Use cases

ATOMiK is for teams whose state movement already hurts.

The lead path is edge and embedded evaluation. Data center, infrastructure, licensing, and investor routes remain important, but the first question is always the same: can the team provide one workload, one baseline, and one expensive constraint?

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Lead ICP

Edge and embedded systems

Pain: Battery, heat, bandwidth, latency, footprint, weight, reliability, or field-service constraints are already measurable.

What customer brings: A representative state-heavy workload, current baseline, state size, update cadence, target hardware, and decision threshold.

What ATOMiK evaluates: Full-state movement, repeated scans, repeated replays, sync overhead, reconstruction cost, and coalescing potential.

Likely metrics: Bytes moved, full-state transfers avoided, operations coalesced, cycles/update, latency, power proxy, thermal proxy, correctness.

Proof needed: Workload map first, then proof review or benchmark exchange. Public claims require linked artifact and evidence label.

Fit signal: The state path is the expensive part and correctness can be checked.

No-fit signal: The workload is mostly stateless compute or the baseline cannot be measured.

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Lead ICP

AI at the edge and agent context

Pain: Context movement, state retention, local response latency, memory pressure, or network dependence constrains the product.

What customer brings: Context/state model, update trace, response target, baseline path, target device, and correctness criteria.

What ATOMiK evaluates: Whether meaningful changes can be tracked without repeatedly moving or rebuilding full context/state.

Likely metrics: Bytes moved, update latency, response latency, memory footprint, operations coalesced, correctness preservation.

Proof needed: Current proof is systems-layer and workload-specific. Do not imply board-integrated AI inference without a matching artifact.

Fit signal: The bottleneck is context/state movement around the AI path.

No-fit signal: The bottleneck is only model math and not state movement.

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Lead ICP

Robotics, industrial, and field control

Pain: Local update latency, reliability, power envelope, and disconnected operation create hard constraints.

What customer brings: Control/state path, timing budget, update cadence, logs or traces, target controller, and failure modes.

What ATOMiK evaluates: State-transition boundaries, replay/reconstruction cost, avoidable scans, and local reconstruction opportunities.

Likely metrics: Update latency, cycles/update, state scans avoided, reconstruction cost, duty cycle, correctness.

Proof needed: A responsible path starts with software/workload mapping before any hardware-backed claim.

Fit signal: The system repeatedly rebuilds or scans state to make local decisions.

No-fit signal: The process cannot tolerate any architecture change or lacks a test oracle.

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Secondary path

Remote, IoT, and defense-adjacent systems

Pain: Every packet, watt, ounce, and field-service event matters when connectivity and power are limited.

What customer brings: Telemetry path, sync cadence, link budget, power model if available, target hardware, and reliability constraints.

What ATOMiK evaluates: Bytes avoided, wake-up frequency, duty cycle, local state preservation, and sync/replay cost.

Likely metrics: Bandwidth pressure, bytes avoided, wake-up frequency, duty cycle, power proxy, footprint pressure, correctness.

Proof needed: Claims must stay mission- and workload-specific. Concept visuals are not proof of deployment readiness.

Fit signal: State movement causes bandwidth, power, reliability, or weight pain.

No-fit signal: The constraint is regulatory or mechanical with no state-path lever.

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Strategic path

Data center and infrastructure partners

Pain: Cooling, water, density, bandwidth, utilization, and infrastructure cost can become expensive when state movement scales.

What customer brings: A specific state-heavy service path, baseline counters, utilization data, transfer volume, and the constraint to evaluate.

What ATOMiK evaluates: Whether state movement rather than generic compute is creating measurable waste.

Likely metrics: Bytes moved, operations coalesced, cycles/update, memory footprint, bandwidth pressure, thermal proxy.

Proof needed: Thermal, water, and power language must remain evaluation-target language until measured for the workload.

Fit signal: A state path is driving a cost center large enough to evaluate.

No-fit signal: The workload is dominated by unrelated compute, storage, or facilities constraints.

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Strategic path

Chip, IP, and licensing partners

Pain: A partner needs to know whether state-aware execution belongs in an accelerator, embedded IP block, or architecture roadmap.

What customer brings: Target integration context, workload class, baseline architecture, proof requirements, and commercial decision path.

What ATOMiK evaluates: IP fit, evidence gaps, ASIC feasibility, synthesis artifacts, and workload-specific proof requirements.

Likely metrics: Cycles/update, operation coalescing, area/footprint pressure, synthesis evidence, correctness, integration risk.

Proof needed: Separate live measured, hardware validated, synthesis validated, projected, conceptual, and roadmap claims.

Fit signal: The partner has a state-heavy architecture problem and a defined diligence path.

No-fit signal: The ask depends on production-ready IP claims not supported by current evidence.

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Strong fit signals

The workload repeatedly moves, scans, syncs, replays, or rebuilds state.

The team can provide a current baseline and a decision threshold.

Battery, heat, bandwidth, latency, footprint, weight, reliability, or cost is already painful.

Correctness can be checked against an expected state, invariant, or output.

The economic or technical value is large enough to justify evaluation work.

No-fit signals

There is no measurable constraint.

The workload is mostly stateless compute.

The customer cannot share even representative state behavior.

The baseline is unknown and cannot be measured.

Correctness cannot be verified.

The claimed pain is not expensive enough to support evaluation.

Competition and status quo

The real alternative is often more of the old workaround.

ATOMiK should be compared against what teams actually do today: add compute, batteries, cooling, bandwidth, compression, caching, sync machinery, accelerators, cloud offload, overbuilt hardware, manual optimization, or feature cuts.

Status quo	Solves	Does not solve	Why teams choose it	Where it breaks	ATOMiK difference	Proof required
More compute	Adds headroom quickly.	Does not remove redundant state movement.	It is familiar and available.	When power, heat, cost, or size becomes the limiter.	Targets the state movement that creates wasted work.	Show that state movement, not raw compute, dominates the bottleneck.
Bigger batteries	Extends runtime by adding stored energy.	Does not reduce work, heat, or data movement.	It avoids architecture changes.	When weight, volume, charging, or field service is constrained.	Evaluates whether the workload can spend less energy on state churn.	Proxy or measured power improvement tied to the target workload.
More cooling	Removes heat after it is created.	Does not reduce the work that creates heat.	Thermal fixes are easier to budget than architecture changes.	When fanless, sealed, dense, or water-constrained deployments hit limits.	Evaluates upstream redundant state work before promising thermal impact.	Measured workload improvement and a responsible thermal test plan.
More bandwidth	Moves larger state faster.	Does not reduce transfer volume or intermittent-link risk.	Network upgrades are straightforward when available.	When links are expensive, congested, remote, or unavailable.	Focuses on moving meaningful change instead of full state.	Bytes moved, bytes avoided, and correctness preservation.
Compression	Shrinks payloads.	May still move full state and can add CPU cost.	It is a proven component-level optimization.	When unchanged state is still repeatedly encoded and moved.	Changes the unit of movement before compression is considered.	Compare compressed baseline to state-aware movement on the same workload.
Caching	Keeps frequently used state closer.	Can add invalidation complexity and stale-state risk.	It is familiar and often effective.	When updates, invalidation, or replay dominate the cost.	Tracks meaningful change boundaries instead of only storing copies.	Show reduced invalidation, replay, or sync work with correctness intact.
Deduplication	Avoids repeated identical data.	May not model state transitions or coalesced updates.	It is useful for storage and transfer systems.	When the painful cost is change propagation and reconstruction.	Evaluates transition-aware work, not only duplicate payloads.	Trace-level comparison of duplicates avoided versus operations coalesced.
Sync protocols	Coordinates state across nodes.	Can still require scans, conflict handling, or large payloads.	They are standard integration surfaces.	When protocol overhead or full-state reconciliation dominates.	Targets the state path beneath or beside the protocol.	Baseline protocol trace and state-aware alternative map.
Specialized accelerators	Speeds a defined compute kernel.	May ignore state movement around the kernel.	They fit known workloads such as AI, DSP, or crypto.	When the expensive work is bookkeeping, sync, or reconstruction.	Focuses on state-aware execution and data movement boundaries.	Show the state path is the bottleneck around the accelerator.
Cloud offload	Moves compute and storage to larger infrastructure.	Adds latency, bandwidth dependence, and reliability exposure.	It reduces device complexity.	When local, disconnected, private, or low-latency operation matters.	Evaluates keeping more useful state work local.	Latency, bandwidth, and correctness comparison for local execution.
Overbuilt hardware	Buys margin across unknowns.	Raises cost, size, power, and thermal burden.	It reduces near-term engineering risk.	When BOM, enclosure, or deployment economics are already tight.	Looks for architectural waste before adding more hardware.	Workload map showing avoided state work and footprint pressure.
Manual optimization or feature cuts	Reduces load by hand or removes functionality.	Can be brittle, slow, or product-limiting.	It is under the team's direct control.	When optimization becomes recurring engineering drag.	Evaluates a reusable state-aware path for the constrained workload.	Measured improvement compared with the current optimized baseline.

Start with the constrained state path.

The best first call is not a broad demo. It is a decision about whether one state-heavy workload has enough pain and evidence to justify deeper evaluation.

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