maintenance operations. Fragile CI/CD pipelines, inconsistent environments across cloud providers, and a lack of observability were slowing feature velocity and causing intermittent production incidents that hurt their NPS.

• Re-architected CI/CD pipeline using GitHub Actions with blue/green deployment strategy
• Containerised all AI services with Docker and orchestrated via Kubernetes (EKS)
• Implemented infrastructure-as-code using Terraform across AWS and GCP
• Built a real-time model monitoring dashboard tracking latency, accuracy drift, and error rates
• Reduced mean time to recovery (MTTR) from 4 hours to under 20 minutes

A mid-size hospital network was drowning in unstructured clinical notes. Physicians spent 2+ hours daily on documentation, and critical insights buried in free-text notes were often missed during handoffs. The client needed a HIPAA-compliant AI system that could extract structured data while remaining explainable to medical staff.

• Trained a custom NER model on de-identified clinical corpora to extract diagnoses, medications, dosages, and symptoms
• Built a summarisation pipeline using a fine-tuned clinical BERT model to generate structured handoff notes
• Developed a physician review UI with confidence scores and source highlighting for every extraction
• Implemented a HIPAA-compliant data pipeline with end-to-end encryption and full audit logging

A global logistics operator managing 14 distribution centres struggled with reactive disruption management and manual spreadsheet-based demand forecasting. Supply chain disruptions were identified only after they had already impacted delivery SLAs, and inventory inefficiency was costing millions annually.

• Built a real-time data lakehouse on AWS ingesting POS data, supplier feeds, weather APIs, and geopolitical risk signals
• Trained an ensemble forecasting model combining gradient boosting and LSTM for 30/60/90-day demand prediction
• Developed a disruption early-warning system using anomaly detection on logistics network graphs
• Deployed an LLM-powered analyst copilot allowing supply chain managers to query predictions in natural language

A fast-growing e-commerce platform with 2M+ SKUs found that 38% of searches returned zero relevant results. Keyword-based search couldn't understand intent or natural language queries, and there was no personalisation layer connecting browsing history to search results — driving shoppers to competitors.

• Replaced keyword search with a dense-vector semantic search engine powered by product embedding models fine-tuned on the client's catalogue
• Built a user-preference model blending collaborative filtering with real-time session context for result re-ranking
• Implemented multi-lingual query understanding supporting 8 languages
• A/B tested against legacy search and rolled out progressively to 100% of traffic over 4 weeks

An online learning platform with 200,000 enrolled students was struggling with a 62% course abandonment rate. One-size-fits-all content and a lack of immediate feedback were leaving learners disengaged, while instructor teams were overwhelmed with repetitive support tickets.

• Built a curriculum-aware LLM assistant with structured course content as grounding context via RAG
• Developed an adaptive assessment engine that adjusts question difficulty based on learner performance vectors
• Created a learner knowledge graph to track concept mastery and personalise revision paths
• Integrated a proactive nudge system using engagement signals to re-activate at-risk learners