Consulting
I help small teams find and build practical automation, data and internal-AI systems where repetitive work, fragmented tools or poor information flow are already creating friction.
No fake transformation story. No invented case-study metrics. The current proof is my engineering work and hands-on operational experience; external consulting cases will be added only when they exist.
Recognisable problems
If the right answer is a deterministic integration, that is the answer. LLMs belong in the parts of the workflow that genuinely need language or reasoning.
Typical problem
Manual handoffs, copying between tools, repetitive admin, brittle spreadsheets and recurring tasks that depend on someone remembering the next step.
Possible intervention
Map the process, remove unnecessary steps, connect APIs and systems, and build the smallest reliable automation or internal tool that closes the loop.
API integrations · CRM workflows · internal tools · Make / automation flows
Typical problem
Knowledge scattered across documents, repetitive operational questions, manual research and unstructured inputs that are expensive to process by hand.
Possible intervention
Build controlled AI-assisted workflows with explicit sources, validation and human decision points instead of a generic chatbot pasted onto the process.
RAG / retrieval · structured LLM workflows · evaluation · guardrails
Typical problem
CRM operations, recurring reporting, manual data manipulation and disconnected systems that make operational decisions slower than they should be.
Possible intervention
Create pipelines, synchronisation, automated reporting, enrichment and decision-support surfaces that reduce recurring operational load.
SQL / pipelines · CRM automation · reporting · data synchronisation
Typical problem
A team knows it should use AI or automation but does not yet know where the economics, data and workflow constraints make it worthwhile.
Possible intervention
Map processes, identify repeatable pain, rank opportunities by value and feasibility, then prototype the strongest candidate before committing to a larger build.
process mapping · ROI / feasibility · prototype · implementation plan
Working principle
“Use the least complex system that reliably removes the problem.”
That might be an API integration, a scheduled data pipeline, a small internal tool, a retrieval system or an LLM workflow with explicit validation. The implementation choice comes after understanding the process.
My background across software engineering, data work and RevOps is useful here because the problem usually crosses more than one layer.
A sensible path
Understand the workflow, users, constraints and where time or quality is actually being lost.
Test the smallest technically honest solution against real inputs before scaling scope.
Build the integration, data path, interface and failure handling needed for regular use.
Document the system, expose the important controls and make ongoing operation understandable.
Current proof
At papernest I work inside RevOps across data, AI and automation. My public work ranges from stateful AI systems and LLM evaluation to reproducible ML pipelines and backend services.
Start small