AI Systems Architecture
Most AI architecture fails not because the models are wrong, but because the systems around them — the pipelines, the routing, the failover, the governance — were not designed for production reality. I design AI systems that hold together when your user base grows 10× overnight.
What you walk away with
- ✓AI systems architecture document — agent graph, data flows, model routing, infrastructure
- ✓Reference implementation in Next.js / FastAPI with full test coverage
- ✓Multi-model routing strategy with cost and latency benchmarks
- ✓Data pipeline design — ingestion, chunking, embedding, retrieval
- ✓Governance and compliance framework — audit trails, access controls, data residency
- ✓Team knowledge transfer and architecture decision records (ADRs)
Engagement model
01
Current-State Review
Audit
1 week. I review your existing stack, identify architecture risks, and define the target state. Delivered as a written report with prioritised recommendations.
02
Architecture Blueprint
Design
2–3 weeks. Full architecture document — system diagrams, component specs, technology decisions, and a phased delivery roadmap.
03
Reference Implementation
Build
8–20 weeks. I build or oversee the build of the core system — with your team or as the lead engineer depending on your preference.
04
Fractional Architecture
Embed
Optional. Ongoing monthly retainer for architecture governance as your system scales and your product evolves.
Recent case study
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Frequently asked questions
- What is an AI systems architect?
- An AI systems architect designs the technical infrastructure that makes AI applications work reliably at scale — the data pipelines, model routing, agent orchestration, observability, and governance layers. Unlike ML engineers who train models, AI systems architects design the production systems that deploy and operate those models.
- When do you need an AI systems architect vs a general software engineer?
- You need an AI systems architect when your AI product has reached a stage where reliability, performance, and governance matter — typically when you have early production users or are preparing for a Series A. General software engineers are excellent for feature delivery; AI systems architects are needed for the infrastructure that underpins it.
- Do you work with any AI infrastructure stack?
- Yes. I am infrastructure-agnostic: AWS Bedrock, Vertex AI, Azure OpenAI, self-hosted models via vLLM or Ollama, and hybrid approaches. I will recommend what is right for your scale, compliance requirements, and cost envelope — not what is fashionable.
- How do you handle compliance and data residency requirements?
- Compliance is designed in from the start: data classification, access controls, audit logs, PII handling policies, and model output logging are all first-class concerns in every architecture I produce. I have shipped AI systems that passed SOC2 and GDPR requirements without architectural rework.
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Ready to start a AI Systems Architecture engagement?
Book a 30-minute strategy call. I will map out a scope and timeline specific to your situation.