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Most automation pipelines fail the moment production traffic spikes. Brittle webhooks drop payloads, API rate limits crash running tasks, and silent runtime bugs corrupt database entries.
I build resilient backend systems engineered to eliminate these exact failure points.
Here is what I deliver for engineering teams and scaling companies:
Custom Protocol Servers: Clean, type-safe backend services connecting LLM environments directly to internal databases and secure APIs.
Fault-Tolerant Pipelines: Resilient n8n and Python architectures equipped with automated error recovery, retries, and state persistence.
Strict Data Validation Layers: Deterministic verification logic that blocks malformed outputs and prevents unstructured data leaks before reaching production.
Autonomous Processing Engines: Long-running background workers engineered to handle structured intake, data processing, and repetitive backend operations without human intervention.
If you are replacing fragile legacy setups or moving internal prototypes to production infrastructure, send a message. Let's build a reliable architecture.
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CRAG — Self-Correcting Research Assistant
Overview
A research-assistant project exploring how AI applications can retrieve information and improve the quality of their responses through a self-correction workflow.
Project focus
Python-based AI application development.
Retrieval and research workflow design.
Improving answer reliability through evaluation and correction.
Technologies: Python, AI, retrieval-augmented generation.
Self-correcting retrieval is the upgrade RAG has needed, Daniel. Most systems stop at the first retrieval and hope for the best, so building evaluation and correction into the workflow is a real reliability win. The live telemetry dashboard makes it feel production-ready.
𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗗𝗼𝗲𝘀𝗻'𝘁 𝗡𝗲𝗲𝗱 𝗠𝗼𝗿𝗲 𝗠𝗮𝗻𝘂𝗮𝗹 𝗪𝗼𝗿𝗸. 𝗜𝘁 𝗡𝗲𝗲𝗱𝘀 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗦𝘆𝘀𝘁𝗲𝗺𝘀.
Think about how much time you spend every week doing the same things.
Replying to customer emails.
Following up with potential clients.
Updating spreadsheets.
Managing leads.
Moving information between applications.
Sending reminders and notifications.
Now ask yourself:
𝗛𝗼𝘄 𝗺𝘂𝗰𝗵 𝗼𝗳 𝘁𝗵𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗯𝗲 𝗱𝗼𝗻𝗲 𝗺𝗮𝗻𝘂𝗮𝗹𝗹𝘆?
Many repetitive business tasks can be automated, yet businesses continue spending valuable hours doing them manually.
𝗧𝗵𝗲 𝘁𝗼𝗽 1% 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝗶𝘀𝗻'𝘁 𝗮𝗯𝗼𝘂𝘁 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 24 𝗵𝗼𝘂𝗿𝘀 𝗮 𝗱𝗮𝘆. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁.
Imagine a workflow where:
• A customer submits an inquiry, and their details are automatically recorded.
• Incoming emails are categorized, and relevant replies are drafted for review.
• New leads are added to your CRM without manual data entry.
• Important tasks trigger notifications and reminders automatically.
• Your business applications exchange information without constant copying and pasting.
• AI helps process documents, organize information, and handle repetitive administrative work.
𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝗮𝘀𝗸𝘀. 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. 𝗢𝗻𝗲 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺.
That's what I help businesses build.
I've launched my new service on Contra:
𝗖𝘂𝘀𝘁𝗼𝗺 𝗔𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀
I build workflows using n8n, Python, and APIs to connect business applications, reduce repetitive work, and simplify daily operations.
My approach is simple:
• Understand your existing business process.
• Identify the repetitive tasks taking up your time.
• Build an automation workflow around your actual needs.
• Test the workflow and make sure it performs as intended.
𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘀𝗵𝗼𝘂𝗹𝗱𝗻'𝘁 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝗱. 𝗜𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗺𝗮𝗸𝗲 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗶𝘁 𝗲𝗮𝘀𝗶𝗲𝗿.
You don't have to automate everything overnight. Sometimes, solving just one repetitive problem is the best place to start.
Your time should go toward growing your business, not repeating tasks that a system could handle.
Explore my service:
Business owners and founders: What's one repetitive task you wish you could automate today?
#AIAutomation #BusinessAutomation #WorkflowAutomation
Well said! 🙌 Automating even one repetitive task can save valuable time and help businesses focus on growth.
Picture a consultant who takes 8 sales calls a week.
After each one comes the same routine: replay the recording, write notes, update the pipeline sheet, write the follow-up email. Call it 30 minutes a call. That is 4 hours a week, and on busy days the follow-up slips to tomorrow.
I built an n8n workflow that takes that routine away:
→ Fireflies records and transcribes the call
→ As soon as the transcript is ready, AI writes a summary, the open questions, the next step and whether the prospect is ready to go ahead
→ A follow-up email that fits that outcome is saved in Gmail drafts, addressed to the person on the call
→ The call is logged in Google Sheets and the notes land in your inbox
Nothing is sent automatically. You read the draft, change a line if you want, and press send.
For that consultant, 4 hours of admin a week turns into a few minutes of reading, and every prospect hears back the same day.
I tested it on a real recorded client call and have submitted it to the n8n template library as a free template. If you want it set up for your business, send me a message.
