You will get an AI integration audit: what to build, cost, and risks
Project details
Before you spend serious money building an AI feature, spend a few days knowing exactly what to build — and what to skip.
I'm a senior AI engineer (8 years full-stack, last 2 shipping production LLM systems: RAG pipelines, LangGraph agents, MCP servers). This audit reviews your stack, your data, and the workflow you want to improve, and delivers a written plan:
• Recommended approach — and explicitly what NOT to build
• Build cost + monthly running cost estimate (API, hosting, maintenance)
• Risk list: data quality, hallucination exposure, compliance, vendor lock-in
• A phased plan starting with the smallest useful milestone
Standard tier adds an architecture sketch and a video walkthrough; Advanced adds a working proof-of-concept spike on your actual data.
You get an honest engineering answer, not a sales pitch — sometimes the answer is "a $200/month SaaS already does this."
I'm a senior AI engineer (8 years full-stack, last 2 shipping production LLM systems: RAG pipelines, LangGraph agents, MCP servers). This audit reviews your stack, your data, and the workflow you want to improve, and delivers a written plan:
• Recommended approach — and explicitly what NOT to build
• Build cost + monthly running cost estimate (API, hosting, maintenance)
• Risk list: data quality, hallucination exposure, compliance, vendor lock-in
• A phased plan starting with the smallest useful milestone
Standard tier adds an architecture sketch and a video walkthrough; Advanced adds a working proof-of-concept spike on your actual data.
You get an honest engineering answer, not a sales pitch — sometimes the answer is "a $200/month SaaS already does this."
AI Development Type
Knowledge Representation, Software MaintenanceAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$350
|
Standard
$500
|
Advanced
$900
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
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ML
Marck L.
Apr 28, 2022
Hubspot CMS developer for Megamenu project.
Unfortunately the freelancer had no experience with Hubspot CMS, which was critical to complete the job. I have no doubts about his development skills, but it was not adequate for our job at hand.
About Oleksandr
AI Agent Developer | LangGraph | RAG | MCP | Python & TypeScript
Wroclaw, Poland - 4:23 pm local time
What I actually ship:
• AI agents: LangGraph StateGraph/ReAct agents with Postgres-checkpointed state (crash-resume), tool calling, structured outputs (Instructor + Zod), and human-in-the-loop approval steps.
• RAG that retrieves the right thing: hybrid lexical + semantic retrieval (Postgres FTS + pgvector HNSW), HyDE query expansion, Reciprocal Rank Fusion, LLM reranking.
• LLM integrations treated like production systems: RAGAS evals over golden datasets in CI, Langfuse + OpenTelemetry tracing, prompt caching and multi-provider fallback (Anthropic, OpenAI, local) for cost and uptime control.
• MCP servers: I design and run Model Context Protocol servers in production (a 20-tool FastMCP service with Telegram integration, deployed on Railway).
Background: 8 years of full-stack engineering (TypeScript/Node — React/Next.js, NestJS, PostgreSQL, Redis), the last 2 focused on applied-LLM systems in Python/FastAPI and TypeScript. I've built AI meal-planning generation pipelines, federal contract-intelligence AI matching, and agent systems with approval workflows.
How I work: scoped milestones with a written "done =" definition, a short paid discovery step for anything non-trivial, daily async updates, and code you can run — with tests, tracing, and deployment included.
If your post mentions LangChain/LangGraph, RAG, agents, chatbots, MCP, or "our AI feature works in the demo but not in production" — that's exactly my lane. Send me the job details and I'll reply with a concrete plan, not a bio.
Steps for completing your project
After purchasing the project, send requirements so Oleksandr can start the project.
Delivery time starts when Oleksandr receives requirements from you.
Oleksandr works on your project following the steps below.
Revisions may occur after the delivery date.
Review
I review your answers, stack and data samples, and ask follow-ups in the workroom if anything is unclear.
Analysis
I map your use case(s), estimate build cost and monthly running cost, and assess risks: data quality, hallucination exposure, compliance, lock-in.