EDGENTIC Augmented Intelligence [EdgenticAI]

Augmenting

Human and Artificial Intelligence for Real Time Business Outcomes

Converging human intelligence with AI on the Edge to deliver measurable business value.

Pricing Advisory

Augmented Intelligence is measured in outcomes.

For Customers

Adopters deploying AI on the Edge

You operate across Manufacturing, Retail, Utilities, Oil & Gas, Ports & Terminals and Smart Cities. You are evaluating AI models and Applied AI apps intended to run on your distributed network and you want to make sure the price matches the value.

We validate proposals, quantify ROI/TCO, and shape terms so you pay for outcomes, not complexity. We benchmark price bands against workload criticality and scale to help you know whether a service should cost $5, $15, $150, or $1,500 per month/node. We then document the rationale so finance, ops and leadership can all sign off with confidence.

What we do for you:

Business case validation: map benefits to KPIs, define payback, and quantify risk-adjusted ROI for AI and Edge initiatives.

Price assessment and market benchmarking: evaluate fairness vs. external benchmarks and internal value metrics; determine price bands by workload criticality and scale and document the rationale for stakeholders.

TCO modelling (12–36 months): model total cost including usage, overages, support, and scale-out; run scenario analysis.

Deployment-linked commercial terms: structure phased ramps, milestone billing, and performance-based renewals aligned to adoption.

Right-sizing consumption: align licenses and usage units (per device, per stream, per inference, per site) to actual demand and rollout stages.

Quote and contract review: analyse line items, usage assumptions, escalators, non-standard terms, and lock-ins before you sign.

Cost optimization levers: apply right-sizing, bundling, tier adjustments, committed-use discounts, multi-year structures, and cloud/edge cost alignment.

Multi-vendor rationalization: eliminate overlap and consolidate where it reduces TCO while maintaining capability.

Commercial negotiation support: prepare negotiation strategy, redlines, fallback positions, and approval paths to secure favourable terms.

Post-deployment value tracking: set up KPI baselines, usage telemetry, and savings reporting to validate ROI and inform renewals.

Typical Outcome:

Defensible spend, lower TCO, pricing terms aligned to adoption and outcomes.

For Vendors

ISVs building AI for the Edge

You build industry and cross‑industry AI applications from video analytics, drone operations, agentic AI assistants, predictive maintenance, and more; that are designed to run at the edge. You now need pricing and packaging that reflect real value, scale with adoption, and are clear and defensible for sales, finance, and leadership.

We design pricing and packaging that express real value across AI and Edge. We calibrate price bands to workload criticality, footprint, and scale, codifying clear consumption units (per device, per stream, per inference, per site), maturity‑aligned tiers, and enterprise‑ready terms that protect margin and accelerate adoption. We document the rationale so sales, finance, and leadership can operate with confidence.

What we do for you:

Pricing strategy workshops: align ICPs, value metrics, packaging, and commercial goals in focused sprints.

Define value metrics and consumption units tailored to AI and Edge (per device, per stream, per inference, per site).

Market benchmarking: position price levels, packaging, and terms against relevant peers and adjacencies.

Custom pricing models: value‑based, usage‑based, or hybrid; include subscription + usage structures for SaaS at the edge.

Packaging and tiering: editions, entitlements, and tiers designed for pilots, scale‑out, and multi‑site rollouts.

Enterprise and channel terms: SLAs, overage rules, ramp plans, partner margins, multi‑year incentives, and enterprise agreements.

Deal desk playbooks: discount guardrails, approvals, and handling of non‑standard terms to protect ARR and margin.

Unit economics and margin modelling: scenario analysis for usage variance, inference costs, edge hardware/ops, and COGS.

ROI/TCO assets: calculators and narratives your sales and solutions teams can use in the field.

Pricing governance: versioning, change management, and data‑driven quarterly refresh of prices and packages.

Typical Outcome:

Higher win rates, cleaner deals, stronger LTV/CAC, a pricing story your team can defend.

How It Works

Discovery: scope, goals, constraints, success metrics.
Modelling: value metrics, unit economics, tiers, scenarios.
Validation: benchmark against market and competitors.
Enablement: artifacts, playbooks, governance for scale.
Calibration & sign‑off: stakeholder alignment and final recommendation.

Core Deliverables

Pricing model and rationale (value metrics, tiers, rules).
ROI/TCO calculators and business case templates.
Benchmark snapshot and competitive landscape.
Deal playbooks (discount bands, approval thresholds).
Executive summary for internal alignment.

Engagement Options

Sprint (2–3 weeks): rapid assessment and recommendations.
Full build (4–6 weeks): pricing + packaging + enablement assets.
Deal/quote support (on‑demand): reviews, redlines, negotiation prep.
Quarterly refresh: benchmark update and model recalibration.

Impact Metrics

150+ projects delivered

Pilots, scale‑outs, and multi‑site rollouts across AI and Edge.

$17.2M infrastructure spend avoided

Right‑sizing, capacity deferral, and optimal placement.

29% cloud cost reduction

Compute, storage, and egress trimmed via workload placement and FinOps.

$3.6M network throughput savings

On‑device preprocessing and data minimization reduce backhaul/egress.

37% GPU cost reduction

Higher utilization (+22 pts), workload scheduling, and tiered accelerators.

+22% points GPU utilization

Higher occupancy from smart batching and placement.

99.96% on‑site inference availability

Distributed failover and local QoS keep decisions online.

Median payback in 11 months

Business cases that clear finance and renew on results.

18% average contract savings

Quote reviews, term fixes, and right‑sizing before signature vs. initial quotes.

28% operations efficiency gain

Fewer tickets, −25% MTTR, and reduced manual runbooks via edge automation.

45% faster time-to-decision

Latency reduced by running inferencing at the edge instead of round-tripping to cloud.

1,250 tCO2e avoided annually

Less backhaul and right‑sized compute improve ESG outcomes.

* Figures are indicative of aggregated engagements across Manufacturing, Retail, Utilities, Oil & Gas, Ports & Terminals and Smart Cities; detailed case studies available on request.