Custom Enterprise AI Solutions
Tailor-made generative AI integrations, fine-tuned domain models, and secure internal knowledge bases built for complex business requirements.
Custom Enterprise AI Solutions by Arranto delivers tailored artificial intelligence architectures for enterprises in Saudi Arabia and the Middle East. Arranto can build private retrieval-augmented generation (RAG) systems that query approved company documents, legal contracts, and historical records while keeping data access, retention, and external-model boundaries explicit. Engineered with type-safe TypeScript and Arabic/English workflows where required, these solutions can support document intelligence, compliance review, and executive decision support.
Overview & Approach
Generic AI tools lack context regarding your company's proprietary SOPs, compliance rules, and historical datasets. Arranto builds dedicated AI solutions grounded exclusively in your data.
From internal knowledge assistants to automated compliance auditors, we build solutions that deliver verified, accurate responses with full source citations.
Core Capabilities
Private RAG Systems
Query approved manuals, contracts, and internal databases with fast vector retrieval.
Access Controls & Data Boundaries
Documented API, retention, and permission controls for sensitive company information.
Source Citation Engine
Every AI response links directly back to the exact source document and line number.
Enterprise Search
Unified search across Google Drive, Notion, Slack, and local SQL servers.
Service Blueprint
Initial client consultation and project brief.
Requirement analysis.
Research and planning.
Resource identification.
Cost estimation based on project requirements.
Development process.
Delivery.
Ongoing maintenance and support.
Service FAQs
Q: Will our data be used to train ChatGPT or other public models?
Not by default. The architecture should define which providers, retention settings, and private stores are allowed, then verify those settings against the client's policies and contracts.
Q: What is a private RAG system and when do we need one?
A private RAG system retrieves relevant passages from approved company sources before generating an answer. It is useful when people need to search policies, manuals, contracts, or internal records without sending the entire knowledge base into a public chat.
Q: How do you reduce incorrect or unsupported AI answers?
We combine source filtering, retrieval tests, prompt rules, confidence handling, citations where appropriate, and human review for high-impact actions. No system should promise perfect answers, so the remaining limits are documented clearly.
Q: Can the solution connect to our existing documents and databases?
Yes, subject to access and data quality. We can plan connectors for approved document stores, databases, or business tools, then define which sources are authoritative and how updates are handled.
Q: Do we need fine-tuning to build a useful company AI assistant?
Not always. A well-designed retrieval and evaluation layer may be enough for a knowledge assistant. Fine-tuning is considered only when the task, examples, data quality, and expected maintenance justify it.
Q: Can the interface work for Arabic-speaking and English-speaking teams?
Yes. Language support is planned across the interface, prompts, source documents, permissions, and review process. We validate both languages with examples from the team's actual vocabulary.
Engineered for local enterprise deployments adhering to GCC government cybersecurity and data privacy frameworks.
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