Build. Advise. Transfer know-how - under a model that fits the work.


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# engage.py
role = "build" | "consult" | "train"
billing = "hourly_capped" | "fixed_scope" | "package"
start = discovery_15m   # agree role + billing before deep work

# Remote by default. On-site only when the work truly needs it.
# AI tooling rules → How I Work (secrets never leave without agreement).

Roles

Build

Own a workstream and ship production systems.

Best when: ongoing delivery

Consult

Fast clarity on architecture, process, or AI delivery.

Best when: a decision this week

Train & hand off

Workshops, pairing, and assistants your team keeps.

Best when: capability must stay in-house

Paths can be combined over time.

Billing

Model Best when You get
Time-based (hourly, often capped monthly) Ongoing product work; scope still evolving Flexible priorities; clear reporting of accepted hours
Fixed scope A defined outcome (API, agent, RAG setup, slice) Agreed scope, acceptance criteria, and price
Package / task Spike, review, training day, architecture session Fast start, limited cost, easy to approve
# example_mix.py
pipeline = [
    ("discovery", "fixed"),      # align on problem & fit
    ("build", "hourly_capped"),  # report → you accept → invoice
    ("handoff", "package"),      # training day or assistant setup
]
# Rates agreed on the discovery call — not published here.
# Unused monthly hours are not billed. Over-cap needs your OK first.

How collaboration runs

1. Discovery

Problem, constraints, role and billing fit

2. Alignment

Goals, milestones, access — How I Work

3. Delivery

Slices you review and accept against agreed criteria

4. Handoff

Docs; optional training. You own IP of accepted deliverables.


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Book a Free 15-min Discovery Call

📧 [email protected]

💻 GitHub: codemonkey-df

Accepting new contracts for 2026.

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