AI features that survive contact with real users
LLM features, chatbots, and workflow automation built by engineers who teach AI-assisted development to 4,800+ students.
Who it's for
This is built for you if…
- Product teams that want an AI feature grounded in their own data, not a thin ChatGPT wrapper
- Operations teams drowning in manual work an LLM pipeline could handle
- Founders who tried a no-code AI tool and hit its ceiling
- CTOs who need someone to separate what LLMs can reliably do from vendor hype
What you get
Deliverables
Everything below is included in the fixed scope — no surprise line items.
- A scoped AI feature in production: assistant, document pipeline, or internal automation
- Retrieval over your own data (RAG) so answers cite your content instead of hallucinating
- Guardrails and evaluation suite: the feature is tested against a fixed question set before and after every change
- Cost and latency budget per request, monitored in production
- Fallback paths for when the model is wrong, because sometimes it is
- Admin visibility: logs of what the AI said, to whom, and at what cost
- A written honest assessment of what we did not automate, and why
Investment
Timeline & starting price
Prices are a starting range, not a teaser: after a 30-minute call you get a fixed-scope proposal within 5 business days, and the number in it is the number you pay.
Proof, not promises
We've shipped this before
AI document assistant: answers from the company's own knowledge, with citations
70% of routine questions answered without a human
An internal LLM assistant grounded in the client's documents, answering staff questions with citations and escalating to a human when unsure.
FAQ
Common questions
What does an AI integration cost to run, not just to build?
We design to a per-request budget and show it in the proposal. A typical support assistant runs $50–$300 per month in model costs at small-business volume; a heavy document pipeline can run more. You see projected monthly cost before we write code, and real cost in a dashboard after launch.
How do you stop it from making things up?
Three ways: retrieval grounding so it answers from your documents, an evaluation set of 50+ real questions it must pass before each release, and explicit refusal paths for questions outside scope. We also log every response so failures are found, not rumored.
Why should we trust you on AI specifically?
We teach AI-assisted development to 4,800+ students on Udemy and YouTube, and we use these tools daily to ship client work. That means we know precisely where LLMs fail, which is the knowledge that actually matters in production.
Next step
Book a 30-minute call
Tell us what you're building. We respond within 4 business hours. NDA on request, fixed-scope proposal within 5 days.