CANOPY digital

AI integrations

AI systems that connect to real work.

Consulting and implementation for teams adding chat, API-backed AI, agent workflows, evaluation harnesses, and developer orchestration to existing products without turning production into a science project.

Where it fits

Practical AI integration paths.

The work can start as a prototype, a rescue pass, a developer workflow, or a production integration review.

Chat and API integration

Connect product surfaces to model APIs, chat flows, retrieval layers, internal tools, and human review paths without hiding risk behind a demo.

Agent setup and harnesses

Plan agent workflows, evaluation harnesses, prompt contracts, tool boundaries, logging, and fallbacks before they become production support issues.

Workbench orchestration

Bring Claude, Codex, Hermes-style routing, local model helpers, and repeatable developer workflows into a team process that can be audited.

Consultant setup diagram

From intake to production handoff.

A useful AI integration has a boundary, a test surface, and an owner. The model provider is only one part of the system.

  1. 01

    Intake

    Capture the business workflow, data boundaries, users, risk, and success criteria before picking a model.

  2. 02

    Knowledge brief

    Turn approved copy, examples, policies, service details, and exclusions into a maintained reference pack.

  3. 03

    Model and tool boundary

    Decide which provider, prompt contract, retrieval path, tool calls, and fallbacks belong in version one.

  4. 04

    Review harness

    Test common prompts, off-topic behavior, spam, rate limits, logging, and human review before public launch.

  5. 05

    Production handoff

    Document environment variables, monitoring, limits, update procedures, and owner responsibilities.

Experience map

APIs, agents, harnesses, and handoff.

  • OpenAI API and chat-style product integrations
  • Claude, Codex, and server-side model-backed implementation workflows
  • Hermes-style orchestration, prompt routing, and harness design
  • Retrieval, evaluation, and failure-mode planning
  • Secure boundaries around secrets, user data, and tool calls
  • Documentation that lets engineering and operations own the system

AI references

Related AI chat, intake, and writing

Setup path AI integration readiness checklist A consultant-led path from business use case to safe public or internal deployment.Chat Canopy AI chat A constrained chat surface for Canopy-related questions, gated behind provider environment config.Writing AI workflow articles Reference notes on model APIs, agent setup, harnesses, and local-first developer workflows.

Static consulting page

AI work should be boring where it matters.

This page describes consulting and implementation capability. The separate AI chat route is env-gated and returns a safe unavailable state when provider config is missing.

Talk through an integration