Starter
- 1 AI feature, up to 3 screens, one model wired in
- Web platform, deployed to a live URL
- Full source code in your repo
- 2 revisions
Atlas is a support agent for a fictional SaaS, and every move it makes is on the record. Ask it anything: watch it plan → retrieve → rank → compose, with per-chunk retrieval scores drawn as live bars. Flip few-shot and chain-of-thought and the pipeline visibly changes shape — then open “why this answer” and audit the exact chunks it used. Most AI gigs show screenshots of a workflow canvas. This one runs.
A delivered knowledge bot citing its source PDF and page — the buyer-facing face of the retrieval trace Atlas opens up.
RAG · citations
LeadPilot, an AI lead-scoring dashboard — ranked, scored AI interfaces like the retrieval bars are home turf.
Scored · ranked UIs“The bot worked, mostly. We paid, said thanks — and quietly started shopping for the next developer.”
“We're on developer number three. The first two billed us and couldn't fix the thing.”
“The listing was AI-generated word salad — typos included — from someone selling AI expertise.”
“We couldn't tell what we'd bought until delivery. Buzzwords in, wrong-answer chatbot out.”
The few-shot and chain-of-thought toggles above Atlas's chat are prompt engineering made visible — same question, different pipeline, measurably different answer.
The retrieve step's per-chunk score bars and every citation chip under Atlas's answers are this service running live — grounded on docs, not vibes.
The rank step and the 0.55 threshold readout in “why this answer” are the evaluation muscle — scoring candidate passages and keeping only the ones that survive.
Ask Atlas the off-corpus roadmap question and watch it refuse below threshold — knowing where AI shouldn't answer is the half of consulting most vendors skip.
The whole glass box — chat, trace, metrics, and inspector wired into one surface — is what “an AI feature inside your product” looks like when it's finished, not mocked.
The compose step drafting only from ranked chunks is the same grounded-generation loop our volume pipelines run — with a human checkpoint where it matters.
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Star walls prove people paid. None of them show the agent loop executing. You just ran one.
Same loop, your docs: retrieval that cites sources, refusal below threshold, and a trace your team can audit. Scoped on one page, priced fixed, shipped to your repo.