This presentation focuses on the technical architecture behind the Anointed EDU AI Companion program.
The core problem is not simply how to add a chatbot to a website.
It is how to make governed institutional knowledge discoverable, machine-readable, version-aware, evidence-aware,
and retrievable in a way that gives an AI system less room to guess.
The architecture combines canonical publications, structured machine records, search and discovery,
authority resolution, evidence verification, and specialized companions.
The system is still in pre-production and research implementation, so I will distinguish what has been
deployed from what remains a hypothesis to be tested.
The technical problem begins before the model generates an answer.
The system needs to know what object it is looking at, which record is authoritative, which version is
current, what evidence status applies, and what the companion is permitted to do.
If those facts are left implicit, the model or retrieval layer must reconstruct them from fragments.
Our approach is to make those relationships explicit before reasoning begins.
That does not eliminate model error, but it reduces avoidable ambiguity and gives the companion a governed
path back to authority.
The first technical layer is the AI-readable knowledge object.
We preserve the canonical PDF for human reading and citation.
We add a structured text representation that makes semantic boundaries explicit.
Then we add a machine manifest that describes identity, version, rights, authority relationships, and routing.
The key principle is that the machine representation does not become the source of truth.
It points back to the governed publication.
By publishing stable identifiers and explicit fields such as current version, epistemic status, rights,
and predecessor or successor relationships, we reduce the amount of semantic reconstruction the retrieval
system must perform.
The second layer is the web deployment architecture.
The public site is not just a visual interface.
It exposes stable IDs, canonical URLs, machine manifests, structured metadata, sitemaps, search indexes, current-version
resolution, and development and deployment provenance.
This lets a search companion locate an object and determine its role without relying only on keyword
similarity.
It also introduces time awareness.
A companion can distinguish current authority from a historical predecessor rather than treating both
as equally current.
The third layer is governed search before generative reasoning.
Search is functioning as an infrastructure companion.
A user query is matched against a governed index.
The system can then filter for canonical and current authority, retrieve bounded records, and only then
pass those records to a task-specific companion.
The companion should return not only an answer but a source path and status.
This architecture is designed to reduce unsupported or authority-confused responses by giving the model
a smaller and better-governed context.
But I want to be precise: we have not yet measured a hallucination-reduction rate, so that remains a
research hypothesis rather than a validated performance claim.
The fourth layer is the Citation and Evidence Companion, which is the first intellectual-task companion in
the series.
Its job is not to write a better-sounding answer.
Its job is to trace a claim to a source, identify where the support appears, interpret the evidence
status, and return the user to the canonical publication or record.
A critical governance rule is that the companion may interpret evidence but may not rewrite canonical
evidence records or present unverified material as verified.
That turns evidence verification into a first-class technical function rather than an afterthought.
The fifth layer is the function-bounded companion architecture.
We are not building one unrestricted chatbot and asking it to do everything.
Search and Discovery, Citation and Evidence, Methodology, Literature Research, Dissertation Development, Peer
Review, Educator, Learning, Knowledge Navigator, and Publishing or Authoring each have different intellectual
jurisdictions.
They can share the same governed knowledge layer, but their instructions, outputs, and evaluation criteria
differ.
This reduces scope ambiguity and gives us a cleaner way to test whether each companion performs its assigned
task reliably.
Another technical layer is epistemic state.
A companion needs more than content; it needs to know what kind of claim it is handling.
Formation provenance explains why an architecture or decision emerged.
A research hypothesis is testable but not established.
Implementation evidence shows that a feature or control exists.
Validation evidence shows whether it performs as intended.
Authoritative fact is governed institutional or publication truth.
Keeping these states separate is one of the mechanisms we use to prevent a deployed feature from being described
as validated simply because it exists.
So what has actually been built so far?
We have a public AI infrastructure site, machine-readable contracts including an AI manifest, current-version
resolver and search index, development and deployment provenance feeds, governed search architecture, the
Faithful Intelligence ethics layer, and an IP disclosure gate for development records.
These are real deployed infrastructure elements.
The companion applications themselves remain in pre-production or planned stages unless separately identified.
That distinction is important because the technical stack is being prepared before broad companion activation.
The technical thesis is simple: reduce ambiguity before asking AI to reason.
We do that through machine-readable identity, canonical authority, version resolution, governed search, evidence
verification, and function-bounded companions.
The goal is not to claim that hallucination has been eliminated.
The goal is to build an architecture in which unsupported inference becomes less necessary and can be measured.
I also want to thank Walden University.
My studies there provided an important technical knowledge and skill foundation that helped me move
from theory and research into actual systems design and technical deployment.
That foundation continues to inform how I connect scholarship, information systems, governance, and
practical implementation.
Sources and provenance:
Anointed EDU AI Companion Master Prompt, AEU-AI-COMP-PROMPT-001; Anointed EDU AI Companion Preproduction Specification,
AEU-AI-COMP-SPEC-001; AH-WEB-MR-001 v0.3 Companion/Search Global Specification;
Anointed EDU AI Companion IP Development Dashboard v2.1; and the deployed AI.
AnointedEDU.
com v1.2.1 infrastructure.
Claims concerning reductions in hallucinations, cost, latency, or token consumption remain hypotheses until
measured.