From the team behind aidrop.it — one workspace to build, host, and keep changing your code.
An independent management consultant was spending evenings turning the same raw notes into the same kinds of deliverables — readouts, proposals, board memos — from scratch each time. The fix wasn't a better template. It was an agent built on a memory of how she actually structures an argument, so it drafts inside her standards and she spends her time on judgment, not formatting. First drafts now start at 70% instead of zero.
The pattern generalizes to anyone who sells thinking by the hour: deposit how you work into memory, let the agent carry the first draft, keep yourself as the reviewer.
The problem: every deliverable started from zero
Consulting output is repetitive in form but bespoke in content. Each engagement reused the same frameworks, the same way of sequencing a recommendation, the same tone for a skeptical board — yet a generic AI rebuilt none of that. So either she wrote the first draft herself, or she spent as long correcting the AI's generic version as writing it. The leverage never materialized.
Why a generic chatbot didn't help
A model trained on the average of the internet produces average consulting — structurally fine, generically reasoned, missing her actual method. It didn't know which framework she reaches for, how she handles a weak data point, or what "good" looks like to her clients. Without that context the AI was a faster way to produce work she'd have to rewrite — the gap an AI twin is meant to close.
The setup: deposit the method, not the documents
Instead of pasting old decks, she captured decision-shaped facts: how she opens a readout, how she frames trade-offs, which arguments survived tough rooms and which didn't, the standards a deliverable has to clear. The memory connects to her tools over MCP — as she works, the agent records new patterns and merges them into a versioned store, so the memory reflects her current method, not last year's.
What the agent produces now
Hand it the raw notes from a client session and it returns a structured first draft in her format: her opening, her sequencing, her tone for the audience. It's not finished — it's a 70% draft that already looks like hers, so her time goes to the 30% that needs judgment: the contrarian read, the call she'd stake her name on. She edits and signs; the agent carries the scaffolding.
The result: hours back, standards intact
The win isn't "AI writes my decks" — it's that the unpaid, repetitive scaffolding stops eating her evenings while the quality bar holds, because the draft is built from her own recorded standards. Every correction she makes becomes a new memory entry, so the next draft starts closer to done. This is Workforce 2.0 in miniature: the agent handles volume, the human owns judgment.
How to replicate this
Pick one deliverable you produce repeatedly. Write down how you actually build it — structure, standards, the reasoning you apply — as facts, not a template. Connect that memory to your AI tool and have it draft from your notes; every time it misses, save the correction. The same play scales to a team — onboard the agent like a new hire — and the draft gets more "you" with every pass.
FAQ
Won't an AI draft sound generic? Only if it's working from a generic model with no memory of you. An agent that drafts from your recorded structure, standards, and reasoning produces work in your voice — the difference is the memory layer, not the model.
Why not just use a template? A template fixes format, not thinking. A memory captures how you reason — which framework, which trade-offs, which standards — so the draft is right in substance, not just layout, and it improves every time you correct it.
How does the agent stay current with how I work? It captures new patterns as you work rather than from a one-time setup. With a versioned store, observations are merged into the memory, so the agent always drafts from your current method, not a stale snapshot.
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