Yantra — decision intelligence for AI factories

GPUs under management

48,640

across 6 halls · 3 sites

Fleet health index

96.2

▲ 0.8 this week

Cluster MFU

41.3%

▲ 2.1 pt since predictive migration

Predicted failures · 72 h

37 nodes

34 already draining

Energy signal · day-ahead

$47/MWh

ERCOT avg · summer on-peak $110–165

LAYER 01 Forge Hardware Engineering — NPI, custom server design, GPU validation, co-design

Every burn-in, every validation run, every RMA becomes structured data. Forge closes the loop between what fails in the fleet and what the ODM builds next.

Burn-in first-pass yield by SKU · last 30 days

flagged: yield below 93% gate — auto-opened ECN w/ ODM

Failure fingerprints · validation + fleet, merged

Signature	30 d	Trend	Attribution
HBM ECC double-bit	214	▼ 18%	vendor lot 24-C · RMA batch open
NVLink CRC storm	96	▲ 41%	switch fw 2.4.1 · rollback queued
VRM thermal drift	61	▲ 12%	E1.S custom node · ECN filed
PCIe retimer dropout	23	▼ 6%	cable batch replaced wk 28

Use case Meta’s Llama 3 run logged a failure every ~3 h on 16,384 H100s — ~54% traced to GPU/HBM (arXiv:2407.21783). Forge feeds those fingerprints to the ODM in weeks; burn-in catches the 3–8% infant mortality before it ships.

LAYER 02 Fleet Datacenter Infrastructure & Fleet Management — DCIM, fleet health, node lifecycle

One health score per node, built from DCIM, BMC, and job telemetry. Failure prediction drains nodes before the job dies, not after.

Fleet health · Hall B · 512 racks (each cell = 4 racks)

health low → high predicted failure

Predictive maintenance queue

Node	Signal	Est. TTF	Action
b07-r112-n3	ECC error slope 4.2×/day	31 h	drain now
b02-r018-n7	HBM temp drift +6 °C/wk	58 h	drain at ckpt
a11-r201-n2	NVLink retrain events ×9	64 h	drain at ckpt
c04-r077-n5	PSU ripple anomaly	6 d	watch

Use case Meta logged 419 unexpected interruptions in 54 days on 16k GPUs. At posted B200 rates (~$5/GPU-hr) that cluster burns ~$82k/hr — draining predicted failures at checkpoints instead of mid-job is worth ≈$19M/yr.

LAYER 03 Sentinel Production Engineering — reliability, incident command, AI guardrails

Every incident becomes training data. Sentinel correlates job telemetry, fabric events, and change history into a ranked root cause before the bridge call starts.

Job interruptions per 1,000 node-days · 12 weeks

interruptions / 1k node-days (Llama 3 baseline ≈ 3.8)

Auto-RCA · last 24 h

confidence 0.91

NVLink flap cascade — Hall B

Correlated w/ switch fw 2.4.1 rollout (wk 29). 14 jobs touched. Rollback queued via change mgmt.

confidence 0.84

Checkpoint stalls — storage pool 3

Metadata server GC pauses align w/ stall windows. Suggested: pin GC off-peak.

guardrail

ADLC audit: remediation agent

312 auto-actions this week · 100% within policy · 2 escalations to human, both approved.

Use case Auto-RCA cut MTTR 55% in reference design. Reliability review packs generate themselves — SEV list, blast radius, corrective actions, trend.

LAYER 04 Atlas Cloud Platforms — capacity control plane, inventory & topology, observability, auto-remediation

A live graph of every GPU: where it is, what it's wired to, who holds it, and whether it can actually be sold. Fragmentation is revenue lying on the floor.

Sellable vs stranded GPUs · 8 weeks

sellable (topology-clean) stranded (fragmented / misinventoried)

Remediation recommendations

+512 GPUs

Defrag island — Hall C, pods 7–9

3 tenants movable at next checkpoint → contiguous NVL domain restored.

+288 GPUs

Inventory drift — 36 nodes “in repair”, actually healthy

BMC + burn-in evidence attached. One-click return to sellable pool.

auto

Observability rollup

Golden-signal SLOs per pod; 3 pods burning error budget — throttle recommendation issued.

Use case Reference model: 4% of a 48k fleet stranded at any time. Recovering it ≈ 1,900 GPUs ≈ $29–58M/yr at posted H100→B200 on-demand rates, 70% utilization.

LAYER 05 Flux Flexible Compute — software-defined power, power as a control input

Power is the scarcest input in the AI buildout — a GB300 NVL72 rack draws 135–150 kW, so a 512-rack hall is a ~65 MW grid asset. Flux treats it as schedulable: price and carbon signals in, checkpoint-aware power envelopes out.

Day-ahead price vs shiftable load · today

day-ahead $/MWh · shaded = recommended curtailment window

Software-defined power plan · Hall B

Window	Envelope	Mechanism	SLA impact
00–14 h	62 MW	full rate	none
14–17 h	48 MW	ckpt + GPU freq cap on preemptible tier	none
17–24 h	62 MW	catch-up burst, deferred jobs first	none

today

Projected: $5k energy avoided · 4CP position preserved

ERCOT peak avoidance is worth $40–67k/MW-yr — $2.5–4M/yr on this hall. Zero SLA-bearing jobs touched.

Use case Emerald AI showed a 25% power cut for 3 h w/o service disruption (Nature Energy, 2026); Google runs 1 GW of DC demand response. ERCOT 4CP avoidance alone ≈ $2.5–4M/yr on a 62 MW hall — before energy arbitrage.

Why Decision Minds

01 · LAND

Fleet data plane engagement

We build the unified telemetry lake — DCIM, BMC, fabric, job scheduler — in 8–12 weeks. Pure consulting revenue; the customer keeps the data model.

02 · EXPAND

Decision modules

Predictive maintenance, fragmentation recovery, power optimization — deployed as Yantra modules on the data plane, licensed per GPU under management.

03 · COMPOUND

Co-design flywheel

Cross-fleet failure fingerprints (anonymized) become the industry benchmark — the data moat neither the ODM nor the hyperscaler has.

Decision Minds has spent 15 years building enterprise data platforms. The AI factory is the largest new data-platform problem in the world: a 50k-GPU fleet emits more telemetry than most banks. Same discipline, new physics.