AI should answer from facts you can point to.
MLNavigator researches and builds local AI systems that trace answers back to source records, preserve receipts, reduce repeated review work, and remain useful without outside services.
Inside your environmentNo document egress
- MSA v4 §8.2
- Addendum p.3
- Policy §11
run 7f2a · policy legal-review@3 · model pinned llama-3.1-8b · offline
Read the research →
Local AI that answers from your records, shows the source, and runs without outside services.
Four requirements for truthful local AI
The worldview before the product. Each principle is a design requirement adapterOS has to meet — and a thread you can follow into the research and the evidence.
The research became adapterOS
Four questions drove the work: can an answer stay true to its source, can it show that source, can it run without outside help, and can it stop repeating the same work. adapterOS is the instrument those questions produced — a local system that answers from approved records and leaves a trace a reviewer can follow.
The answer is only one artifact
Every run leaves a record a human can inspect later: the sources it drew from, what ran, and what a reviewer decided.
Source trace
The exact records and passages an answer drew from.
Run receipt
What ran — model, policy, inputs, outputs — as a signed line a reviewer can check.
Review packet
What a human accepted, rejected, or marked uncertain.
run local-0142 sources approved set (3) model llama-3.1-8b (pinned) policy legal-review@3 network offline review held for legal approval
Useful without outside services
Sensitive document work runs locally. No outbound network calls, no telemetry, no routine document egress. Models are verified by hash before use, and updates are explicit and verified.
Customer environment
Approved records
Local model
Questions
Answers
Receipts
Review state
No routine document egress
Less repeated effort
When a team has already reviewed the source trail, the next run should reuse that work and focus attention on the delta — fewer repeated full-context runs on the same material.
First runfull source pass
Later runreviewed context + delta
Local operationno re-shipping the same documents
Measured as compute per useful answer — an engineering direction, not a marketing number.
Field deployment for one workflow
Start with one source-bound document workflow your reviewers already own. We ship hardware, install adapterOS in your environment, and you keep the review packet whether or not you expand.
01
Pick the workflow
Choose the review, reporting, or compliance task where sensitive documents already slow the team down.
02
Scope the sources
Load the approved sources, define the reviewer route, and set the boundary for what the system can use.
03
Run real work
Ask questions, compare, summarize, and draft on local hardware with the team that owns the process.
04
Decide with evidence
Measure usefulness, source quality, and review fit — and whether the workflow should expand.
You keep: the review packet
- NSF I-Corps
- ACCEL-KS Grant
- 50+ operator interviews
- Patent applications filed
Start with one answer your team can trace.
Bring one sensitive workflow, the records it must stay inside, and the review standard it has to meet. We map the deployment around the trace your team needs at the end.