Service · 03 03 of 10

Chatbots and Virtual Assistants

Conversational interfaces that ship narrow, work daily, and earn their seat. Knowledge grounding so answers cite the source. Human handoff when the bot reaches its edge.


Most chatbot projects fail in one of two ways. They try to do everything and end up doing nothing well, or they’re built in a no-code tool that can’t be tested or version-controlled. We ship narrow chatbots that do one thing daily — and we ship them as code, not as a vendor’s UI.

What this looks like in practice

We pick the single highest-frequency conversation your team or customers have, and we automate that — not “everything Sales does,” not “all customer support questions.” Just the one with the most repetition. The bot lives as version-controlled prompt files plus a small agent runtime. It cites its sources when it answers. It hands off to a human when it’s not confident.

For a deeper teardown of the patterns that make narrow conversational interfaces actually work, we wrote up 14 patterns from a well-designed Codex skill — identity locks, subagent boundaries, provenance, no-fallback gates, deterministic packaging. The same instincts go into our chatbot builds.

Who this fits (and who it doesn’t)

Fits: teams with a clear repeated conversation that’s swallowing time. Teams where “the answer exists in our docs but no one reads them.” Teams that want a bot they can audit when something goes wrong.

Doesn’t fit: teams that want a bot to “handle customer service” with no further specification. We can’t ship that; no one can.

The first conversation

We’ll ask what conversation the bot would replace. If you can describe it in two sentences, we can ship in weeks. If you can’t, we’ll spend the first conversation pinning it down before we quote a build.

From the Field Journal

  1. Research

    Source-Backed Memory for AI Coding Agents

    JEV Context Fabric keeps exact, hashed source text as coding-agent memory and marks retrieved excerpts as untrusted and possibly stale.

  2. Research

    Finding Where AI Decisions Actually Improve Existing Software

    An offline evaluator that finds judgment calls in code, gates every change behind approval, and runs studies allowed to say no. Five of six did.

  3. Prototype

    Pruning Repeated File Reads From Coding-Agent Context

    An experimental kit that drops exact repeated file reads from coding-agent requests, leaving a verified marker and the original transcript intact.

  4. Prototype

    Designing Deterministic AI Moderation for Discord

    How TypeCheck keeps a probabilistic model out of Discord moderation decisions: typed answers, pinned versions, one policy engine, and human review.