Service
AI & Automation
Add AI to your business — features and workflows that remove real work
I bring AI into your product and your day-to-day operations, where it removes real effort — not as a gimmick bolted on the side.
That means AI wired into the exact moment work happens: drafting, search, summaries, classification, support replies, and the repetitive back-office tasks that quietly eat your team's week. Each feature gets tight, structured context so the output stays on topic, and a human confirms anything that matters. I work across Anthropic Claude, OpenAI, and Google Gemini, and I have shipped this in production, including the backend of an AI marketing and CRM platform that orchestrates a team of AI agents with usage-based billing.
When the problem needs a real model rather than a prompt, I take it from raw data through a trained, evaluated result, the same discipline behind my peer-reviewed research on eye-disease detection with transfer learning. I favour approaches whose behaviour can be inspected over opaque end-to-end models, especially for anything sensitive.
Starting from
$5,000
per project · pilots and scoped automations from $2,500
The figure above is a starting point, not a fixed tier. Your quote depends on scope and complexity — the number of features, integrations, and how much already exists. After a short scoping call you get one fixed price for the whole project, or, for ongoing work, a monthly retainer. Every fixed-price project is split into four equal milestones. I invoice 25% only after each 25% of the work is finished and you have reviewed it, so nothing is paid up front and you never pay for work you haven't seen.
What’s included
AI features wired into your product's real actions
Automations that take repetitive work off your team
LLM integration with tight, structured context and guardrails
Retrieval and semantic search over your own content
Applied ML: data prep, training, and honest evaluation
Cost control: prompt caching and usage metering
What you get
- Working AI features and automations in your product
- An evaluated model or pipeline where relevant
- Documentation on prompts, limits, and costs
Ideal for
Businesses that want AI to earn its place by removing busywork and speeding up real decisions.
Typical stack
How it works
Scope
A short call to understand goals, users, and constraints, turned into a fixed plan, timeline, and quote.
Design
I shape the data model, architecture, and interface, with previews early so we agree before I build.
Build
Four milestones, each ending with a working preview you review before paying its 25% — clean, tested code and regular updates.
Launch & support
Deployment, monitoring, and handover, with post-launch support or a retainer when you need it.
Example work
Real projects where I did this kind of work — the problem, the build, and where it landed.
Ready to start your AI & Automation project?
Send me a short brief or just an idea. I’ll reply with a plan, a timeline, and clear next steps.
AI & Automation: common questions
Which AI models do you work with?
Anthropic Claude, OpenAI, and Google Gemini — chosen per task rather than by default. For classic ML I use Python with TensorFlow and scikit-learn.
How do you keep AI costs under control?
With prompt caching, tight context, and usage metering. On Tryneth, fixing caching on the busiest endpoint removed roughly ten million uncached tokens.
Is AI reliable enough to put in front of my users?
When it's scoped right. I give models tight, structured context and guardrails, and keep a human in the loop for anything sensitive — every suggestion is a draft someone confirms, not an unchecked action.