Question 1

Do you train your own models?

Accepted Answer

No — and we'll tell you not to, in 95% of cases. We integrate Claude, GPT, and Gemini APIs with retrieval (RAG) on your data. Training a custom model is a different specialism, costs 100x more, and is rarely the right call.

Question 2

How do you handle hallucinations?

Accepted Answer

Three layers. First, retrieval-grounded answers (RAG) so the model cites actual documents. Second, eval sets — we test against known-good Q/A pairs every release. Third, fallback to human review for high-stakes outputs. No agency that says 'we eliminated hallucinations' is being honest; we minimize them and design around the residual risk.

Question 3

What about data privacy?

Accepted Answer

Your data stays in your stack. Retrieval databases (Pinecone, pgvector) are on your accounts. API calls to Claude / GPT / Gemini run with the no-training opt-out flag. For sensitive data we route through Bedrock or Azure OpenAI in your tenancy.

Question 4

How much does the AI part cost to run?

Accepted Answer

Token costs vary wildly by use case. A typical RAG chatbot runs $50–$300/month. Public-facing generators with anonymous traffic can hit $500–$2,000/month and need rate limits and caching. We model the unit economics in the proposal and propose model routing (cheap model for easy queries, expensive model for hard ones) to keep margin.

Question 5

Can the AI integrate with our existing systems?

Accepted Answer

Yes. Most AI tools we ship sit on top of CRMs, Slack, Notion, your databases, or your knowledge base. Tool-calling and function-calling are first-class.

Question 6

What's included in the starting price of $1,999?

Accepted Answer

A focused AI tool — typically a chatbot trained on a single corpus, a generator with one structured output, or a Slack copilot for one team. Includes prompt engineering, retrieval setup, deploy, eval set, and 30 days of post-launch tuning.

Question 7

How do you handle prompt injection and abuse?

Accepted Answer

Input sanitization, output validation, rate limits, and a per-session token cap. For public-facing tools we add abuse detection and CAPTCHA on the email gate. We've shipped public AI tools handling 1,500+ runs without an incident.

Question 8

Will the AI capability stay current as models improve?

Accepted Answer

Through the Always-On Partnership retainer (AI Ops tier), yes — quarterly model upgrades, prompt tuning, eval re-runs, and capability expansion. AI without ongoing tuning regresses within 6 months.

Sub-service	What it is
Custom GPT / RAG chatbot trained on your data	Your docs, FAQs, pricing — answered with citations, not hallucinations.
Lead-qualification chatbot on your website	Replaces the contact form. Captures, qualifies, routes — 24/7.
Public-facing AI generators (cover letter / SOP / quote / calculator)	Lead-magnet tools that earn email addresses while users get value.
AI-powered site search	Semantic search that actually finds what users meant, not what they typed.
Internal team copilot (Slack bot, Notion AI)	AI that lives where your team already works.
AI document summarization + extraction	Bulk-process PDFs, contracts, and forms — structured output.
AI email drafting + reply assistant	Drafts in your voice, sends after a human approves.
Voice agents (inbound / outbound)	Twilio + LLM — for triage, scheduling, and qualification calls.
AI SEO content pipeline	Briefs, drafts, edits, schema — at speed, with a human in the loop.
AI image / video generation for marketing	On-brand assets at the cadence social demands.

01	We use AI as a capability under a craft anchor — not as the headline. The build is the proof.
02	RAG done right: vector store, retrieval, citation, eval set. Not just an OpenAI prompt.
03	Cost-aware by default — model routing, caching, and token budgets are scoped from day one.
04	Human-in-the-loop where it matters. We don't ship AI that approves its own work on real money.

Stage	What happens
01 Discover	We map the buyer, the offer, the success metric, and the constraints. The proposal is what we agree to ship.
02 Design	Wireframes, then high-fidelity in your brand. We test the user flow before we build it, not after.
03 Build	Two-week sprints with a working demo at the end of each. You see progress weekly, not at the reveal.
04 Launch & Care	30 days of free Care post-launch. Then optional Always-On Partnership, the same team that built it runs it.

AI Tools & Assistants for businesses that want AI doing real work — not just a chatbot in the corner

Every sub-service inside AI Tools & Assistants

What you don't get from a typical web shop

Four stages. Honest timelines. No reveals.

What we actually build with

Pikasso in production

GoAusbildung

Qogent Global

Plain words from people who hired us

Honest numbers, published

Four promises we keep on our worst week

Free AI Audit

Speed Promise

Always-On Guarantee

Capacity Window

Questions buyers actually ask

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