AI Systems
AI Systems Architecture for SaaS Products
I design AI capabilities as platform infrastructure — predictable, secure, observable, and cost-aware. Not demos. Production systems.
Typical outputs
A practical AI architecture blueprint your team can implement and operate reliably.
- LLM integration reference architecture
- Agent orchestration and workflow design
- Cost & token governance model
- Security controls for AI endpoints
- Evaluation + observability plan (quality, latency, cost)
When AI becomes expensive, unreliable, or risky
Most AI initiatives fail not because models are weak, but because product teams treat AI like a feature. In production, AI needs architecture: governance, safety, observability, and cost control.
Unpredictable costs
Token usage grows silently, latency increases, and budgets become impossible to forecast without governance.
No reliability baseline
Quality fluctuates, prompts drift, and regressions happen without evaluation and monitoring pipelines.
Security and abuse exposure
Prompt injection, data leakage, and abuse patterns appear when AI endpoints lack policy enforcement and controls.
AI features don’t scale
A prototype works for one workflow, but the product needs reusable patterns for multiple use cases and teams.
What I deliver
The output is an implementable architecture — patterns, boundaries, and rollout steps. Your team gets a clear path from prototype to production AI.
LLM Integration Architecture
A robust integration layer for LLM-backed features inside your product.
- Provider strategy and abstraction (multi-provider ready)
- Prompt management and versioning approach
- Caching, batching, and latency optimization patterns
- Data handling and privacy boundaries
Agent & Workflow Orchestration
Agent systems designed as workflows you can reason about and maintain.
- Agent responsibilities and boundaries (avoid tool sprawl)
- Orchestration design (queues, schedulers, step functions)
- Tooling interfaces and safe execution model
- Human-in-the-loop checkpoints where needed
Governance, Safety & Security Controls
Define enforceable policies for AI endpoints and data access.
- Security model for AI APIs (auth, scopes, policy enforcement)
- Input/output filtering and risk controls
- Abuse prevention, rate limits, quotas, and auditability
- Tenant isolation and sensitive data handling
Evaluation & AI Observability
Make AI measurable: quality, cost, and reliability become visible.
- Offline evaluation set design and regression testing
- Quality metrics and acceptance criteria per use case
- Operational monitoring (latency, cost, failure modes)
- Feedback loops for continuous improvement
Engagement models
Most teams start with an architecture sprint, then move to advisory during implementation.
AI Architecture Sprint (2–4 weeks)
Assess current state, define target architecture, and produce a rollout plan.
Output: blueprint + governance + implementation plan
Advisory Retainer
Ongoing guidance as your team ships features and operationalizes AI.
Output: reviews, decisions, guardrails, escalation path
Implementation Oversight
Hands-on supervision to reduce risk and ensure delivery quality.
Output: alignment, quality gates, and risk reduction
FAQ
Is this only about OpenAI integration?
No. The focus is architecture: patterns and governance that work across providers. Provider choice is an implementation detail — the system design is the long-term asset.
Can you help us move from prototype to production?
Yes. That’s a common engagement. We add evaluation, observability, security controls, cost governance, and reusable integration patterns.
How do you control LLM costs?
By introducing token governance, caching and batching patterns, model routing, and usage visibility. Cost becomes measurable and enforceable.
Do you design agentic systems?
Yes — but with discipline. Agents should be workflows with clear boundaries, safe tool execution, and observability, not an unstructured 'AI magic' layer.
Build AI capabilities you can operate and scale
If you want AI features that are reliable, secure, and cost-aware — I can help design an AI architecture your team can ship and maintain.