You will get a Production RAG Pipeline with LangSmith Evaluation Dashboard
Project details
Most RAG implementations ship without any way to measure whether retrieval is actually working.
You get a pipeline that returns answers — but no visibility into whether those answers are accurate, grounded, or drifting over time.
I build the RAG pipeline and set up LangSmith evaluations at the same time: relevance, faithfulness, and groundedness scores on every production trace, plus cost and latency monitoring.
This is the approach I use at Structed.ai — observability is not an afterthought, it's part of the build.
Deliverables: retrieval pipeline, LangSmith eval dashboard, automated quality scores, cost tracking. Stack: LangChain, LangSmith, Python.
You get a pipeline that returns answers — but no visibility into whether those answers are accurate, grounded, or drifting over time.
I build the RAG pipeline and set up LangSmith evaluations at the same time: relevance, faithfulness, and groundedness scores on every production trace, plus cost and latency monitoring.
This is the approach I use at Structed.ai — observability is not an afterthought, it's part of the build.
Deliverables: retrieval pipeline, LangSmith eval dashboard, automated quality scores, cost tracking. Stack: LangChain, LangSmith, Python.
AI Algorithms
Large Language ModelAI Applications
AIOpsAI Models
GPT-4What's included $3,500
These options are included with the project scope.
$3,500
- Delivery Time 14 days
- Number of Revisions 1
- MLOps
- Model Monitoring
- Prompt Engineering
Frequently asked questions
About Denys
Fractional CTO | AI Systems Architect | Early-Stage Tech Leadership
Palm Coast, United States - 12:16 pm local time
Available for:
- Fractional CTO (paid retainer, 1–2 startups at a time)
- Technical co-founder / CTO partnership (equity-based, right early-stage product)
What I bring:
- Technical architecture decisions (not just code review)
- AI integration strategy - LangGraph, RAG, LangSmith, agent pipelines, production AI systems
- Engineering team building - first hires, onboarding, defining standards
- Roadmap from prototype to production (I've been on both sides of that gap)
LLM Observability & LangSmith:
If your LangChain/LangGraph agents are in production (or heading there), I set up proper observability: tracing, evaluations, cost monitoring, and alert rules. I run this exact setup at Structed.ai. Available as a standalone engagement (1 week, $1,500–3,500) or as part of a broader CTO engagement.
My background:
- Structed.ai (current): Co-founder & CTO, building an AI Discovery Agent from zero - NestJS, FastAPI/LangGraph, PostgreSQL, Docker
- BigPanda: backend platform connecting 200+ enterprise monitoring tools (Datadog, ServiceNow, PagerDuty), 40+ microservices, real-time incident correlation at scale
- Autodesk: modernization of checkout & billing platform - revenue-critical infrastructure serving millions of subscribers globally
I work with founders who need someone to answer: "Is our architecture right for where we're going?" and "Who's going to make the technical calls while we're figuring this out?"
Entry points:
- Tech Snapshot (2 days, $500–800) - quick written verdict on your stack
- Architecture Audit (2 weeks, $2,500–5,000) - full report + 90-day roadmap
Ongoing: Monthly Advisor retainer for continued CTO-level guidance.
Who this works for: Pre-seed / seed stage, B2B SaaS or AI product, non-technical CEO, 1–5 engineers.
Send me a message with where you are technically and where you need to get to.
Steps for completing your project
After purchasing the project, send requirements so Denys can start the project.
Delivery time starts when Denys receives requirements from you.
Denys works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery + data audit
Review your data sources, use case, and existing stack. Define retrieval scope and quality criteria.
RAG pipeline build
Build retrieval pipeline: document ingestion, chunking, embedding, vector search, LLM generation.