We recover underperforming AI systems and prove it with benchmarks.
AI Cost & Reliability Engineering for production LLM systems. We fix wrong answers, hallucinations, and inference cost—then prove improvements with before/after benchmarks.
Founded to contribute to the AI era with full passion. OptyxStack brings systematic thinking, long-term focus, and evidence-based delivery to every engagement.
Before/after benchmarks are part of delivery. Exact deltas depend on the starting system and constraints.
Daniel R Foster
Daniel brings a systematic mindset to everything he builds—from production AI systems to team practices. He consistently favors long-term, sustainable direction over short-term wins, applying rigorous engineering principles to both technology and organizational design.
His approach: baseline before fixing, measure before claiming, and leave clients with durable artifacts—evals, dashboards, regression gates—so improvements persist long after the engagement ends.
Learn more about DanielBuilt to contribute to the AI era
OptyxStack was founded with a clear mission: to contribute a meaningful force to the AI era—with full dedication and no shortcuts. We believe production AI systems deserve the same rigor as traditional software: measurable baselines, evidence-based fixes, and governance that prevents drift.
Before OptyxStack, we saw teams struggling with wrong answers, hallucinations, and runaway inference cost—often without the tools or methodology to diagnose and fix. We built this practice to fill that gap: audit-first delivery, end-to-end troubleshooting, and outcomes you can prove.
Today we work with enterprise teams on LLM audits, RAG recovery, and reliability retainers—while staying deeply involved in the broader AI community through open-source contributions and industry benchmarks.
Fix underperforming AI systems with evidence, constraints, and measurable outcomes—from prompting to RAG reliability.
How we think about AI optimization
No guesswork. We apply a structured, evidence-based methodology to fix production AI systems.
Audit-first, evidence-based delivery
We don't guess. Every engagement starts with a baseline audit—measuring cost per conversation, groundedness score, TTFT/P95 latency, and failure modes. Fixes are prioritized by ROI, not by what's trendy. Evidence in, measurable improvement out.
End-to-end AI system troubleshooting
We troubleshoot production AI across the full stack—prompting, retrieval, reranking, tool calls, routing, caching, and LLM serving. If your chatbot gives wrong answers or hallucinates, we isolate the constraint and ship fixes with before/after benchmarks.
You keep the artifacts
We don't create vendor lock-in. After every sprint, you own the evaluation framework, golden test set, monitoring dashboards, and regression gates in CI. Your team can maintain and iterate without us. Knowledge transfer, not dependency.
Measurable outcomes, not promises
Every fix is verified with production metrics: cost/conversation, groundedness score, TTFT P50/P95, and retrieval hit rate. We don't ship "improvements" without proof. Show the delta, or it didn't happen.
How we work: Audit → Sprint → Reliability
A structured process designed to deliver measurable improvements with minimal risk.
AI System Audit
Baseline your system: cost/conv, TTFT/time-to-answer, quality score, retrieval hit rate, failure modes. Produce a decision-ready recovery plan prioritized by ROI.
Deliverables
- Baseline metrics
- Failure analysis
- ROI roadmap
AI Recovery Sprint
Focus on 1–2 recovery goals. Ship PRs across prompting, RAG, pipeline, serving. Verify with before/after benchmarks on cost, quality, and speed.
Deliverables
- Production PRs
- Eval harness + golden set
- Before/after proof
Reliability Retainer
Ongoing monitoring, regression gates, tuning, and incident triage. Prevent quality drift and cost regression as your system evolves.
Deliverables
- Monitoring dashboards
- Regression gates in CI
- Incident response
Enterprise AI teams with production systems
Best fit: teams running LLMs, RAG, or AI agents in production—with accuracy, reliability, cost, or drift pain.
Good fit
Recommended
- Enterprise chatbots giving wrong answers despite good documentation
- RAG systems with low retrieval accuracy and citation gaps
- AI products with high inference cost and slow P95 latency
- Teams without evals to catch hallucinations and quality drift
- Vendor-built AI systems that need a second-opinion audit
Industries: Finance (support copilot, compliance Q&A), Legal (contract search, RAG), Healthcare (internal assistants), Enterprise SaaS (customer-facing chatbots).
Core skills
AI optimization expertise
Deep AI engineering, not just consulting
We don't write strategy decks. We ship PRs, build eval frameworks, and prove improvements with production metrics.
Open source & AI benchmarks
We stay sharp by contributing to the AI ecosystem—open-source projects, benchmarks, and industry competitions.
Open-source AI projects
Contributing to the ecosystem
OptyxStack actively participates in open-source AI initiatives—from evaluation frameworks and RAG tooling to observability libraries. We believe the best practices for production AI should be shared, not siloed.
AI competitions & benchmarks
Pushing the frontier
We take part in major AI competitions and benchmark efforts—testing our methods against real-world challenges and staying current with the latest advances.
Why we invest in the community
Long-term thinking means building for the ecosystem, not just for clients. By contributing to open source and participating in benchmarks, we stay honest about our methods—and bring back learnings that make every engagement stronger.
Ready to fix your AI system?
Fixed-scope audit. Sprint priced after baseline. Response within 24 hours.