If you need help connecting data readiness, custom AI, systems integration, and governance into a scoped private RAG implementation, Moltech Solutions can support that effort. Reach out through your approved Moltech contact pathway to start the conversation.
Frequently Asked Questions
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A private enterprise AI assistant project is a controlled, permission-aware AI system that provides employees access to approved company knowledge while enforcing security and governance. It retrieves information from authorized sources, shows verifiable citations, and respects user permissions to ensure trust and privacy.
Starting with a narrow use case focuses the AI assistant on one recurring, bounded question type with clear right/wrong answers. This approach enables strict governance, better data preparation, measurable success criteria, and manageable pilot size, making it easier to prove value before scaling enterprise-wide.
Company data should be prepared by inventorying and approving only owned, useful sources with clear business purposes. Data must be cleaned, duplicates removed, classified at the document and page level for sensitivity, enriched with traceable metadata, and managed with defined lifecycle and connector rules to ensure security and quality.
A permission-aware private retrieval-augmented generation (RAG) system ensures that all retrieved content is authorized for the querying user. It includes identity and access management integration, metadata-based permission filters on chunks, and strict enforcement at ingestion, indexing, retrieval, and answer rendering stages to prevent unauthorized data exposure.
Deployment options include managed (SaaS), private cloud, hybrid, and on-premises, each with different trade-offs in control, privacy, integration, operational complexity, and costs. The best choice depends on data residency requirements, contractual terms, integration needs, operational capacity, recovery targets, observability, and cost drivers. Organizations should pick the simplest model that meets their policies and run a pilot before scaling.
Security and governance must be baked into every prompt, retrieval, and action. This includes scanning inputs for prompt injection, enforcing retrieval limits, filtering output for sensitive data, locking down model behavior, validating outputs against rules before execution, adversarial testing, maintaining least privilege policies, logging and monitoring events with privacy controls, and having clear ownership and incident escalation playbooks.
Metrics include retrieval quality, groundedness (answers reflect retrieved sources), completeness, citation usability, correctness validated by domain experts, authorization behavior, safety filters for toxic or sensitive content, and escalation effectiveness. Tests cover permission changes, deletion propagation, conflicting versions, handling restricted documents, and prompt-injection resilience, ensuring reliable and secure answers.
Human review is crucial for high-impact answers involving contract interpretation, regulated decisions, sensitive data exports, or consequential workflow actions. Starting with read-only retrieval and draft creation, human oversight ensures accuracy and compliance before enabling automated actions, preventing errors and unauthorized decisions that could harm the business.
A governed pilot starts narrow with one user group, approved sources, and question class, with named owners and limited integrations. It requires monitoring key signals like answer failures, permission anomalies, citations, user feedback, and costs. Clear stop or rollback conditions and explicit operational ownership help manage risk. Expansion happens only after acceptance criteria are met and residual risks accepted by stakeholders.
Moltech Solutions Inc. supports building private AI assistants by providing fit and readiness assessments covering approved data, integrations, governance controls, hosting options (on-premises or private cloud), and operational processes. They help organizations scope projects, connect data readiness with AI solutions, and establish secure, governed knowledge access systems tailored to business needs.
