Our Case StudiesAutomating RBI compliance with a secure, air-gapped Knowledge Engine.
Softlabs Group deployed a private, on-premise AI model to help financial analysts navigate complex regulatory frameworks. Using Retrieval-Augmented Generation (RAG) on a secure vector database, the solution delivers instant, citational answers from thousands of RBI circulars while ensuring 100% data sovereignty.
BFSI (Banking, Financial Services, and Insurance)
Private Enterprise LLM (RAG)
Secure On-Premise Deployment
Web Portal & Internal API
We partnered with a leading Financial Institute in India, a key player in the banking sector handling complex regulatory frameworks.
They needed a secure, internal intelligence engine to ingest decades of RBI circulars, automate rule retrieval and ensure zero-error compliance in their operational schemes.
India
The RBI recently reviewed over 9,000 circulars, consolidating them into 244 Master Directions. Navigating this dense web of legacy rules and daily updates manually is nearly impossible, often leading to conflicting interpretations.
The cost of missing a single guideline is steep. In 2024 alone, the RBI imposed over ₹56 crore in penalties across 300+ enforcement actions, highlighting the severe financial and reputational risks of compliance gaps.
Financial analysts spend an estimated 1.8 hours every day just searching for information rather than analyzing it. In a sector where speed defines advantage, this "search tax" was severely bottling the client's operational efficiency.
We engineered a Private LLM Knowledge Engine hosted entirely within the client’s secure VPC (Virtual Private Cloud). This system acts as a "Regulatory Super-Analyst."
Using Retrieval-Augmented Generation (RAG), the system ingests thousands of RBI circulars, Master Directions, and internal policy documents. It indexes them into a vector database, allowing compliance officers to ask natural language questions (e.g., "What is the latest provision for NPA classification under the 2025 Master Direction?") and receive instant, fact-based answers with direct citations to the original circular.
Curious about the secure architecture? Check the working of this product and its private deployment.
View Solution ArchitectureRules were buried in thousands of PDFs, emails and legacy circulars.
Public AI tools (like ChatGPT) often invent facts, a disaster for banking.
Uploading sensitive internal rules to the cloud was non-negotiable.
Tracking daily RBI updates manually led to version control errors.
Connecting a 1990 circular to a 2024 amendment was manual and error-prone.
Ingested and indexed 10,000+ documents into a searchable "Single Source of Truth."
The AI is restricted to answer only from the uploaded documents, citing the specific circular ID for every claim.
Deployed open-weight models (Llama 3) locally; no data ever leaves the client's firewall.
The system automatically ingests new circulars and flags "deprecated" rules instantly.
The AI understands context, linking related rules across decades even if keywords differ.
Reduced the time to interpret new RBI guidelines from days to minutes, allowing the bank to roll out compliant schemes faster than competitors.
Achieved total privacy by running the LLM on-premise, ensuring not a single byte of sensitive financial data touched public servers.
Cut down information retrieval time by 90%, freeing up analysts to focus on strategy rather than document digging.
Every AI answer comes with a "Citation Trail," providing auditors with instant verification of the source documents used for decision-making.
The model provides footnotes linking directly to the source PDF page (e.g., "Source: RBI/2024-25/112, Para 4.2").
The AI respects internal hierarchy; a junior analyst cannot access sensitive board-level strategy documents.
Understands banking jargon (e.g., "haircut," "NPA," "provisioning") better than keyword search.
Can instantly compare two versions of a Master Direction to highlight what changed.
Legal & Law Firms Automated case law research and complex contract analysis.
Pharmaceutical Industry Tracking FDA regulations and drug safety compliance data.
Insurance Sector Streamlining policy underwriting and claims verification rules.
Manufacturing Plants Instant retrieval of SOPs and safety manual protocols.
Llama 3 / Mistral (Open-Weight LLMs)
LangChain
LlamaIndex
Milvus / pgvector
Private VPC (AWS/Azure) with GPU Acceleration
React-based Conversational UI
Years of Experience
Countries
Clients
Projects
At Softlabs Group, we take pride in solving complex business challenges with innovative and reliable solutions. Our case studies showcase how we’ve empowered clients across industries with tailored software that delivers measurable results and drives success.
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Built AI-powered vision system for PPE detection, hazard alerts and zone monitoring.
Fewer safety violations, higher compliance rates and safer industrial environments.
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Built wearable AI solution tracking vibration levels, health fatigue and tool usage.
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FAQs
Never. This is a 100% Private, Air-Gapped Solution. The LLM and the Vector Database are deployed entirely on your on-premise servers (or private VPC). No data is sent to public cloud providers (like OpenAI or Google), ensuring full compliance with data localization and privacy norms.
We implement an Automated Ingestion Pipeline. As soon as a new circular is released or downloaded to your repository, the system ingests, categorizes and indexes it. It also automatically "deprecates" older rules that the new circular overrides, ensuring your compliance team never references outdated regulations.
No. The system uses Role-Based Access Control (RBAC). The AI respects your existing security hierarchy. If a junior analyst asks a question, the AI only searches documents they have permission to view. If the answer resides in a "Confidential" document they cannot access, the AI will not reveal it.
Keyword search fails when terms don't match exactly (e.g., searching "bad loans" won't find "Non-Performing Assets"). Our Private LLM uses Semantic Search, understanding the meaning of the banking concepts. It allows you to ask complex questions like "How have the provisioning norms for agriculture loans changed since 2020?" and get a synthesized summary, not just a list of 50 PDF links.
