AI Systems
AI Systems Architecture for SaaS Products
I design AI capabilities as platform infrastructure: predictable, secure, observable, and cost-aware. Not demos, but production systems.
- LLM Integration
- AI Agents
- AI Governance
- Secure AI APIs
- Evaluation Pipelines
- Cost Control & Observability
Problems
When AI Becomes Expensive, Unreliable, or Risky
Most AI initiatives don't fail because the models are weak. They fail because AI is treated as a feature. In production, AI needs architecture: governance, safety, observability, and cost control.
Unpredictable costs
Token usage grows silently, latency creeps up, and without governance the budget is impossible to forecast.
No reliability baseline
Quality fluctuates, prompts drift, and regressions slip through because there is no evaluation or monitoring.
Security and abuse exposure
Prompt injection, data leakage, and abuse 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 many use cases and teams.
Deliverables
What I Deliver
An implementable architecture: patterns, boundaries, and rollout steps. Your team gets a clear path from prototype to production AI.
01
LLM Integration Architecture
A robust integration layer for LLM-backed features inside your product.
Includes
- Provider strategy and abstraction, ready for multiple providers
- Prompt management and versioning
- Caching, batching, and latency optimization
- Data handling and privacy boundaries
02
Agent & Workflow Orchestration
Agent systems designed as workflows you can reason about and maintain.
Includes
- Agent responsibilities and boundaries, without tool sprawl
- Orchestration with queues, schedulers, or step functions
- Tool interfaces and a safe execution model
- Human-in-the-loop checkpoints where needed
03
Governance, Safety & Security Controls
Enforceable policies for AI endpoints and data access.
Includes
- Security model for AI APIs: auth, scopes, policy enforcement
- Input and output filtering and risk controls
- Abuse prevention, rate limits, quotas, and audit trails
- Tenant isolation and sensitive data handling
04
Evaluation & AI Observability
AI made measurable: quality, cost, and reliability become visible.
Includes
- Offline evaluation sets and regression testing
- Quality metrics and acceptance criteria per use case
- Operational monitoring of latency, cost, and failure modes
- Feedback loops for continuous improvement
Engagement
Engagement Models
Most teams start with an architecture sprint, then continue with advisory support during implementation.
2–4 weeks
AI Architecture Sprint
An assessment of the current state, a target architecture, and a rollout plan.
OutputBlueprint, governance model, and implementation plan
Ongoing
Advisory Retainer
Guidance while your team ships AI features and runs them in production.
OutputReviews, decisions, guardrails, and an escalation path
Through delivery
Implementation Oversight
Hands-on supervision that reduces risk and keeps delivery quality high.
OutputAligned execution, quality gates, and lower risk
FAQ
Frequently Asked Questions
Is this only about OpenAI integration?
No. The focus is architecture: patterns and governance that work across providers. The provider 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?
With token governance, caching and batching, model routing, and usage visibility. Cost becomes measurable and enforceable.
Do you design agentic systems?
Yes, with discipline. Agents should be workflows with clear boundaries, safe tool execution, and observability, not an unstructured layer of AI magic.
Build AI Capabilities You Can Operate and Scale
If you want AI features that are reliable, secure, and cost-aware, I can help you design an AI architecture your team can ship and maintain.