01 Pulse CAGE — Compute Opportunity Assessment
Find the AI work you may not need to run twice.
CAGE examines a representative request sample and identifies where verified reuse, deterministic bypass, or safer rejection may reduce unnecessary model execution. Start with exported or synthetic data—no production control required.
Read-only first. Evidence for every classification. Production remains untouched.
LIVE GOVERNANCE DEMONSTRATION
00:30 / SIDE-BY-SIDE
“The widening space between the lines is released compute capacity—and CAGE produces the receipt.”
Read video transcript
Baseline computes every request, so its compute line continues to rise. CAGE governs every request. During authorized exact repeats, the CAGE compute line flattens while useful outcomes continue increasing. Stale artifacts, cross-mission requests, wrong equivalence, and adversarial near-matches are rejected. When reuse is unsafe, CAGE authorizes fresh computation. The widening gap represents released compute capacity, backed by a decision receipt.
01 Fresh computation02 Verified reuse03 Deterministic bypass04 Reject → safe fresh compute
02 Start small
A useful answer before an infrastructure commitment.
Begin with a bounded sample, not an integration. The initial assessment is advisory and read-only: CAGE classifies opportunity and risk without controlling production output.
A redacted request export
A synthetic workload
A representative test fixture
Existing cost and latency information, if available
No credentials or production access for the initial assessment
ASSESSMENT OUTPUT
Is a larger shadow evaluation justified?
If the opportunity is weak, unsafe, or likely to cost more to verify than it can save, the report should say so.
STOPREFINEPROCEED
03 Three-step assessment
From bounded sample to evidence-backed decision.
- 01
Define the sample
Supply a bounded, representative, redacted, or synthetic request set and describe its correctness requirements.
- 02
Measure the opportunity
CAGE evaluates exact repetition, admissible equivalence, freshness, tenant and mission boundaries, expected compute avoided, and CAGE’s own overhead.
- 03
Receive the evidence report
Receive an opportunity map, risk findings, assumptions, limitations, and a stop/proceed recommendation.
04 Report preview
See the opportunity—and the reasons not to take it.
This example shows report structure only. Values marked “illustrative” are not customer results or measured production savings.
PULSE CAGECompute Opportunity Report
ILLUSTRATIVE SAMPLECross-tenant or cross-mission conflictsFlagged and excludedIllustrative
Estimated model executions potentially avoidedRange reportedIllustrative
Estimated GPU time and cost rangeAssumption-boundedIllustrative
Evaluation overheadMeasured separatelyIllustrative
Confidence and limitation notesIncludedIllustrative
Recommended next stepSTOP / REFINE / PROCEEDEvidence-based
05 Customer fit
Who should start with a CAGE assessment?
Small AI SaaS teams with growing inference costs
AI agencies running repeatable customer workflows
Teams operating local or private models
Managed-service providers running GPU infrastructure
Document-processing and extraction workflows
Developers who suspect repeated requests or failed loops are wasting compute
TX Token X-Ray
Proposed launch prices.
Measure it on your workload first. Upgrade only if the demonstrated value justifies it.
Live checkout — starts a paid monthly subscription.
Prices exclude applicable tax.
01
Free
$0
Reproduce the benchmark.
No checkout required02
Pro
$49USD / month
For individual developers.
Subscribe to Pro03
Team
$199USD / month
For teams managing multiple AI workloads.
Subscribe to Team04
Enterprise
Custom
A tailored commercial conversation.
Talk to Pulse AI06 Commercial entry point
Start with a bounded assessment.
A free initial feasibility conversation
A defined sample and written assessment scope
No production integration in the first stage
Pricing and delivery terms confirmed before any customer data is transferred
A stop/proceed recommendation rather than a forced deployment
07 What CAGE examines
Four plain-language decisions before deeper technical policy.
Fresh computation
Run the model when no prior result is safe, current, and authorized for this request.
Verified reuse
Admit a prior result only when the required identity, authority, freshness, equivalence, and correctness boundaries hold.
Deterministic bypass
Skip model execution when a verified deterministic path can produce the required outcome more directly.
Reject unsafe reuse
Reject a stale, conflicting, or inadmissible candidate and authorize safe fresh computation instead.
Tenant
Mission
Model
Version
Authority
Freshness
Equivalence
Correctness
Evidence
Receipts preserve the inputs, policy outcome, and resource impact needed to review each recommendation.
They support inspection and accountability; they are not a claim of certification, legal approval, or regulatory compliance.
08 Existing measured evidence
Same useful outcomes.
Fewer authorized executions.
Compute-every-time baseline
60 model calls / 60 equivalent requests
33
model executions avoided
on this local synthetic workloadCAGE governed lane
27 model calls / 60 equivalent requests09 Safety demonstration
Attack the reuse boundary.
Select a request condition. CAGE authorizes reuse only for the exact, currently authorized repeat.
DECISION RECEIPTRCPT-EXACT-001
VERIFIED REUSE
- Policy result
- All authorization and equivalence bounds satisfied
- Compute disposition
- Prior verified result admitted
- Evidence
- Decision inputs + policy outcome + resource impact
False reuse 0Stale reuse 0Cross-tenant reuse 0Invalid output 0
10 Local GPU evidence
MEASURED — LOCAL NVIDIA TEST Randomized RTX 3080 inference experiment.
Detected false reuse 0
Detected stale reuse 0
Detected cross-mission reuse 0
Detected invalid reuse 0
11 Deeper evidence — physical QPU
MEASURED — PHYSICAL-QPU RUN Governed execution on a physical Rigetti QPU through Amazon Braket.
This is physical-QPU evidence. It is separate from the local NVIDIA GPU experiment.
Cepheus-1-108Q
False reuse 0
Stale reuse 0
Cross-mission reuse 0
Undetected invalid results 0
12 Deeper analysis — scale scenario
Model a projected scenario.
Do not mistake it for measured savings.
Each independently verified 1% avoidance rate represents 10 million potentially avoided executions for every one billion requests.
PROJECTED SCENARIO — NOT REALIZED SAVINGS
Net projected value$0
- Annual executions avoided
- 0
- Released GPU-hours
- 0
- Equivalent continuous GPU capacity
- 0
- Gross capacity value
- $0
- CAGE overhead
- $0
- Estimated GPU energy difference
- 0 kWh
Net verified savings = avoided GPU-hours × loaded GPU-hour cost + avoided energy × electricity rate − CAGE compute − registry/storage cost − verification cost − operating cost.
Energy is a projected device-only extrapolation using the local allocated ratio of 0.514 Wh per 28.293 active inference seconds. It excludes facility energy, cooling, PUE, WUE, water, and deferred hardware.
13 After the assessment
A 60-day shadow-mode pilot is an optional later stage.
Only proceed when the sample assessment shows enough safe opportunity to justify deeper validation.
CAGE proposes decisions but does not control output. Every proposed reuse is compared against a fresh execution.
Traffic assignment is independent of CAGE. Both lanes use equivalent models, hardware, concurrency, prompt distributions, timing, and service-level objectives.
Start with the assessment- 01Sample assessment
- 02Local or customer-controlled reproduction
- 03Shadow-mode validation with fresh output authoritative
- 04Limited rollout only after agreed safety and economic gates
Pilot pass requirements
- Zero cross-tenant reuse
- Zero stale reuse
- Zero undetected invalid output
- Statistically bounded false-reuse risk
- At least 99.9% meeting baseline quality policy
- Positive net savings after CAGE overhead
- No unacceptable p95 or p99 regression
- Independently verified hashes and telemetry
14 Founder
“Pulse exists to make compute accountable: authorize necessary work, safely reuse verified work, and preserve evidence for every decision.”
Dustin Cummings — Founder, Pulse AI Technologies
737-781-647215 Protected architecture
Measurable outside.
Protected inside.
“Pulse shares measurable behavior, experimental methods, claim boundaries, and evidence verification. Proprietary policy implementation, reuse-key construction, authorization internals, credentials, and private source code are disclosed only under an appropriate confidentiality agreement.”
Source codeInternal algorithmsReuse-key constructionCredentialsRaw private evidencePolicy internalsPrivate architecture diagramsAccount informationCustomer data
16 Contact
Request a Compute Assessment.
Start with a scoping conversation. Do not send a request sample until scope, transfer method, pricing, and delivery terms are confirmed.
17 Claims and limitations
Evidence first. Boundaries always visible.
Local GPU results are from a small synthetic RTX 3080 experiment and are not production savings predictions.
Physical-QPU results came from Rigetti Cepheus-1-108Q tasks executed through Amazon Braket and are not NVIDIA GPU evidence.
Scenario calculator outputs are projections, not measured savings. Production claims require controlled shadow-mode validation.
Experiments used local NVIDIA hardware and a physical Rigetti QPU through Amazon Braket. No partnership or endorsement is claimed or implied.
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