Knowledge Ingestion
Document, database, and API ingestion pipelines with parsing, normalization, metadata, versioning, and content refresh behavior.
- Multi-source ingestion
- Metadata and lineage
- Refresh workflows
AI & AUTOMATION / SYSTEM NODEEnterprise RAG & Knowledge Systems
RAG development for grounded, permission-aware enterprise AI.
We engineer retrieval and knowledge workflows around your documents, databases, permissions, evaluation criteria, and deployment constraints rather than treating RAG as a single vector-search feature.
Ownership, deployment boundary, acceptance criteria, and handover are defined per engagement.
RUNTIME ORCHESTRATION
WHAT WE BUILD
Enterprise RAG & Knowledge Systems, engineered as an operating system.
Scope follows the real workflow, environment, failure modes, and ownership model rather than a fixed vendor package.
Hybrid Retrieval
Retrieval pipelines combining lexical and semantic search, filtering, reranking, and context assembly where the use case requires it.
- Hybrid search
- Reranking
- Context controls
Permission-Aware Context
Identity and authorization checks designed so retrieved context follows the data-access boundaries expected by the underlying systems.
- User-aware filtering
- Source authorization
- Audit-friendly context
Grounded AI Applications
Search, question answering, analyst copilots, support assistants, and internal knowledge interfaces connected to controlled retrieval services.
- Citations
- Refusal paths
- Application APIs
Evaluation & Operations
Evaluation sets, trace review, quality monitoring, feedback workflows, and operational runbooks for maintaining a knowledge system after launch.
- Retrieval evaluation
- Trace review
- Content freshness
DELIVERY SEQUENCE
From unknowns to production evidence.
01
Knowledge Audit
Map source systems, document types, ownership, permissions, freshness requirements, and the questions the system must support.
Knowledge Estate Knowledge Map02
Retrieval Design
Choose parsing, chunking, indexing, metadata, hybrid retrieval, reranking, and citation behavior against representative content.
Knowledge Map Retrieval Design03
Build & Integrate
Implement ingestion, retrieval, model orchestration, application APIs, and identity-aware context boundaries.
Retrieval Design Integrated System04
Evaluate
Measure retrieval relevance, groundedness, task completion, refusal behavior, latency, and cost against an agreed evaluation set.
Integrated System Evaluation Evidence05
Operate
Deploy with telemetry, content refresh procedures, access-change handling, feedback capture, and reviewable runbooks.
Evaluation Evidence Operated SystemTECHNOLOGY SURFACE
Tools selected for the system, not the trend.
Technology follows environment, operating constraints, team ownership, and long-term maintainability.
01Application
Python · FastAPI · TypeScript · Next.js
02Orchestration
LangGraph · LangChain · Custom workflows
03Retrieval
PostgreSQL / pgvector · Qdrant · OpenSearch · Hybrid search
04Models
OpenAI-compatible APIs · Anthropic-compatible APIs · Open-source models
05Operations
OpenTelemetry · Evaluation harnesses · Structured logging
WHERE IT FITS
The same capability changes by domain.
Enterprise Knowledge
Internal policies, technical documentation, procedures, controlled search, and knowledge-assistant workflows.
Finance
Research, document intelligence, internal knowledge retrieval, and review workflows subject to access controls.
Healthcare
Knowledge-assistance workflows where deployment, privacy, permissions, and human review are explicitly scoped.
Technology
Product documentation, support knowledge, developer content, and AI features grounded in maintained sources.
TECHNICAL FAQ
Questions worth resolving before delivery.
01Is RAG only a vector database project?+
No. Production knowledge systems usually involve ingestion, identity, authorization, retrieval quality, model behavior, evaluation, refresh operations, and application integration in addition to an index.
02Can the system use our existing identity and permissions?+
Where the source systems expose the required identity and authorization information, we design retrieval so access rules can be enforced before context is supplied to the model.
03Can this run in a private environment?+
Deployment can be designed for cloud, private-cloud, or controlled infrastructure depending on model availability, data policy, network constraints, and the systems that need to be integrated.
04How do you evaluate answer quality?+
We define representative questions and expected evidence, then measure retrieval and response behavior with automated checks plus human review for the risks that matter to the use case.
OSYSTIC / PROJECT CHANNELOPEN
START A PROJECT
Turn governed knowledge into a usable AI system.
Share the sources, users, permission model, and questions you need the system to handle. We will help define the retrieval and operating architecture.