AI Engineering & LLM Integration
Pragmatic, production-grade AI features that drive concrete business value—not vaporware.
Integrating AI into production requires rigorous schema enforcement, fallback handling, semantic retrieval, and background orchestration. I build intelligent features that feel native to your product and operate cost-effectively.
The Business & Architectural Problem It Solves
Many AI initiatives fail in production due to prompt fragility, high token latency, hallucination risks, and lack of structured evaluation. I bridge the gap between experimental LLM prompts and deterministic software engineering.
Key Deliverables & System Capabilities
Concrete engineering outputs, architectures, and automated deliverables produced during an engagement.
What You Can Bring to Me
Common architectural inflection points and codebase challenges where this service provides immediate clarity.
An existing application where LLM completions are brittle, unstructured, or failing to match backend API contracts.
Architecting context-aware AI assistants, semantic search, or multi-step agent workflows within strict latency and token cost budgets.
Building automated evaluation harnesses and prompt regression tests to safely upgrade foundation models.
Target Audience & Ideal Fit
Teams seeking to add generative AI, document processing, intelligent tutoring, or interactive conversational workflows to their software.
Available Engagement Models
- AI Feature Sprints
- LLM Pipeline Architecture
- AI Audit & Optimization
Core Technologies & Infrastructure
Frequently Asked Questions
Clear, practical answers regarding engagement structure, timelines, code ownership, and working models.
How do you prevent AI hallucinations in production?+
We enforce strict structured JSON schema validation, Pydantic/Zod contract parsing, prompt regression test suites, and deterministic fallback workflows so invalid completions never reach users.
How do you control latency and token costs?+
By applying semantic caching, prompt token budgeting, multi-tier model routing (fast flash models for routine extraction; larger reasoning models only when needed), and streaming responses.
Do we need our own GPUs or dedicated machine learning infrastructure?+
For the vast majority of commercial software, no. Enterprise foundation model APIs paired with serverless edge functions and managed vector databases deliver superior reliability with zero hardware overhead.
Will our proprietary business data be used to train models?+
No. All integrations use enterprise API endpoints with zero-data-retention terms and encrypted vector embeddings, ensuring your proprietary data remains strictly private.