Your hardware.
Your business data.
You retain ownership of the machines and business records. We define where data is processed and which external systems may connect.
INFRASTRUCTURE. INFERENCE. IN YOUR HANDS.
COGNIO LAB / 001Local compute. Carefully chosen models.
Useful AI, built around the work you do.
THE LOCAL INTELLIGENCE LOOPILLUSTRATIVE ARCHITECTURE · NOT LIVE TELEMETRY
MODEL PROCESSING STAYS LOCALCONNECT → INFER → REVIEW → IMPROVE
01 / THE PLATFORM · BUILT FOR SMBsEnterprise-grade ambition.
Explore the platform ↗Enterprise-grade ambition.
Small-business practicality.
Infrastructure you own.
We help small and medium-sized businesses deploy practical AI on their own NVIDIA DGX Spark or RTX-powered systems—combining hardware, optimized models, and business workflows with measurable results.
Your hardware.
A working stack.
Support around it.
Hardware sizing, installation, model optimization, workflow integration, training support, pilot sandboxes, and ongoing support scoped to your deployment.
Completed work.
Demonstrated value.
Track cost per accepted task, time saved, output quality, human review effort, and payback using your own business baseline.
Ownership gives you control over deployment and capacity. Lower costs and data sovereignty depend on the complete configuration, connected services, operating practices, and measured workload—not hardware ownership alone.
02 / BUSINESS IN MOTIONSmaller models.
Explore interactive use-case dashboards ↗Smaller models.
Meaningful work.
Designed around the everyday loops of a small or medium-sized business.
REVENUE OPERATIONSLess chasing.
Less chasing.
More moving forward.
Find unanswered quotes, surface missing requirements, and prepare the next follow-up for review.
WORKFLOW PREVIEWSYNTHETIC EXAMPLE
QUOTE / 0042REVIEW REQUIRED
An estimate is waiting.
No message or transaction is sent automatically.
03 / BUILT FOR YOUR REALITYYour data.
Your data.
Your decisions.
Your advantage.
Local by design.
Run model inference on your own infrastructure. Connect external business tools only where your workflow requires it, with explicit data boundaries.
Measure the work, not the hype.
Track completed tasks, correction rates, review effort, and time to result. Separate measured outcomes from projected savings.
Start focused. Earn the expansion.
Validate one workflow on representative records before adding more. Build the case for scale with evidence from your own business.
04 / MAKE THE NUMBERS WORKYour workflow.
Your workflow.
Your business case.
Model the value of time returned to your team. Compare local and cloud costs. Change every assumption.
PLANNING ESTIMATE · USD · ILLUSTRATIVE INPUTS, NOT PRICES OR MEASURED RESULTS
YOUR LOCAL AI ESTIMATEBROWSER ONLY
Enter a valid number in every field within the stated limits to calculate results.
Estimated net monthly cash benefit
Net hours freed / month
Capacity value / month
Simple payback
First-year cash ROI
Manual work
With AI + review
| Measure | Local | Cloud |
|---|---|---|
| Upfront investment | ||
| Operating cost / month | ||
| Total first-year cost | ||
| First-year net cash benefit |
Nothing is uploaded or stored. No email required.
How we calculate this—and what a pilot must validate
Baseline hours = records × manual minutes ÷ 60. Hours with AI = records × [(1 − automation share) × manual minutes + automation share × review minutes] ÷ 60. Net hours freed = baseline − hours with AI; negative values mean more work.
Capacity value = net hours freed × hourly cost. Estimated cash savings = positive capacity value × cash conversion percentage. Added labor, when present, is charged at its full value. Net monthly cash benefit = estimated cash savings − local monthly costs. Payback = upfront investment ÷ positive net monthly cash benefit. First-year ROI = (12 × cash savings − upfront − 12 × operating costs) ÷ (upfront + 12 × operating costs). Undefined results are shown explicitly.
Conservative: 80% of the entered automation share and 125% of review time. Expected: entered assumptions. Optimistic: 120% of automation share, capped at 100%, and 75% of review time. These are sensitivity scenarios, not probability forecasts.
Local and cloud assume identical workload quality and handling time for cost comparison only. A pilot must verify throughput, accuracy, review effort, capacity, and equivalent configurations. This estimate excludes ramp-up, downtime, financing, taxes, revenue uplift and residual hardware value; it is a steady-state planning model, not guaranteed savings.
What could your
infrastructure do?
Define the workflow. Choose the constraints.
Make a plan you can measure.