marp: true theme: warble-design-system class: lead

Frakma / Warble

Financial predictability in the AI Era

CFO & Financial Leadership Pitch

The Cost of Reliability Today

Human Capital Costs: Engineering and SRE talent is expensive. Spending their time on reactive firefighting instead of product development is a massive opportunity cost.
SLA Penalties: Extended downtime (High MTTR) directly impacts revenue and customer retention.
Tooling Sprawl: Paying per-host or per-metric pricing for monitoring tools creates unpredictable, exponential cost growth.

The "Frontier-Model Tax"

AI promises to solve operational burdens, but existing AI-ops solutions rely on third-party frontier models (like GPT-4).
The Problem: You pay exorbitant recurring costs tied to token usage and inference.
It is an unpredictable Opex line item that scales linearly with the number of incidents and logs.

Opex vs. Inventory Framing

Legacy & Proprietary AI: Scales on your data/inventory. The more you monitor, the more you pay. Unpredictable Opex.
The Warble Approach: Scales on inference tokens—a knob you can control and budget. We provide the intelligence; you control the compute.

Avirka: Economics of Open AI

10x Cheaper: Avirka delivers GPT-4 class reasoning for operations at a fraction of the cost.
Hardware Efficiency: Optimized to run on a single H100 GPU.
Predictable Spend: By running the model natively or via our managed cloud, your AI spend becomes predictable and manageable, eliminating API bill shock.

ROI & Value Realization

77% MTTR Reduction: Modeled internal pilots show massive decreases in downtime.
99.6% Cost Reduction in AI Ops: Drastically lower token and compute costs compared to proprietary frontier models.
Asset Creation: "Incident memory" becomes a tangible, customer-owned asset, reducing onboarding costs for new engineers and protecting against turnover.

Let's Talk Economics

Transition from unpredictable alert costs to a flat, predictable intelligence layer.
Try our ROI and Fitment Calculator to see your exact savings.