HONONOH designs, deploys, and operates bounded private AI systems for complicated work in sensitive environments. We start with the workflow and its evidence, then select the models, controls, and compute required to run it responsibly.
FDE hierarchy: the workload and authority model come first. Private compute follows only when a qualified operating system requires it.
A managed operating capability—not an AI appliance with a markup.
The value is often locked in sensitive records, fragmented systems, and judgment that has never been made explicit.
The system may live on client-owned hardware, on HONONOH-managed equipment at the client site, or in dedicated Canadian-hosted infrastructure.
Private inference close to the work on a qualified desktop or workstation-class system.
Dedicated private infrastructure at the client site without leaving an unsupported AI appliance behind.
Dedicated Canadian-hosted infrastructure for workloads that need more capacity, managed resilience, or contractual isolation.
HONONOH documents data location, provider exposure, operator access, retention, backups, logs, subprocessors, human authority, and prohibited secondary use for the actual workload.
Every deployment needs six named layers with clear ownership and acceptance evidence.
Define the users, decisions, operating result, consequences, and prohibited actions.
Set source authority, retrieval rules, provenance, retention, and refresh cycles.
Choose models, routing, adaptations, evaluation cases, and fallback behaviour.
Apply identity, access, approval gates, logs, tool permissions, and exception paths.
Size compute, storage, network, power, cooling, backup, and recovery.
Monitor, patch, update, recover, support, and report against the service boundary.
HONONOH uses the least complex method that passes the acceptance test.
The Private AI Decision Record establishes the operating and technical basis for a deployment decision.
A 128 GB-class system can be a practical starting point. It is not a universal performance claim. Model size, quantization, context, concurrency, latency, tools, and reliability determine fit.
Equipment, deployment, managed operations, and later change work remain separate commercial components.
The offer remains bounded until the deployment evidence proves otherwise.
“Data never leaves” applies only when prompts, outputs, logs, backups, updates, support paths, and external calls all support the statement.
Consequential actions need named authority, least privilege, evidence, logs, exception handling, and rollback.
No chip, model, operating system, or memory class is promised before the representative workload is tested.
Use these guides to define the service boundary, compare deployment paths, and identify what must be measured.
Define what is private, what HONONOH manages, and why local hardware alone does not create a governed production system.
Read the guideCompare custody, exposure paths, capacity, availability, support, recovery, contracts, and total operating responsibility.
Compare the pathsTrace approved data, model, support, log, backup, export, and deletion routes before making a privacy claim.
Review the controlsTurn one operating problem into a decision record with evidence, authority, evaluation, options, and no-go conditions.
Review the outputsSeparate qualification, implementation, capacity, managed operations, and authorized change.
See the cost structureUse representative evaluation, authority, recovery, operating ownership, and a narrow controlled release.
Review the release gatesThese internal systems show bounded implementation patterns. They are not presented as client case studies, compliance results, or quantified business outcomes.
HONONOH built a local proof harness that detects and replaces sensitive fields while preserving usable document structure. The work includes repeatable tests, OCR qualification, review gates, and a macOS application path.
HONONOH built a bridge that collects development events, normalizes them into reviewable proposals, and keeps a human approval step before external time records are prepared.
Describe the work, evidence, users, constraints, and consequence. Do not upload confidential source material.