03 · AI Solutions
AI built into
your business.
Not built on someone else's platform.
AI agents, embedded chatbots, document processing, RAG over your own data, and internal copilots. Built on Claude, OpenAI, and open-source models. Owned by you, branded as yours, running on your domain.
The problem
AI hype is everywhere. AI value is rare.
- ✕ You have been told to "do something with AI" but have no clear use case.
- ✕ You tried a generic chatbot and it embarrassed itself in front of your customers.
- ✕ Your data is locked in tools that AI cannot read.
- ✕ You worry about handing your business data to a US tech giant. Reasonable.
What I build
AI that earns its keep.
AI agents
Multi-step agents that can browse, query, decide, and act. Custom sales agents, research agents, internal assistants. Built on Claude or OpenAI with tool use.
RAG chatbots
Chatbots that actually know your business. Trained on your docs, your product info, your policies. Branded as yours, running on your domain.
Document processing
Invoices, contracts, PDFs, scanned forms. Extract structured data, classify, route. The boring work where AI shines.
Internal copilots
AI assistants for your sales team, your support team, your ops team. Trained on your CRM, your tickets, your playbooks.
AI workflow nodes
Drop AI steps into your n8n or Make workflows. Classify, summarise, generate, translate. The Lego brick your automations were missing.
AI strategy & audits
Lots of teams want to use AI but do not know where to start. I will audit your workflows and tell you the three places where AI will actually pay back.
How it works
Prototype before you commit.
Most AI projects fail because they get scoped before anyone tested whether the model can actually do the thing. We test in week two, commit in week three.
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01
Week 1
Use-case mapping
We find the three places where AI will actually save time or earn revenue. Most of the rest is theatre.
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02
Week 2
Prototype
A working prototype on real data, end of week two. You will see whether AI can actually do the thing before we commit to building it.
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03
Weeks 3 to 4
Deploy
Integrate into your stack, your domain, your auth. Branded as yours. Documented so your team can own it.
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04
Ongoing
Monitor
AI drifts. Models update. Prompts age. Optional retainer to keep things sharp and add new capabilities as they ship.
What this looks like in practice
AI use cases that work.
Custom GPT for proposal drafting
Sales team feeds in a discovery call transcript, gets back a tailored proposal draft in their voice, with the right pricing and case studies pulled from internal docs.
RAG chatbot on the help docs
Replaces 60% of first-line support tickets. Hands off to a human when it is not confident. Built on the client own docs, branded fully.
Document extraction agent
Vendor contracts arrive as PDFs. Agent extracts parties, term, renewal date, key clauses, drops the result into the contract management system.
Slack copilot for ops queries
Team types "@bot what is the status of order 4521?" in Slack. Bot queries the database, summarises, replies. No more digging through dashboards.
FAQ
The honest answers.
Will my data be safe?
Yes. By default I use enterprise tiers of Claude and OpenAI that do not train on your data. For sensitive workloads I can run open-source models on your own infrastructure so nothing leaves your network.
What does AI work actually cost?
A focused AI agent or chatbot starts around R25k for build, plus token costs (usually a few hundred rand per month for SME volume). Internal copilots and custom agents are quoted per use case.
Do I have to commit to OpenAI or Claude?
No. I build with abstraction layers so swapping models later is a config change, not a rebuild. You can run Claude today and self-hosted Llama next year if you want.
Is this just a wrapper around ChatGPT?
No. ChatGPT is a chat interface. What I build is integrated into your stack, fed by your data, branded as yours, with logic and guardrails that fit your business. The "wrapper" cliché applies to people who slap a prompt on a chatbot and call it AI strategy.
Can AI replace my employees?
Some tasks, yes. Whole employees, almost never. The realistic outcome is your team handles 3x the volume, or shifts away from grunt work toward judgement-heavy work. That is usually the goal.
What if AI gives a wrong answer to a customer?
Real risk. We mitigate with guardrails (no answering outside known topics), confidence thresholds (hand off to human when unsure), and review queues (humans see flagged conversations). Not perfect, but better than most call centres.
Want AI that actually works?
Let's prototype something.
A short call to find the place where AI will pay back. Two weeks to a working prototype. No commitment to the full build until you have seen it run on your data.