Daniel R. Foster
Founder of OptyxStack and expert in AI project recovery, LLM audit, and RAG optimization. Daniel helps teams fix underperforming AI systems—wrong answers, hallucinations, high cost, slow latency—with measurable before/after benchmarks.
Audit-first delivery: baseline your system, isolate root causes in retrieval, prompting, or model, then ship PRs. You keep the eval framework, golden set, and monitoring—no vendor lock-in.
Daniel combines end-to-end AI troubleshooting with evidence-based engineering. Every fix is verified with production metrics: cost/conversation, groundedness score, TTFT P50/P95.
"Audit → Baseline → Fix → Prove. Every improvement is measured with before/after benchmarks."
Founder & AI Engineering Expert
Daniel R Foster brings deep expertise in AI project recovery, LLM audit, and RAG optimization. As the founder of OptyxStack, he helps teams fix underperforming AI systems with audit-first delivery and measurable before/after benchmarks.
Background
Daniel has spent years troubleshooting production AI systems—LLMs, RAG pipelines, and AI agents. His experience spans wrong answers, hallucinations, high inference cost, and slow P95 latency.
As the founder of OptyxStack, Daniel has built a practice focused on evidence-based AI engineering—measurable improvements with before/after benchmarks, not theoretical best practices. Every engagement starts with a baseline audit.
Daniel's approach combines end-to-end AI troubleshooting with systems thinking. Isolate the constraint (retrieval, prompting, model), ship fixes, and prove with cost/conversation, groundedness score, and TTFT.
Why OptyxStack
OptyxStack was founded to help teams stuck with underperforming AI systems—chatbots giving wrong answers, RAG with low retrieval accuracy, or inference cost that keeps climbing.
The mission is simple: audit first, fix with evidence, prove with benchmarks. You keep the eval framework, golden set, and monitoring—no vendor lock-in.
Daniel brings one partner who can reason across prompting, retrieval, reranking, tool calls, routing, and serving—instead of needing multiple agencies for different pieces.
End-to-End AI Troubleshooting & Evidence-Based Delivery
Daniel troubleshoots the full LLM/RAG pipeline—from prompting to retrieval to serving. Every fix is verified with production metrics. You keep the artifacts.
End-to-End AI Troubleshooting
From prompting to retrieval, reranking to serving—Daniel troubleshoots the full LLM/RAG pipeline. No guesswork: isolate the constraint, ship PRs, prove with benchmarks.
Evidence-Based Delivery
Every fix is verified with production metrics: cost/conversation, groundedness score, TTFT P50/P95. Baseline before fixes, measure after—no promises without proof.
Production AI Systems
LLM evaluation frameworks, golden sets, regression gates in CI. You keep the artifacts—evals, dashboards, monitoring—so your team can maintain and iterate.
AI Stack
- LLM Evaluation & Golden Sets
- RAG Retrieval & Reranking
- AI Observability & Monitoring
- Regression Gates in CI
Measured Outcomes
- Cost per conversation
- Groundedness score
- TTFT P50/P95
- Retrieval hit rate
Areas of Focus
AI project recovery, LLM audit, and RAG optimization—what works in production. Daniel helps teams fix wrong answers, hallucinations, and high cost with measurable benchmarks.
AI System Audit & Recovery
Audit-first delivery: baseline cost/conv, quality score, TTFT, and failure modes. Daniel helps teams isolate root causes in retrieval, prompting, or model—then ship fixes with measurable before/after benchmarks.
LLM & RAG Optimization
Fix wrong answers, hallucinations, and low retrieval accuracy. Golden sets, eval harnesses, reranking, and verification flows—measured on production signals.
Cost & Latency Optimization
Reduce inference cost and P95 latency. Routing, caching, context trimming, and pipeline optimization—verified with cost-per-conversation and TTFT metrics.
AI Production Reliability
Regression gates, monitoring dashboards, and drift detection. Prevent quality and cost regression as your LLM/RAG system evolves in production.
Architecture & Reliability Guides
Practical guides on reliability, scalability, and systems thinking—foundational for production AI systems. Written from real production experience.
SRE in Practice: How We Actually Keep Systems Reliable
Real SRE is an operating loop. Here's what works (and what 'fake SRE' gets wrong).
Reliability Audit: What We Measure (and What We Ignore)
A reliability audit is not monitoring and not a checklist. It's a failure-mode map + predictive signals + a prioritized fix order.
From Firefighting to Reliability Governance
If incidents are frequent, governance is failing. Reliability is a process: SLO policy, change control, postmortems that ship fixes.
Error Budgets for Scaling Teams: How to Grow Without Burning Out On-call
Scaling isn't just about traffic; it's about team endurance. Learn how to use Error Budgets to balance feature velocity with system reliability.
Work with Daniel
Need help with AI system audit, wrong answers, hallucinations, or high inference cost? Let's discuss how to baseline your system and ship fixes with measurable before/after benchmarks.