Stop piloting AI.
Start Scaling It.
/01 THE SCALING PROBLEM
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Governing the Ungoverned
You’re already accountable for AI initiatives running in your organisation that you don’t know about. Shadow AI is growing faster than any governance framework can keep up with.
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The bottom line
Those challenges don’t go away by running more pilots. They go away by building the right platform. And that’s where our solution comes in.
The cost of inaction is well-documented. McKinsey’s “Seizing the Agentic AI Advantage” (June 2025) identifies what it calls the “gen AI paradox”: nearly 78% of companies have deployed gen AI in some form, yet roughly the same percentage report no material impact on earnings. The root cause is consistent, high-impact, function-specific use cases rarely make it out of the pilot phase due to technical, organisational, data, and cultural barriers. McKinsey finds that 90% of vertical AI use cases remain stuck in pilot stages. Nearly two-thirds of enterprises have experimented with agents, but fewer than 10% have scaled them to deliver tangible value.
Forrester is equally direct: 60% of enterprise AI projects will fail to scale without proper governance frameworks, and 75% of firms that attempt to build agentic architectures on their own will fail (Forrester Predictions 2025: AI). McKinsey adds that 80% of companies cite data limitations as the single biggest roadblock to scaling, directly compounding governance risk. The platform addresses each of these failure modes by design: governance inherited at runtime, data boundaries enforced at the identity layer, and full observability from day one.
Sources: McKinsey, “Seizing the Agentic AI Advantage,” June 2025; McKinsey, “Building the Foundations for Agentic AI at Scale,” April 2026; Gartner press release, June 2025; Forrester Predictions 2025: Artificial Intelligence.
The traditional approach to AI deployment creates technical debt by design: each team builds its own guardrails, each agent needs its own monitoring, each use case requires its own security review. The result is a sprawling portfolio of fragile, ungoverned AI — exactly the shadow IT problem that governance teams fear.
The Brighting Agentic AI Platform, built on AWS AgentCore, inverts this model. Instead of each agent reinventing the wheel, a shared platform layer provides every capability every agent will ever need — governance, identity, tooling, observability, and infrastructure — inherited automatically at runtime.
Deploy the platform once. Every agent you build — today, next quarter, and next year — automatically inherits current and future platform capabilities. Your governance investment compounds over time, not linearly per agent.
Concretely, this means you can finally say yes where you previously had to say no:
The platform serves as a central orchestration layer through which all agent interactions are routed and governed. It supports three types of agents — each with different build complexity, maintenance requirements, and ownership models:
Tier 1
Low-Code Agents
Tier 2
Custom-Built Agents
Tier 3
External SaaS Agents
The core principle
For retail and omnichannel organisations, the platform unlocks a category of AI use cases that cannot be safely operated in isolation: inventory and replenishment agents that act on live ERP data, personalisation agents with access to customer profiles, and order management agents that span OMS, WMS, and fulfilment systems. Each of these requires exactly the governance, identity scoping, and audit capability the platform provides — deployed once, inherited by every agent.
Brighting holds the AWS Retail Competency — one of a small number of firms globally to do so — and combines this with deep Composable Commerce and MACH architecture expertise. For retail and CPG enterprises already on a headless or composable journey, the Agentic AI Platform is a natural extension: agents that orchestrate across your commerce stack, governed by the same platform that governs everything else.
The platform is deployed as Terraform Infrastructure as Code (IaC) into the customer’s own AWS account. It carries a perpetual license and is designed with zero vendor lock-in. The architecture provides eight core capabilities:
Cost & Allocation
Per-department and per-agent token spend tracking, budget alerts, and model cost attribution.
/04 FOUR PILLARS
Governance
Access Control
Infrastructure
Observability
Value Assessment
Platform Design
Phase 4
Core Team Alignment
/06 MANAGED SERVICES
Once implemented, you have a choice: manage the platform with your own team, or retain Brighting for ongoing optimisation, monitoring, and incident response. All managed service packages include the same foundation:
- Platform updates and improvements
- Platform monitoring — cost, uptime, performance, anomalies
- SLA on incident response time
- Incident triage and escalation management
Support Tiers
| BASIC | PRO | ENTERPRISE | INCLUDED IN ALL | |
|---|---|---|---|---|
| Coverage | 8/5 9:00–17:00 CET | 15/7 7:00–23:00 CET | 24/7 / 365 | Platform updates |
| Response Time | 90 min | 60 min | 60 min | Monitoring |
| Agents Included | 0 (BYO) | 5 | 10 | SLA + Incident triage |
| Best For | Controlled rollout | Production workloads | Mission-critical AI | Escalation management |
The Basic tier is designed for organisations rolling out cautiously with daytime coverage. Pro suits production-grade workloads with extended-hours support. Enterprise provides mission-critical 24/7/365 coverage for organisations where AI agents are core to operations.
Why Managed Services Matter for Agentic AI
Agentic AI introduces operational challenges that traditional software managed services do not address. LLM providers release new model versions on cycles of weeks, not years — each requiring regression testing against your agents’ behaviour before adoption. Prompt drift is a real phenomenon: agent performance can degrade silently as underlying model behaviour shifts without any change to your own code. And a new MCP tool misconfiguration can expose internal systems to unintended agent access within a single deployment.
Brighting’s managed service is built for this: continuous monitoring of agent output quality and cost efficiency, proactive model version management, and a team that understands your platform architecture from day one because we built it. For organisations where AI agents are becoming operationally critical, managed services are not optional infrastructure, they are the difference between a platform that compounds in value and one that quietly degrades.
Commercial Model
The commercial model mirrors the platform logic. There are two components: a fixed monthly platform fee covering platform updates and improvements, platform monitoring (cost, uptime, performance, anomalies), SLA on incident response time, incident triage and escalaltion — and a variable per-agent fee that decreases as your agent portfolio grows. The more agents you run on the platform, the lower the cost per agent. The platform becomes more valuable and more economical at the same time.
This structure directly addresses the cost compounding problem McKinsey identifies in ungoverned AI portfolios. Rather than each new agent carrying its own governance, security, and monitoring overhead, those costs are absorbed by the platform layer and shared across every agent that runs on it. The per-agent fee covers operational oversight only — not rebuilding the foundation each time. Detailed pricing is available on request.
/07 PRICING & PACKAGING
The platform is structured in three components, each independently scoped:
Setup
One-off setup and configuration — delivered fixed-price:
- Agentic AI Platform deployment
- Orchestration agent
- Identity integration configuration
- Infrastructure as Code (Terraform)
- Knowledge base setup
Managed Service
- Service desk — 9/5, 15/7, or 24/7
- Continuous optimization
- Feature add-ons
- Performance monitoring
- Standard service requests
What sets us apart for Agentic AI engagements:
- HEMA & ASICS – We are currently deploying the Agentic AI Platform for leading Dutch and Global retailers enabling governed AI agents across merchandising, customer service, and supply chain.
- AWS AgentCore specialists — We have built our platform natively on AWS AgentCore — not retrofitted on top of it.
- Senior engineers only — Our Novi Sad team is 35 senior engineers (backend, frontend, data, AI) — all AWS and/or Azure certified.
- Boutique speed, enterprise rigour — We move at scale-up pace with enterprise-grade security and compliance built in from day one.
- No lock-in by design — Terraform IaC, perpetual license, your AWS account. You own everything we build.
- Proven with enterprise clients — Current clients include HEMA, ASICS, ANWB, Royal FloraHolland, and MS Mode, plus scale-ups including Milence, Treatwell, and Quatt.
For the Head of Data & AI
This whitepaper is written for you as much as your CTO. The platform directly addresses your three core problems: getting AI initiatives into production faster, proving ROI to the board with attribution data, and maintaining technical control over a growing portfolio of agents built by teams across the organisation.
Where many AI leaders are spending 60–70% of their time on governance, compliance reviews, and firefighting fragmented pilots, the platform shifts that ratio. Governance is handled at the platform layer. You spend your time on use case delivery, not infrastructure and policy. The 30-minute value assessment starts with your AI portfolio, not an architecture blueprint!
Ready to scale?
Once implemented, you can either manage the platform internally or choose for us to provide ongoing support, optimisations and monitoring. This includes:
Don’t just take our word for it.
Check our selected case studies.
Let’s start with a quick value assessment.
Introduction Meeting