RAG chatbots over your docs. Workflow automations over your ops. One fixed price quoted before any code gets written. Running in your environment on week five.
PROBLEMS I HEAR EVERY WEEK
Your team knows the answer exists somewhere in your docs. Finding it, copying it, and pasting it into the right format takes 5 to 10 minutes per ticket. Multiply that by 40 tickets a week and you have a full-time person doing copy-paste work. A RAG chatbot over your help docs and past tickets gives your support team instant answers they can verify and send in under a minute.
The answers live across Notion, Slack threads, and a Google Doc someone wrote 8 months ago. Nobody knows which version is current. A chatbot trained on your internal docs gives every new hire the same quality of answer your best team member would give, without pulling that person away from their work./p>
That report could build itself. A workflow that pulls from your database, Stripe, and your app's API, formats the numbers, and drops them into Slack before the meeting starts. No human in the loop. The same logic applies to weekly client reports, invoice summaries, and pipeline updates.
Speed-to-lead matters more at seed stage than at any other point. A workflow that catches the form submission, enriches the lead from your CRM, scores it against your history, and routes it to the right rep with context saves the deal before it goes cold. The whole sequence runs in under 30 seconds.
Zapier works until it doesn't. Once you have more than a handful of zaps with conditional logic, error handling becomes a full-time job. I build workflows in n8n, self-hosted on your infrastructure, with error alerts and retry logic built in from day one. Your team can see and edit every step in a visual editor.
Search doesn't fix this. Your team searches, gets 40 results, and still can't tell which one is current. A RAG system indexes your docs, understands context, and returns the specific paragraph that answers the question. Not a list of links. The actual answer, with the source document attached so your team can verify it.
How I solve it
Each move maps back to one of the quotes above. None of them is optional.
The "we automated it but nobody trusts it" failure starts with skipping this step. Week 1 produces a workflow map that names every trigger, every data source, every decision point, and every output. Both of us sign off before I write a line of code. The automation never breaks trust if both sides agreed up front on what it was supposed to do and when.
Not every problem needs a chatbot. Some problems need a simple n8n workflow. Some need vector search on pgvector. Some need a graph database because the relationships in your data matter more than the text. I built a GraphRAG system with Neo4j for exactly that reason. The scoping doc recommends the architecture and explains why.
The "scattered knowledge" problem and the "manual Monday report" problem are both integration problems. Your docs live in Notion. Your tickets live in Zendesk. Your numbers live in Postgres and Stripe. I connect to the systems you already use. No new tools for your team to learn. No manual data migration.
The "Zapier breaks every month" problem exists because most automations ship without retry logic or failure alerts. Every workflow I build includes error notifications, automatic retries on transient failures, and a logging dashboard your team can check. When something fails, you know what failed and why, before your customers do.
A chatbot nobody can update is dead in 6 months. Every system I ship comes with docs your team can follow, a visual workflow editor they can modify, and a content update process that doesn't require calling me back. After week five, your team owns the system.
The Setup: Standard vector search (RAG) works well when the answer sits in a single paragraph. It falls apart when the answer requires connecting facts across multiple documents. "Who worked on Project X and what tools did they use?" needs a graph, not a keyword match.
What got built: A personal knowledge base chatbot using Neo4j as the graph database, Ollama for local LLM inference, and LangChain for the retrieval pipeline. Documents get ingested into a knowledge graph where entities and relationships are extracted and stored as nodes and edges. Queries traverse the graph to assemble answers that span multiple source documents.
Why this matters for your use case: If your internal docs reference each other (support playbooks that depend on product specs, onboarding guides that reference policy documents, sales decks that pull from case studies), GraphRAG returns better answers than flat vector search. The scoping call covers which approach fits your data.
Stack: Python, Neo4j (Docker), Ollama, LangChain.
See the GitHub repoThe Setup: A fintech SaaS startup's sales team stitched competitor research together by hand. Trustpilot reviews, hiring data, and industry news lived in different places and different languages. Per-prospect research took 3 to 4 hours.
What got built: An n8n workflow automation pipeline that pulled 520+ data points from Trustpilot API and industry news sources. Multilingual text normalization handled German and English reviews in the same pipeline. The output fed directly into a Streamlit dashboard the sales team used daily.
What changed: Per-prospect research dropped from 3 hours to under 1. The automation ran on a schedule. The sales team stopped doing the data collection entirely.
Stack: Python, n8n, BeautifulSoup, Trustpilot API, Streamlit.
Work done as Data Science Intern at Wavess.io between July and October 2025.
Get a similar resultDELIVERABLES
A workflow map covering every trigger, data source, decision point, and output
An audit of your existing docs and data sources for chatbot coverage and quality gaps
A RAG chatbot (pgvector or Neo4j) deployed on your infrastructure, with source citations on every answer
Workflow automations (n8n, self-hosted) with error handling, retry logic, and failure alerts
API integrations connecting your existing tools (Notion, Slack, Stripe, your database, CRM, ticketing system)
A monitoring dashboard showing query volume, response quality, and workflow success rates
A content update guide your team follows to add new docs or modify workflows without calling me
A 30-day post-deployment review against the usage and time-saved metrics we agreed on in week 1
How It Works
A 30-minute call. We go through your docs, your workflows, and the manual work you want to eliminate. I tell you straight whether automation is the right answer for your case. No deck. No pitch.
I write up a scoping doc that covers the workflow map, the architecture decision (vector RAG vs. GraphRAG, chatbot vs. workflow vs. both), the integrations, and your fixed price. If at the end of the week it isn't the right fit, you keep the document and we part ways. No contract trap.
Chatbot gets trained on your docs. Workflows get wired to your tools. I push to staging in your environment. You test with real queries and real data. We adjust retrieval quality and workflow logic based on what your team actually asks and needs.
Error handling, retry logic, monitoring dashboard, and fallback behavior all go in this week. The chatbot gets a "I don't know" response for questions outside its training scope instead of hallucinating. Workflows get failure alerts routed to the right person.
Walkthrough with your team. Content update guide. Workflow editor access. Monitoring dashboard review. After this week, your team owns the system.
What slows the build down. Docs that are outdated or contradictory. Permissions issues on the tools I need to connect to. A data source that has no API and requires scraping or manual export. I flag these in week 1 if I see them coming.
If you're not sure which side you fall on, book the call. I'll tell you straight.
ChatGPT answers from its training data. A RAG chatbot answers from your data. It searches your docs, finds the relevant section, and returns that answer with a citation. Your team verifies the source before acting on it. No hallucinations about your own company's policies.
Depends on the problem. If your team asks the same questions about your docs repeatedly, that's a chatbot. If your team does the same manual steps in the same order every week, that's a workflow. Most seed-stage teams need one of each. The scoping call figures out which one moves the needle first.
Yes. The ingestion pipeline reads from whatever tools your docs live in. If it has an API, I can connect to it. If it doesn't (some legacy wikis), we discuss workarounds on the scoping call.
The content update guide covers this. For most setups, re-ingestion runs on a schedule (daily or weekly) and picks up changes automatically. For large doc sets, incremental updates keep it fast.
Every RAG system has a confidence boundary. When the chatbot can't find a relevant answer in your docs, it says "I don't have enough information to answer that" instead of making something up. Source citations on every response let your team verify before acting.
Everything runs on your infrastructure. Your docs never leave your environment. I use self-hosted LLMs (Ollama) when the data can't touch a third-party API. The scoping doc covers the privacy architecture for your specific case.
You don't have to. If your Zapier setup works and doesn't break, keep it. But if you've hit the point where debugging zaps takes longer than the manual work they replaced, n8n gives you a self-hosted visual editor, full error logs, and no per-task pricing. Migration is straightforward for most workflows.
By time saved, not by impressiveness of the tech. The scoping doc names the metric: tickets resolved without human intervention, hours saved on weekly reporting, lead response time in seconds, or onboarding questions handled by the bot instead of a teammate. We measure against that number at the 30-day review.
A 20-minute scoping call. We go through your docs, your workflows, and the manual steps you want gone. A one-page proposal lands in your inbox within 48 hours of the call.