Ingestion and retrieval
Review how knowledge enters the system, moves through its lifecycle, and is retrieved for grounded responses.
Enterprise RAG Architecture Assessment
AI Architecture Observatory
Assess your RAG workload's architecture capabilities, identify critical control gaps, and create a measurable improvement plan — with a downloadable report.
The first Observatory module gives technical leaders a structured, self-reported view of architecture maturity without claiming automated inspection or independent certification.
- Self-reported
- 18 technical controls
- Downloadable report
What it is
A clearer way to reason about AI architecture decisions.
AI Architecture Observatory is a TitanBases platform for structured architecture decision-support. It helps teams examine capability, control, and improvement questions around important AI workloads.
Its first module focuses on Enterprise RAG Architecture Assessment: a guided review of the practices that shape ingestion, retrieval, evaluation, governance, security, reliability, and operations.
Why enterprise RAG architecture matters
Evidence-backed decisions need more than a working demo.
A successful RAG response is only one signal. Sustainable systems also need intentional controls across the workload lifecycle.
Evaluation and quality
Make evaluation signals, quality controls, and improvement priorities visible to technical decision-makers.
Governance and security
Surface architecture controls that support appropriate access, evidence handling, and accountable operation.
Reliability and operations
Connect reliability, observability, and operational practices to a practical architecture improvement plan.
How the assessment works
From self-reported inputs to a measurable improvement plan.
The flow keeps architecture context, control review, findings, and next actions connected.
- 01
Describe the workload
Provide self-reported context about the RAG workload, architecture decisions, and current practices.
- 02
Review 18 controls
Work through the technical controls and critical gates that shape the assessment.
- 03
Map maturity and gaps
See how the reported practices map to maturity areas and where critical gaps need attention.
- 04
Plan measurable action
Turn findings into prioritized recommendations, practical KPIs, and a downloadable report where supported.
Who it is for
Built for teams making real RAG architecture decisions.
Use the assessment when planning a RAG PoC, moving from PoC to production, or improving an existing production architecture.
AI Engineers
Evaluate the architecture behind a RAG proof of concept or an operating workload.
Solution Architects
Structure architecture conversations around shared controls, gaps, and evidence.
Tech Leads and Engineering Managers
Align teams on priorities as a RAG initiative moves toward production.
CTOs and Heads of AI
Connect technical findings to governance, risk, and measurable improvement decisions.
Heads of Engineering
Identify the operating capabilities that need focused investment and ownership.
IT Directors
Review enterprise architecture implications without treating the output as a certification.
Assessment deliverables
A practical output for architecture review and follow-through.
The assessment organizes the reported architecture state into findings and actions that teams can discuss, assign, and measure.
Maturity view
A structured view of the capabilities reported across the assessment.
Critical findings and gates
Clear visibility into reported control gaps and critical architecture gates.
Prioritized actions
Recommendations ordered to support a practical improvement sequence.
Measurable KPIs
Suggested measures that help teams track progress after the assessment.
Downloadable report
A report designed to support internal review and follow-up where download is supported.
Architecture assessmentConceptual view
Capability view
Critical gatesPrioritized actionsMeasurable KPIs
Downloadable reportFindings, actions, and measures
Methodology and limitations
Transparent by design.
Self-reported evidence
The assessment reflects the workload information and practices supplied by the participant. TitanBases AI Architecture Observatory does not independently verify those inputs.
Decision-support, not certification
The output is not an automated infrastructure inspection, security audit, compliance attestation, or guarantee of production readiness.
Context still matters
Recommendations should be reviewed with the workload's business requirements, risk profile, constraints, and supporting technical evidence.
FAQ
Questions technical and buying teams ask.
Clear boundaries make the assessment more useful.
Does the assessment inspect our infrastructure automatically?
No. The assessment is based on information you report about one RAG workload. It does not connect to, scan, or independently inspect your infrastructure.
Is this a security audit or certification?
No. It is architecture decision-support. The output is not an independent security audit, certification, compliance attestation, or production-readiness guarantee.
What does the V1 assessment cover?
V1 focuses on Enterprise RAG Architecture Assessment across 18 technical controls, critical gates, maturity mapping, gaps, prioritized recommendations, and measurable KPIs.
Can we use it for an early RAG proof of concept?
Yes. It is intended for RAG proof-of-concept planning, PoC-to-production decisions, and the improvement of an existing production architecture.
Does the report prove that our answers are correct?
No. Results reflect the supplied answers and the assessment methodology. Self-reported evidence is not independently verified by the platform.
Is the assessment application hosted on this website?
No. This page explains the product. The assessment itself runs separately at observatory.titanbases.com.
Start with one workload
Assess your RAG architecture.
Build a clearer view of capabilities, critical gaps, and the actions that can move the architecture forward.