01 / PREPARE
Make internal data dependable
Identify authoritative documents, tables, tickets, policies and runbooks. Resolve ownership, currency, duplication and access obligations.
Optional Extension · Phase 05 · AI & Intelligent Data Enablement
Make trusted enterprise knowledge useful before adding AI.
For organizations with dependable data foundations—or those ready to build them—Bitbryx helps assess AI readiness and design governed retrieval capabilities. AI is optional, independently scoped, and not a mandatory transformation phase.
AI where it creates value
Data first. Retrieval second. LLM third.
Start with authoritative sources, quality, ownership and access. Measure permission-aware retrieval against real use cases. Introduce generation only where it improves the outcome.
02 / RETRIEVE
Prove evidence can be found
Structure, chunk, label and index content; evaluate permission-aware search, relevance and source traceability.
03 / ENABLE
Use AI where justified
Introduce RAG-based summarization, comparison or drafting where evidence, privacy, latency, cost and oversight meet agreed thresholds.
Four controls to validate
A useful AI capability needs operational discipline.
The model is an optional, governed consumer of approved evidence—not the enterprise knowledge source.
TRUSTED SOURCES
Quality and lineage
Authoritative material with defined owners, retention and freshness measures.
GOVERNED ACCESS
Identity and permissions
Permission enforcement, classification, auditability and safe handling of sensitive content.
RETRIEVAL QUALITY
Relevance and citations
Known-good evaluations of recall, accuracy, citation support and no-answer behavior.
OPERATING CONTROLS
Human accountability
Use-case owners, monitoring, feedback, incident procedures and cost management.
Optional deliverables
A governed foundation for intelligent workflows.
AI enablement may follow core transformation or start independently if existing foundations meet the need.
OUTPUT 01
AI-readiness assessment
Use cases, source ownership, permissions, risk, quality and value hypotheses.
OUTPUT 02
Knowledge architecture
Ingestion, metadata, retrieval, embedding and access-control design.
OUTPUT 03
RAG evaluation framework
Representative questions, retrieval tests, citation and privacy acceptance criteria.
OUTPUT 04
AI operating model
Oversight, monitoring, operating costs, change management and support responsibilities.
Start with clarity
Start with the knowledge your business already owns.
We can help determine whether governed retrieval or a targeted AI use case is justified and how it could be introduced responsibly.
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