Fine-tuning vs domain-specific LLMs for banks
A guide to fine-tuning vs domain-specific LLMs in regulated finance: when each fits, where RAG and tools help, and the evidence to keep. Updated October 2026.
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Principal · AI Native Platform Engineering
Experience18+ years, around 9 in financial services
Raghu is Bugni Labs' principal for AI Native Platform Engineering, the substrate AI-native applications deploy onto. He operates as both architect and hands-on engineer: shaping GCP-native platform architecture, GitOps, and DevSecOps patterns, and building them into production platforms with Python-driven automation. At Bugni Labs he is core developer of HYPER, the internal platform product for automated GCP environment setup. 18+ years of technical architecture and around nine years in UK financial services.
The substrate AI runs on. The recurring threads are platform engineering displacing DevOps in regulated firms, governed delivery pipelines rather than plain CI/CD, and zero-trust design for LLM platforms. A hands-on series follows the agent-fabric from a laptop running Docker Compose through to multi-provider production, alongside Terraform and GCP build notes.
20 pieces · 3 Perspectives · 7 Field Notes · 10 Guides
A guide to fine-tuning vs domain-specific LLMs in regulated finance: when each fits, where RAG and tools help, and the evidence to keep. Updated October 2026.
Read guideA guide to judging an AI engineering partner in banking: the eight capabilities that take a pilot into regulated production. Updated October 2026.
Read guideA guide to augmented engineering: governed AI agents, gates and evidence for software delivery in regulated financial services. Updated October 2026.
Read guideA guide to platform engineering tools for regulated banks: intent-time governance, auditability and explainability for safe agentic operation.
Read guideHow enterprises measure platform engineering ROI, the hidden costs that surface after adoption, and what changes when AI agents join the lifecycle.
Read guideOne morning a prose reduction run cut 87 lines of comment and docstring from a codebase a coding assistant had written. By evening it had put 318 back.
Read field noteWhy regulated financial institutions are moving from isolated AI pilots to governed platforms with evidence, controls, and bounded autonomy.
Read guideMLOps for financial services is the engineering discipline for building, releasing, monitoring, and governing machine learning systems in regulated environments. It connects model development with software delivery, data governance, runtime observability, and audit evidence so that a model can move from experiment to production without becoming a black box.
Read guideWhat changes when agent-fabric has real traffic, multiple LLM providers, a second agent path, and cost controls that need to survive production use.
Read field noteHow agent-fabric moves from localhost to live HTTPS on GCP with Terraform-managed Cloud Run, load balancing, secrets, DNS, and observability.
Read field noteHow to run agent-fabric locally with Docker Compose while keeping the same gateway, auth, agent, and web code paths used in production.
Read field noteThe architecture choices behind agent-fabric, a zero-trust LLM platform that keeps auth, quotas, provider routing, and agent execution in separate services.
Read field noteCloud-native architecture for financial services: definition, core building blocks, banking implementation framework, ISO 20022 patterns, pitfalls, FAQ.
Read guideZero-trust architecture for financial services explained: NIST and CISA foundations, UK and EU regulator expectations, a six-step plan and AI agents.
Read guideFor banks, the multi-cloud versus hybrid cloud question is not a preference. It is a constraint, shaped by where regulated data lives, what the legacy stack actually looks like, and how mature the engineering team really is.
Read perspectiveHow we built an internal developer platform that shipped 20 microservices to a UK neobank in 4 months: 12-15 deploys/day, 47-minute lead time, zero incidents.
Read field noteIn most regulated banks, the DevOps team has quietly become the bottleneck DevOps was supposed to remove. Platform engineering is not a rebranding. It is a recognition that the original arrangement broke under load.
Read perspectiveMaster building governed AI delivery pipelines with AI engineering methodology. Bugni Labs' proven approach delivers 4-month concept-to-production for financial services, with zero incidents and 3-5x velocity.
Read field noteA guide to platform engineering for enterprise leaders: internal platforms, governed agent workflows, and delivery in regulated environments.
Read guideDiscover why governed AI delivery pipelines are replacing traditional CI/CD for faster, safer AI deployment in financial services. Learn current limitations, key developments, implications, and future steps from Bugni Labs' AI-native expertise.
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