Your team is answering the same questions, routing the same requests, and digging through the same docs — every single day. I build AI systems that handle all of it, so they don't have to.
Sound familiar?
Whether it's HR policy, product docs, or operational SOPs — your smartest people are being used as a search engine. Every answer they give manually is an answer an AI could handle in seconds.
Tier-1 support is almost entirely pattern-matching. A well-built AI agent handles 70–80% of these queries instantly — and your team handles only the edge cases that actually need human judgment.
Institutional knowledge is locked in PDFs, Notion pages, Slack threads and email chains. A RAG-based system makes all of it instantly queryable — so onboarding takes days, not months.
Generic chatbots fail because they're not trained on your specific context, data, and tone. Custom-built systems — designed around your actual workflows — are a completely different product.
Company background, recent news, LinkedIn activity, funding rounds — all of this can be auto-pulled and summarised before every call. Your reps show up prepared, not scrambling.
ChatGPT is great for generic tasks. What your business needs are AI agents that read your CRM, write to your database, send Slack messages, and trigger workflows — not just generate text.
How I solve it
I don't deploy generic AI tools and call it automation. I build systems that understand your data, your tone, and your business logic — then integrate them directly into the tools your team already uses.
Before writing a line of code, I map which workflows are eating the most time and where automation will have the highest ROI. Often the real problem is different from the stated one.
I ingest your docs, databases, and internal tools into a retrieval layer that gives the AI accurate, grounded context — so it answers from your data, not from its training.
Slack, Notion, HubSpot, Salesforce, custom APIs — I build connectors that make the AI a native part of your workflow, not another tab to open.
Every system I ship has logging, accuracy tracking, and alerting built in. AI systems degrade silently — I make sure you'd know before your users do.
A B2B SaaS founder came to me asking for a customer chatbot. After a 30-minute diagnosis call, the real problem was different: their 12-person ops team was spending 3+ hours/day answering internal questions that were already documented — just impossible to find.
I built a RAG-based internal knowledge agent connected to their Notion workspace, Confluence docs, and PostgreSQL database. Team members could now query it from Slack. The customer chatbot came later — and cost a fraction of what it would have cost first, because the knowledge base was already clean and structured.
What's included
A structured audit of your current workflows to identify exactly where AI automation will have the highest impact — before a line of code is written.
Data ingestion, chunking, embedding, and vector retrieval — tuned for your specific data shape. Not a generic wrapper around an API.
System prompts that constrain the model to your domain, tone, and accuracy requirements — with safety rails to prevent hallucinations and off-topic responses.
Slack, Notion, HubSpot, Salesforce, or custom API — I build the connectors that make the AI agent a native part of your workflow, not a separate tool.
A test suite measuring accuracy, hallucination rate, and latency — so you can track quality over time and know when retraining or updates are needed.
Logging dashboard, anomaly alerts, and 30 days of direct support after go-live — so any issues surface and get fixed before they affect your team or customers.
Book a free 30-minute discovery call. I'll map the highest-leverage automation in your business and tell you exactly what it would take to build it.