AI Solution Architecture & Design
Complete solution architecture for an identified AI opportunity — technology selection, integration design, data strategy, team specification, and governance framework. Build-ready output an engineering team can implement from day one.
Engage when
- Leadership mandate to "add AI" with no technical plan
- completed assessment with a "proceed" verdict but no production path
- pilot stuck in purgatory with no production path
- competitor shipping AI features
- failed AI hire or wrong-vendor selection
- regulatory pressure to modernise
The engagement
The AI Solution Architecture & Design engagement produces a complete, build-ready solution architecture for an identified AI opportunity. In 2-3 weeks, you get the answer to every question that blocks production: exactly what to build, which technologies to use, how it integrates with existing systems, what team is needed, and how to govern it through delivery.
This is distinct from assessing existing AI architecture (the AI Architecture Diagnostic). This engagement designs new AI capabilities from scratch — producing a solution architecture, technology selection, integration design, data strategy, team specification, and governance framework that an engineering team can implement from on day one.
Every recommendation is placed within your existing enterprise architecture — application landscape, integration patterns, data architecture layers, and enterprise standards — so the AI capability fits the estate rather than creating a parallel system the organisation must reconcile later.
Process
Discovery & Requirements
Week 1
Solution Architecture & Design
Week 2
Organisational Design & Governance
Week 3
Discovery & Requirements
Week 1
Solution Architecture & Design
Week 2
Organisational Design & Governance
Week 3
Deliverables
- Solution Architecture Document
Complete technical architecture for the AI capability: system design, component architecture, integration patterns, data flows, technology stack selection with rationale, and deployment model
- Technology Selection Matrix
Structured evaluation of technology options against client-specific criteria: existing stack compatibility, regulatory requirements, team skills, cost, and scalability
- Integration Design
Enterprise integration architecture: how the AI solution connects to the existing application landscape, with cross-cutting concerns (identity, observability, data lineage, API strategy)
- Data Strategy & Pipeline Design
Data sources, ingestion patterns, embedding strategy, vector store design, retrieval architecture, with data quality, lineage, privacy (GDPR), and regulatory compliance
- Team & Pod Specification
Recommended team structure, roles, and skills required to implement the designed solution
- Implementation Roadmap
Phased plan from architecture to production, with sprint breakdown, dependencies, decision gates, risk register, and estimated effort
- Governance Framework
Enterprise architecture governance model for implementation phase: ADRs, architecture review board structure, standards compliance checks, decision rights, review cadences, quality gates
Who This Is For
Typical Buyers
CTO, VP Engineering, Chief Architect, Head of AI/ML, Head of Digital Transformation
Industries
Any enterprise with complex existing systems and identified AI opportunities. Domain depth in insurance, banking, and financial services
Why Sparkling Neuronics
- Architectures designed to be built by other teams — and they were. Sparkling Neuronics' founder spent 15+ years across a tier-1 strategy consultancy and a listed European digital services firm designing solution architectures that were then successfully implemented by separate delivery teams across insurance and banking. This is the standard enterprise architect operating model — the architect designs, a delivery team builds — and it is exactly what this engagement produces. He also built an AI agentic system from idea to production, scaling from 2-3 people to 15+ engineers, using the same technologies this engagement advises on (RAG, vector databases, multi-agent orchestration).
- Build-ready output. Deliverables are engineering documents, not strategy decks. An implementation team can start building from the Solution Architecture Document without a translation phase. Architecture decisions include rationale, trade-offs, and fallback options.
- Organisational design included. Not just "what to build" but "who builds it and how." Team specification, governance framework, and implementation roadmap address the organisational dimension that pure technical architecture ignores — and that causes the majority of AI project failures.
- Vendor-neutral, client-controlled. The architecture belongs to you. Build with an external delivery team, your internal team, or a third-party SI. No lock-in, no proprietary frameworks.
Part of these journeys
This engagement is a step in these playbooks. See the full plan if you want the longer arc.
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Ready to discuss AI Solution Architecture & Design?
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