Kernel-level · On-premise · Zero cloud calls

Your GPU dashboard
is lying to you.

95% utilisation doesn't mean 95% useful work. Cognit reads every layer of the compute stack — from silicon to scheduler — and tells you exactly which job is on which GPU, whether it's doing real work, and what it costs. All on-premise. No SaaS. No cloud dependency.

Cognit is a GPU intelligence layer that sits on your existing cluster — reads every signal from kernel to BMC — and tells you, in plain English, why jobs run slow, which GPU-hours are wasted, and what that costs. On-premise. No cloud. No rip-and-replace.
India-built No outbound telemetry NVIDIA + AMD
The 9-layer compute stack
Job / Workload
slurm sacct
Observe
🔧
Framework
torch profiler
Observe
Runtime / Serving
vLLM /metrics · partial
Observe
📦
Container
cgroup v2
Observe
🗂
Scheduler
squeue
Observe
Kernel (Linux)
eBPF agent
Cognit's layer
🖥
OS Userspace
DCGM · nvidia-smi
Observe
🔌
Firmware / BMC
Redfish · IPMI
Observe
Hardware
DCGM · IB · PDU
Observe
Products

One control plane.
Every layer of your cluster.

Cognit deploys inside your environment — on-premise, with no outbound telemetry — and connects to the tools you already run. No rip-and-replace. No data leaving your network.

Commercial · Edition 1
Observe
Visibility + efficiency
SaaS — agentless
Reads your existing Prometheus / DCGM. No agent to install. Connects in minutes.
Per GPU · on request
On-premise — full agent
All 9 telemetry bands including kernel eBPF. Zero data egress. No outbound telemetry.
Per GPU · on request
What's included
  • Every telemetry band fused — fd→GPU attribution, per-tenant usage
  • MFU vs util · io-wait (idle vs storage-starved)
  • DCGM / vLLM / scheduler correlation
  • Dashboards, alerts, and pattern detection library
  • GPU routing intelligence — NVIDIA, AMD, Intel
Sovereign Edition
Sovereign Platform
For national defence, government & regulated buyers
Where data sovereignty and provable per-job audit are mandatory — deployable in-country, from an independent vendor of allied / non-aligned origin, outside sanctioned or adversarial jurisdictions.
Proof of Audit — land
Scoped agent on 1–2 nodes, capped at 8 weeks → per-job audit trail + data-movement report + compliance-gap assessment. 100% creditable to year 1.
$10,000 creditable
Sovereign Platform — annual
Behavioral audit + data-movement / USB / egress tracing + forensic query + retained audit trail + all compliance frameworks + offline CVE bundles + agent. All bundled.
Scoped per site · on request
What a dashboard cannot give —
  • Per job & per user: which datasets were read, what left the node, where it went
  • USB / removable-media events — no metric stack records this
  • Jobs behaving unlike their declared workload — forbidden paths, novel egress
  • Append-only queryable audit trail · configurable retention + forensic reconstruction
  • Every signal attributed at the kernel — deployed in your jurisdiction
⚡ Engagements begin with a paid scoping study, credited in full against the first audit.
Connects to your existing stack —
Slurm
Kubernetes
Prometheus
DCGM
vLLM
Triton
Redfish / IPMI
Lustre / NFS
InfiniBand
Terraform
Ansible
Pulumi
NVIDIA DCGM
AMD MIGraphX
OpenVINO
Warewulf / xCAT
What's inside

Every module. One screen.

From cluster health to compliance score to inference runtime — all visible, all governed, all on your hardware.

Workloads
Dashboard
Cluster health
GPU utilisation, active nodes, PCIe throughput, ECC errors, active patterns, node status, and recommended actions — in one executive + ops view. Answers three questions: where am I wasting GPU time, am I compliant, are my models healthy.
Patterns
17 detectors
17 pre-built detection patterns across scheduling, memory, thermal, hardware, storage, network, jobs, and accounting. Ships ready — no configuration. Each pattern is status-tracked: ACTIVE / WARNING / CLEAN / PENDING.
Jobs
Kernel attribution
Role-scoped job view with Kernel Attribution — which job is driving or starving each GPU at the kernel level. Pramaan Trust Score per job — per-workload scoring at admission. In development.
Job Analytics
GPU heatmap · MFU
GPU utilisation heatmap by partition and hour. Partition efficiency summary with MFU% — the real model-FLOP utilisation number your CFO wants.
Scheduler
Herd-intelligent
Herd-Intelligent Job Placement — nodes scored via live ECC history, PCIe health, and NCCL herd patterns before every job submission. Flags degraded hardware before submission — advisory today, enforcement in development.
GPU Routing
NVIDIA · AMD · Intel
Automatic workload-to-GPU routing by job class. LLM inference → HBM2e. GROMACS → AMD MI210 (74% faster). CUDA training → RTX Ada. ECMP elephant flow analysis and per-port queue depth for fabric health.
Intelligence
Inference Runtime
vLLM · Triton · on-prem LLM
Designed to bind your in-perimeter model and surface which model runs on which GPU, at what speed — TTFT, ITL, KV-cache%, queue depth. Runs against your own in-perimeter model, bound at deployment. Sovereign Stack: OpenVINO, AMD MIGraphX, Intel Habana. Zero cloud calls.
Consciousness
RAG · IaC · audit-grade
Governance findings aligned to ISO 42001 and NIST AI RMF. Multilingual Knowledge Base (EN/HI/GU/TA/TE) with per-PI audit-signed RAG. IaC Connector showing live Terraform, Ansible, and Pulumi drift.
Digital Twin
What-if · fault injection
Simulate cluster changes before committing them. Adjust GPU load, nodes, queue size, and memory pressure. 85% confidence scoring. Fault injection: node failure or degraded NIC. Recommended actions generated automatically.
Infrastructure
Nodes
Hardware registry · CMDB
Complete per-node hardware visibility: CPU, RAM, GPU (utilisation, VRAM, temp, power, PCIe TX, ECC, CUDA driver), storage, network, BMC. Living hardware registry — the CMDB your cluster never had.
Topology
1,000-node canvas · RDMA
1,000-node canvas map with rack-level drill-down. RDMA latency matrix: microsecond pairwise GPUDirect measurements. Five view modes: Cluster, Rack, Node, GPU Util, Network.
Vulnerabilities
Offline CVE · signed bundles
Offline CVE scanning via signed .vulnbundle — designed for environments with no outbound connectivity. CVE × Pattern attribution. PDF export for compliance evidence.
Governance
Compliance Intelligence
Pramaan Score
Pramaan Score maps live infrastructure posture across 27 frameworks with live evidence — ISO 42001, NIST AI RMF, EU AI Act, DPDPA, CERT-In, SOC 2, GxP, MeitY, CAG, STQC. 179 prioritised fixes. Gap engine with "Show me how" remediation.
Finance Intelligence
₹/$ · kWh · kg CO₂
Your cluster cost ₹45,000 this month. 120 jobs delivered. 1% of compute was productively used. Energy and carbon per job. PI-variant reports with student anonymisation. Pramaan evidence PDF for grant reporting.
Admin & RBAC
13 roles · LDAP · PAM
13 user roles — admin, auditor, finance, pi-lead, cluster-engineer, researcher, supervisor, and more. LDAP-backed. All role changes are written to the hash-chain audit log.
9
Telemetry layers
Job → Framework → Runtime → Container → Scheduler → OS → Kernel → Firmware → Hardware
17
Detection patterns
Ships ready — scheduling, memory, thermal, hardware, storage, network, jobs, accounting
255
Probes producing verdicts
27 of 40 frameworks with live evidence · on-premise · zero cloud calls
13
User roles
admin · auditor · finance · pi-lead · researcher · supervisor · and more
Three solutions. One control plane.

Pick the problem you need to solve first.

Every Cognit deployment covers all three. These are the buyer entry points — where the pain is sharpest, the conversation starts.

Solution 01
Cluster Intelligence
See your GPU cluster as it actually is.
😰 Pain
Grafana shows node-level utilisation. DCGM shows process metrics. Nobody can answer: why is this training run 30% slower than last week? Or: which team's job is burning GPU time right now? Investigations take days.
🎯 Need
GPU telemetry correlated from the Slurm job layer all the way down to kernel scheduling and NVLink state — in one dashboard, without building a fragile DIY stack or sending data to a cloud vendor.
✨ What Cognit delivers
eBPF kernel instrumentation traces syscalls and scheduling events the kernel sees — not what a user-space exporter guesses. Every anomaly is correlated to a job, a user, a node, and a timestamp. MTTD drops from hours to seconds. Root cause is one dashboard click, not a war room.
9
Telemetry
layers read
<5s
Metric
latency
0
Cloud
calls
Who it's for MLOps Engineer · Infra Lead · Platform Team · HPC Centre Manager · AI Programme Director
Solution 02
Cluster Efficiency
Attribute every GPU-hour. Recover the waste.
💸 Pain
Finance asks for per-project GPU spend. The answer is a Slurm node-hour report nobody trusts. Teams over-provision jobs "just in case." Orphaned allocations sit idle for days. The CFO wants a number — not a cluster config dump.
🎯 Need
GPU-hours attributed to cost centres at cgroup granularity — using kernel-verified compute, not DCGM's inflated utilisation estimate. Finance-ready, not a raw Prometheus query that only the MLOps lead understands.
✨ What Cognit delivers
Every GPU-second mapped to a Slurm job, user, cgroup, and cost centre — using kernel-verified idle detection. Chargeback reports export to CSV or your finance system. Right-sizing recommendations backed by 9 layers of evidence. We model the payback against your own utilisation data before you commit.
~30%
Typical GPU
headroom found
13
User roles
supported
₹0
Cloud
tax
Who it's for CFO / FinOps Lead · AI Programme Director · Research Head · Grant Manager · HPC Centre Manager
Solution 03
Cluster Governance
Sovereign AI compliance — detected continuously, explained on-premise.
😰 Pain
Every compliance tool requires sending GPU telemetry to a third-party SaaS dashboard — violating the exact data residency obligations the CISO is trying to prove. Manual audit prep takes 6–8 weeks and still misses kernel-level drift events.
🎯 Need
Continuous governance monitoring — mapped to DPDP-India, ISO 42001, MeitY, and other frameworks — running entirely within your physical boundary. Findings explained in plain language, not raw log dumps, to be board-presentable.
✨ What Cognit delivers
Every telemetry layer, every compliance check, and every LLM-generated finding explanation runs entirely within your perimeter. The Pramaan Score™ — a live composite governance metric across 10 frameworks — is updated continuously. Audit evidence packages export in hours, not weeks.
10
Compliance
frameworks
179
Unique fixes
mapped
0
External
API calls
Who it's for CISO · Data Protection Officer · Compliance Lead · AI Programme Head · Board Risk Committee
Capabilities

Everything your cluster needs.
Nothing it doesn't.

Ten capabilities. One agent. Deployed in your environment — not ours.

See the Truth
Kernel-level attribution ties every GPU cycle back to the exact job and tenant. What DCGM, Slurm and Prometheus don't surface by default, Cognit correlates.
fd→GPU · per-tenant · io-wait
Know the Cost
GPU-hours wasted, energy and carbon (modelled), and rupee cost — attributed per job, per user, per team. Your CFO finally gets a straight answer.
₹/$ per job · kWh · kg CO₂
Stay Compliant
Pramaan Score maps your live infrastructure posture across ISO 42001, NIST AI RMF, EU AI Act, DPDPA, CERT-In, SOC 2, GxP and more — with 179 prioritised fixes.
27 of 40 frameworks · live evidence
Stay Sovereign
Fully on-premise. No outbound telemetry. Offline CVE bundles with hash-chain integrity verification. No foreign-jurisdiction cloud dependency. India-built, independent vendor of allied origin.
On-premise · CERT-In · DPDPA · Make-in-India
Detect Failures Early
17 pre-built detection patterns across scheduling, memory, thermal, hardware, storage, network, and accounting. Ships ready — no configuration required.
17 patterns · 8 categories · auto-detect
Govern Workloads
Per-job Pramaan Trust Score is designed to flag workloads at admission. In development — declaration capture and drift detection ship today. Declarations drive policy; the kernel agent drives proof.
Trust scoring · workload gating · hash-chain audit
Pattern Detection Library
From GPU Idle Despite Queue to InfiniBand Link Degradation — each pattern is independently tracked, status-flagged (ACTIVE / WARNING / CLEAN), and linked to causal explanation.
P1–P17 · scheduling to network
GPU Routing Intelligence
Recommends the right GPU for each workload by job class — LLM inference to HBM2e, CUDA training to CUDA-optimised silicon. NVIDIA, AMD, and Intel in one plane.
Workload routing · NVIDIA + AMD + Intel
Compliance Intelligence
Not a checklist — a live gap engine. Each gap shows which frameworks it closes, which controls it satisfies, the exact remediation step, and API endpoints to verify after the fix.
Gap engine · cross-framework · Show me how
Built for Your Requirements
Every cluster is different. If your environment needs a capability Cognit doesn't ship today — a custom integration, a specific compliance framework, a hardware connector, or a bespoke reporting module — we build it with you.
Custom integrations · bespoke modules · your stack · your rules
Custom scheduler connector
Proprietary hardware telemetry
Bespoke compliance framework
Custom reporting & dashboards
Internal audit format export
Your use case →
Tell us what you need →
What your current stack misses

Three ways dashboards lie. Every day.

Your GPU monitoring stack gives you device-level aggregates. It cannot tie kernel-level evidence back to the job or the tenant. That gap is the whole problem.

util% = useful work
→ MFU tells the truth
A GPU pinned at 95% can be spinning, waiting on a NCCL collective, or KV-cache thrashing. DCGM sees the chip is busy. It does not see whether the work is real. MFU — model FLOP utilisation — does.
idle% = available
→ io-wait tells the truth
A GPU at 20% during AlphaFold's MSA phase isn't free — it's blocked on Lustre reads. Reclaiming it would kill the job. Only kernel io-wait tells idle apart from storage-starved.
shared = attributed
→ kernel fd-table tells the truth
On a shared, MIG, or opaque GPU, who is actually holding the allocation? Only the kernel's file-descriptor table names the tenant. No downstream Prometheus join can reconstruct it once the aggregate is emitted.
Use Cases

What the kernel layer
Changes in Practice

Three real workloads where the standard stack gives you a number. Cognit gives you the reason.

USE CASE 01
vLLM inference — TTFT spiking
DCGM: 90% util. Problem unseen.
KV-cache is full. vLLM is preempting and swapping. The GPU looks busy because it is — doing the wrong work. Kernel attribution names the tenant causing the evictions. Add capacity or evict the right job. Don't trust the util gauge.
USE CASE 02
AlphaFold — the GPU that looks idle
DCGM: 20% util. Operator reclaims it.
MSA search is running: massive sequential reads from BFD and UniRef on Lustre. The GPU is waiting on storage. Kernel io-wait proves it's blocked, not free. The fix is the data path — not handing the GPU to another job.
USE CASE 03
Isaac Sim — right GPU, wrong silicon
Scheduler: GPU assigned. Job runs.
The sim runs on any GPU but quietly collapses on cards without RT cores. An A100 has none. Cognit gates placement on RT-core capability and surfaces sim-on-sim contention via per-tenant context-switch monitoring.
Pramaan Score

Governance that's
measured, not claimed.

One number. Forty frameworks, twenty-seven with live evidence. Live infrastructure evidence — not self-assessment checkboxes. Every gap ranked by cross-framework impact with a clear remediation path.

NC
Governance policy document missing. Closes 10 controls across CERT-In CI-5.1, DPDPA 1.2/1.5, EU AI Act AIA-2.1, ISO 42001, NIST AI RMF.
OFI
No CVE scan recorded. Ingest a signed .vulnbundle and run scan. Closes CERT-In CI-2.1, NIST MAN-3.1.
C
System operations monitoring — Conformant. Hash-chain audit log + Prometheus telemetry + vulnerability intake all verified.
27
/40
Frameworks with live evidence · ISO/IEC 27001:2022 certified
A panel reads “measured” only when the payload carries an explicit synthetic:false from a real source — a failed or empty read renders “unavailable”, never a green badge. Demo-safe mode is an allowlist, not a denylist. Panels currently modelled rather than measured: energy and inference. A qualified auditor must review before reliance.
FRAMEWORKS COVERED
ISO 42001
NIST AI RMF
SOC 2 TSC (mapping)
EU AI Act
DPDPA 2023
CERT-In 2025
GxP / 21 CFR 11
MeitY 2025
CAG IS Audit
STQC AI QA
179 unique fixes · 40 frameworks · ranked by impact
How we engage

Start with a fixed-scope study.
Everything credits forward.

We don't ask for a platform commitment before you've seen your own numbers. Each step is fixed scope, fixed price, and credited in full against the next — so the only thing you risk is the first one.

Step 01 · 1–2 weeks
Scoping Study
You send metrics. We tell you where the waste and the risk are — before anything touches your environment.
£2,500 fixed
SGD 4,000 · €3,000 · credited in full against Step 02
  • Export from Prometheus, DCGM or Slurm accounting — a screenshot is enough to start
  • Written findings: where GPU-hours are going and which patterns are firing
  • Indicative recoverable headroom for your cluster
  • No agent, no access, nothing installed
Start here
Step 03 · scoped per engagement
Full Audit
The complete governance engagement across your estate, ending in a certification-body-style work paper.
from £15,000
Scoped to cluster size and framework set · Proof of Audit credited
  • Full control catalogue — 40 frameworks, 27 with live evidence today
  • Ranked remediation with cross-framework impact and the API endpoint to re-verify
  • Auditor work paper with per-control provenance — live evidence vs attached document
  • Findings the platform did not measure are marked not assessed, never scored
Talk to us
AFTER THE AUDIT — ONGOING PLATFORM

Continuous observability and the compliance plane, deployed on your hardware — Observe for attribution and efficiency, Govern for the full compliance plane. Licensed per GPU, annually, and scoped to your cluster. Pricing on request — we quote it against what the audit found, not from a table.

Volume tiers apply above 64 GPUs and aggregate across clusters. Sovereign and defence deployments are scoped separately.

How it Works

From first conversation
to full visibility — in days.

No big-bang deployment. No data leaving your perimeter. We start with what you already have and show you value before asking for anything more.

01
Starting point — interchangeable with 02
Share your cluster metrics
Tell us what you're running — GPU count, scheduler, utilisation numbers, job queue length, anything you have. We analyse it and tell you exactly where the waste or risk likely is before a single line of code touches your environment.
→ Export from Prometheus, DCGM, or Slurm sacct
→ Even a screenshot is enough to start
→ We identify the pattern, you confirm the pain
02
Starting point — interchangeable with 01
See it live on our cluster
Not slides. Not a recorded demo. A live walkthrough on Cognit's own GPU cluster — live BMC and GPU telemetry, and a live Pramaan Score against a real control catalogue. You see exactly what the platform looks like on real hardware. Energy and inference panels are modelled today, and labelled as such.
→ 30-minute technical walkthrough
→ Kernel attribution, cost breakdown, compliance score — all live
→ Bring your MLOps, infra, or compliance team
03
The proof point
Observability on your cluster — read-only
Cognit connects to your existing stack — Prometheus, DCGM, Slurm — in read-only mode. No agents on compute nodes to start. No data leaves your cluster at any point. Your team sees their own jobs, their own GPUs, their own cost and compliance posture — for the first time, at the kernel level.
→ Connects to your existing Prometheus / DCGM in minutes
→ Zero writes to your cluster · zero data egress
→ First Pramaan Score within 24 hours of connection
→ Full on-premise agent deployment only if you choose to proceed
Steps 01 and 02 can happen in either order — or simultaneously if you prefer to see the demo while we review your metrics.
Architecture

Cognit reads every layer.
Most stacks skip the kernel band. That's where we start.

Most stacks bolt one tool per band and call it coverage. The runtime and device ends are well-observed. The kernel — where causation actually lives — is a blind spot.

Layer
What it tells you
Standard tool
Coverage
Job / Workload
State, runtime, GPU alloc, exit code
slurm sacct · k8s
Partial
Framework
Loss curves, tokens/s, samples/s
torch profiler
Partial
Runtime / Serving
TTFT · ITL · KV-cache% · MFU
vLLM /metrics · partial
Partial
Container
Image provenance, cgroup pressure
cgroup v2
Partial
Scheduler
Queue wait, placement, GRES allocation
squeue · kube-sched
Partial
Kernel (Linux)
io-wait · fd→GPU map · per-tenant attribution · declared-vs-actual behaviour
eBPF — Cognit's layer
★ Cognit
OS Userspace
Device counters, NCCL events, dmesg
DCGM · nvidia-smi
Partial
Firmware / BMC
Power, thermal, fan, PSU, ECC
Redfish · IPMI
Partial
Hardware
GPU util%, temp, NVLink, IB counters
DCGM · IB · PDU
Partial
Who We Serve

Built for people who run real clusters.
Not cloud-credit holders.

If you own the hardware, share GPUs across teams, and have ever wondered where the hours actually went — this is for you.

☁️
GPU Clouds & Neoclouds
Per-tenant attribution is directly monetizable. Know which tenant holds which GPU, whether it's idle on I/O, and bill accurately.
→ Reclaim idle · bill precisely · enforce SLAs
🧬
Drug Discovery & Life Sciences
AlphaFold and molecular dynamics pipelines are I/O-starved by design. We prove it. 21 CFR Part 11 GxP audit trails built in.
→ GxP · HIPAA · idle ≠ free
🏛
National Defence & Govt
Sovereignty is a mandatory requirement, not a preference. On-premise deployment, signed offline bundles, no foreign-jurisdiction cloud dependency.
→ DRDO · C-DAC · CERT-In · DPDPA
🔬
Research & HPC Centres
Shared infrastructure, per-PI accountability, utilisation reports in 10 minutes instead of 3 weeks. Grant reporting that doesn't require a spreadsheet.
→ IIT · IISc · C-DAC · NSM clusters
Not for you if —
You call a hosted LLM API · you run a single-user box · you use managed serverless GPUs · your infrastructure is 100% hyperscaler-managed. No shame — just not the problem we solve.
About

Built in India.
Sovereign by design.

Cognit is an India-based infrastructure software company building the governance and observability layer for on-premise AI compute. We exist because the organisations that need this most — national labs, defence programs, research institutions, regulated enterprises — cannot use foreign-jurisdiction SaaS tools, and no one was building for them.

We fund the deep-tech build with a working services practice — embedded systems, cloud communications and cybersecurity — which delivered and collected ₹13.3 lakh in FY 2024-25. Self-funded, debt-light and audited. We move carefully, we say what we mean, and we build in public where we can.

🇮🇳 Bangalore, India
Self-funded · revenue-backed
On-premise · No egress
Independent · non-aligned origin
REGISTERED & CERTIFIED
DPIIT DIPP169103
ISO/IEC 27001:2022 QCC/E808/1025
Udyam MSME KL-10-0040651
2 patents filed
Two patents filed — cluster management via telemetry correlation, and boot-time firmware-tamper attestation.
9
Telemetry layers read — Job to Hardware
117
Live BMC sensors read — timestamped, per node
27/40
Frameworks with live evidence — of 40 in catalogue
255
Probes producing verdicts — 179 ranked fixes across 40 frameworks
Get in touch
sales@cognit.run
For pilots, partnerships, and early access conversations.
The Team

The people building this.

A working team, not a founder and a deck. Engineers who run the lab cluster, design the compute module and deploy on customer sites — with specialists who have shipped kernels, audits and HPC.

Krishnadas Puthukudy
FOUNDER
Capital, operations and long-term strategy. Previously Saudi Chevron.
Agnidipa Manna
CARRIER BOARD ENGINEER
Designs the COM-HPC carrier board — the indigenous compute module.
Sainath Reddy
LAB CLUSTER ENGINEER
Runs the Megh GPU lab cluster. FPGA and VLSI background.
Savin Sundar
FORWARD DEPLOYMENT ENGINEER
PMP-certified delivery lead. Multi-country ERP and SAP rollouts.
Yoshita Pacholy
OPERATIONS
Project delivery and programme management. Previously Xieno, Ericsson.
Parag Somani
STRATEGY
Technology investment banker and former founder.
Sandeep Krishna
CO-FOUNDER
Network engineering. Previously Telstra and Ericsson.
ADVISORS & SPECIALISTS
Bobby Mathews runtime, workload & kernel · Rust
Ebin Babu Linux systems
Saheb Mandal COM-HPC design verification
Paul D. vLLM & ML performance
Shruthi P Nair audit, compliance & cloud governance
Truong N. robotics · ROS2
Get Started

Ready to see your cluster
as it actually is?

Share a few details and we'll get back to you within 2 business days. No demo-bot. No automated funnel. A real conversation with the team.

30-minute technical walkthrough on our live cluster
We review your metrics and tell you where the waste is — before touching anything
Read-only pilot on your cluster · nothing leaves your network
First Pramaan Score within 24 hours of connection
No commitment required to get started

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or
sales@cognit.run

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