🔩 7-Agentic AI and Knowledge-Based Lending
Why LLMs alone aren’t enough — and how we make them work for lending.
✍️ Written from Riyadh — for founders, product teams, and AI builders in regulated markets.
🎧 Listen to this Article
When people hear “AI agents,” they picture virtual assistants, chatbots, or autonomous copilots trying to replace a human.
That’s not what we built.
At SoyakaAI, we designed agents for a specific job:
→ To make sense of unstructured financial content
→ To support real-world lending decisions
This is agentic AI, but grounded in compliance, explainability, and local domain expertise.
📚 The Problem: Lending Knowledge Is Trapped
In most institutions, critical lending knowledge is scattered:
Inside PDF manuals
In scanned financial statements
Buried in Excel files, policy decks, or WhatsApp threads
In the heads of credit officers who’ve “been around long enough to know”
None of it is accessible at machine speed. None of it is reliable at scale.
That’s where we come in.
🧩 Our Approach: Vector DBs + RAG + Domain Agents
We ingest messy, real-world documents — and turn them into searchable, queryable, and explainable knowledge.
🔗 The architecture:
Document Ingestion: PDFs, spreadsheets, screenshots — all supported
Embedding + Vectorization: We encode key content using LLMs (e.g., Deepseek, Nomic, LLaMA)
Secure Vector Databases: Hosted inside client infra (fully PDPL-compliant)
Retrieval-Augmented Generation (RAG): Our agents fetch only the relevant context before generating any answer
Guardrails + Traceability: Every response is linked to source documents and includes citations or logic paths
This isn’t chatbot fluff. It’s decision-grade knowledge delivery.
🤖 What Our Lending Agents Actually Do
Think of them not as “AI assistants,” but as highly specialized lending interns — trained to support real use cases:
Document Review Agent
→ “Is this a valid audited balance sheet?”
→ “Highlight all missing fields in Arabic income statements”
Policy Navigation Agent
→ “What’s the max loan amount for a Tier B customer in 2023 policy?”
→ “Summarize the risk override process from our internal playbook”
Borrower Query Agent (future roadmap)
→ “Why was my loan rejected?”
→ “Which financial document caused the score drop?”
All agents are grounded in client-hosted knowledge bases, not generic internet fluff. And all responses can be audited, traced, and logged.
🔐 Built for Regulated Markets
Unlike most open LLM tools, we don’t send your documents to the cloud.
On-prem deployment
GPU-based LLM inference inside the client’s infrastructure
Zero outbound data flows
Full logging + API-based control of agent behavior
This makes us usable where others can’t even pass a security review.
🛠 What This Unlocks for Clients
With our agentic layer, clients can:
Turn static policy PDFs into a living knowledge base
Automate document checks at onboarding or underwriting
Guide junior credit officers through complex workflows
Prepare for regulator audits with fast, context-aware queries
Build borrower-facing interfaces that explain decisions using internal logic, not hallucinated chat
🧭 Final Thought
Large language models are powerful. But in lending, unstructured knowledge is still the biggest blocker — not lack of compute.
With Qararak, we don’t just bring LLMs into the workflow.
We turn your documents, rules, and human expertise into agents that actually help make better credit decisions.
This is what agentic AI looks like — not a toy, not a chatbot, but a trusted knowledge system for regulated finance.
Next Article
🔩 How We Build AI Differently | What Makes Our API Layer Lending-Grade
🎧 Explore More
→ Listen to the 🤖AI on the Ground Podcast: Real-world AI powering compliance, credit, and regulated markets in Saudi — decoded for operators.