Automoat
private local AI + a harness for building your moat
Automoat
Automoat is being built around two connected tracks: private, cost-efficient local AI on consumer hardware, and a harness that helps a business discover, build, and compound proprietary advantage.
Token economics and privacy are core product constraints. Local techniques must earn their place through measured useful work; moat hypotheses must earn theirs through evidence.
Product hierarchy
Keep sensitive context on consumer hardware and make token economics work.
The local-inference track evaluates models, quantization, caching, speculative decoding, and advanced techniques such as DFlash 2 against useful output, memory, latency, and cost. DFlash 2 is an evaluated candidate, not a claimed Automoat integration or measured Automoat speedup.
Discover proprietary signal, build it into workflows, and compound what proves valuable.
The harness can start with a business or its records. It maps moat hypotheses, structures the right data, creates evals, compares baselines, and measures whether a proprietary workflow creates durable lift.
Know what this machine has actually proved before routing private work to it.
A content-free Local Run Receipt and a person's frozen quality,
time, cost, and privacy requirements become a
fit, not_fit, or
unmeasured decision card. Missing parallel,
memory, cache, or no-egress evidence stays unmeasured; a
vendor speed claim never fills it. The card cannot start a
model or dispatch work.
See the local fit boundary.
Independent evidence paths catch omissions before incomplete data is trusted.
Whole-Record Check is a released capability and proof of the larger harness. In Dallas electrical permit and inspection records, it compares independent evidence paths so missing records surface instead of disappearing inside a plausible dataset.