Understand the work
Interview account, strategy, creative, production, operations, and the senior person everyone asks when the answer is hard to find.
About Alderson
Alderson is a small technical lab for experiential teams.
We help agencies turn scattered project data into AI agents for the jobs their teams repeat.
The starting point is not a strategy deck. It is the real work: pitches, recaps, estimates, production plans, field notes, approvals, photos, budgets, and the operator memory that keeps the team moving under deadline.
Why Alderson exists
Your best work should not stay trapped in memory.
Experiential teams already have years of valuable material. The problem is that it is rarely in a shape AI can use.
Final decks sit beside drafts. Proof shots are not tagged by rights or approval status. Budget logic is detached from the project story. Vendor notes live in chats. Recap evidence gets rebuilt after every event. Senior operators become the search engine for everyone else.
Prepare the source
Separate final from draft, current from stale, approved from risky, useful from noise, and sensitive from safe-to-use.
Design the agent
Define the job before adding AI: inputs, outputs, review points, owner, examples, and baseline.
Build the agent
Ship a focused internal agent for one repeated job, with source references, limitations, and human review points.
Train and operate
Help the team use the agent in the work they already do, then monitor adoption, data quality, and the next build.
AI starts with clean project data.
Agents can only be trusted when the inputs are current, approved, and clear about their limits.
One job is enough.
A small repeated job can show whether the approach works before a broader AI program starts.
Human review is part of the system.
Review points belong in the workflow from the beginning, especially near client-facing, financial, legal, safety, or procurement work.
The first agents should sit near revenue or delivery.
Pitch proof, recap assembly, estimate precedent, and production planning are better wedges than generic AI training.
Experiential work has domain details generic tools miss.
Venues, weather, staffing, approvals, sensory context, vendor history, photos, proof, and field data all matter.
Useful systems make the next build easier.
Each data cleanup and agent build should improve the foundation for the next one.
Client proof stays controlled.
Alderson does not publish client names, logos, exact budgets, private results, or confidential workflow details without written approval.
Public proof can include
- Approved workflow teardown
- Anonymized source-readiness example
- Before and after source inventory
- Approved client quote
- Metric from a measured workflow
- Public demo using non-client material