Aderencia Knowledge Expert AI Knowledge Base Managed Services

🏛️ Aderencia Knowledge Expert is delivered as a fully managed service designed to prepare, curate, and continuously evolve the knowledge bases that feed AI assistants and retrieval augmented generation solutions. Instead of sending raw documents straight into a retrieval pipeline, the service treats complexity upstream: source content is collected, cleaned, structured, standardized, and approved before indexing, producing optimized and fully traceable knowledge assets. The service is source and domain independent by design, so the same platform serves legal, regulatory, human resources, financial, technical support, and operational knowledge without changes to its core.

⚙️ The service covers the complete preparation flow. Multi source ingestion handles web crawling, HTML portals, JSON and REST APIs, internal document stores, and manual upload, with resilient connectors. Extraction and consolidation remove noise, deduplicate by checksum, and discard superseded content by validity date. LLM based processing structures each document into sections, hierarchy, and metadata, classifies it by theme, audience, and status, and standardizes terminology against a controlled glossary. Semantic enrichment adds examples, conditions, and cross references, producing structured documents, FAQs, and glossaries traceable back to their original source.

🧩 Quality is enforced by design rather than by inspection. A human curation gate approves or rejects every document before it reaches the index, golden sets maintained per knowledge base support both automated evaluation and manual review by curators, and objective indicators such as answer quality score and top ranked retrieval accuracy make content quality measurable and auditable over time. Domain restricted assistants answer strictly from approved content, cite the passages they used, and decline questions outside the defined scope, which materially reduces hallucination risk in regulated environments.

🔧 The platform is built as decoupled layers that the customer can extend. A web single page application supports administration, curation, and conversational use, with adaptable theming. A REST API built on FastAPI exposes every capability over HTTP and JSON, with real time progress updates. A Python domain core orchestrates connectors, parsers, LLM processing, chunking, and embedding generation, and PostgreSQL with the pgvector extension stores content and vectors in a single database. New domains and sources are onboarded through configurable templates with no code changes, and knowledge bases and assistants combine in a reusable many to many model, allowing reuse across multiple assistants and bases.

☁️ The platform maps directly onto AWS managed services while remaining fully portable. Document landing zones use Amazon S3 with versioning for source traceability, AWS Step Functions and Amazon SQS orchestrate the preparation flow, and Amazon ECS or Amazon EKS run the FastAPI core, with Amazon Textract reinforcing extraction from scanned documents and complex tables. Language and embedding models are consumed from Amazon Bedrock through private VPC endpoints, or served from customer owned models on Amazon SageMaker, with Amazon Bedrock Guardrails adding a policy layer over domain restriction. Content and vectors are stored in Amazon Aurora PostgreSQL with the pgvector extension, using the same schema and index strategy as an on premises deployment. Everything runs inside the customers own AWS account and virtual private cloud, with encryption through AWS KMS and full auditability through AWS CloudTrail.

🔀 Hybrid operation is a first class deployment model rather than an exception. Because no proprietary service sits on the critical path outside the replaceable AI provider abstraction, workloads move between AWS and restricted on premises environments as a configuration and deployment decision, not a rewrite. Customers commonly run ingestion, elastic processing, and non production environments on AWS while keeping sensitive production data and inference within their own perimeter, or run the full platform on AWS in their own account when data residency requirements allow it. This flexibility is what makes the service viable for organizations operating under strict privacy and data residency regimes, including the Brazilian LGPD.

🛠️ Beyond implementation, the offering operates under a managed services model covering assisted operation, monitoring of ingestion and indexing pipelines, incident, problem, and change management, periodic recuration of aging content, onboarding of new domains and sources, model and prompt tuning, and executive reporting. This provides predictability, traceability, and operational resilience as the number of sources, knowledge bases, and assistants grows.