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Paper Crane / services / ai-consulting / ai-automation
Some work is too large, too repetitive or too constant for a team to keep up with. That is the work we automate, using your own data and the systems you already run.
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Note
What AI automation actually means
It does not mean a chatbot, and it usually does not mean replacing anyone. It means taking a job that a person currently does by hand, over and over, and building something that does it continuously and correctly instead.
The interesting cases are the ones nobody could staff. A database that has to be re-checked every week to stay accurate. A queue of submissions that needs reading before anyone can act on it. A research task that has to happen for every new record. These were never done badly by the people doing them. There were just never enough hours.
The engineering problem is almost always reliability rather than capability. Getting a model to do something once is easy. Getting it to do the same thing correctly a million times, and knowing when it has not, is the actual work.
What we build
01
Parsing and enrichment pipelines
Turning unstructured sources into structured records at scale, with validation at each stage so bad output is caught rather than stored.
02
Agents and harnesses
Purpose-built agents for a specific job, wired into the CRM or the system where the work actually lives, rather than a general assistant nobody has a use for.
03
Moderation and review queues
First-pass review on volumes that a person cannot get through, structured so a human still makes the decisions that matter.
04
Lead and pipeline work
Gathering, qualifying and enriching pipeline data continuously, including the RFP pipelines we run for agencies and for ourselves.
05
Content operations
Automated updating, rewriting and de-identification of records where doing it by hand across a large dataset was never realistic.
06
Grounded internal tools
Tools that answer from your own data rather than from the open internet, which is the difference between something a team trusts and something they check twice.
Note
Lexter
When the GPT API was months old, we built a parsing pipeline for Lexter that read law firm websites across the country and turned them into structured records: names, titles, practice areas, credentials, bios. It became the largest lawyer database in Canada, and unlike the directories it competed with, it stayed current.
Part of the pipeline edited rather than extracted. It produced de-identified versions of bios and positions, removing the details that reveal who someone is while keeping the substance of what they had done, which made blind assessment possible on records never written for it.
It ran millions of times. That is the scale at which this stops being a productivity tool and starts being infrastructure.
01Project example
Tools grounded in their own data
We took a creator agency off spreadsheets and per-seat fees onto a bespoke operating system, with batch invoicing, a passwordless creator portal, and AI tools that answer from their own records.
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Common questions
How do you know the model is not getting it wrong?
Validation at each stage, and a design that assumes it will be wrong sometimes. On a pipeline running at scale, the question is never whether there will be bad output. It is whether bad output gets caught before it is written down.
Is this going to replace people on our team?
Not in the work we take. Almost everything we automate is work nobody had time to do properly in the first place, or work that grew past what a team could hold.
Do we need to be on a particular model or vendor?
No. Where it makes sense we build so you can route across providers, because the pricing and the capability keep moving and rebuilding every time is expensive.
What does this cost to run?
Model usage is a real operating cost and we size it before you commit, including what it looks like at ten times the volume. A pipeline that is affordable in testing and ruinous in production is a design failure.
Do we own it?
Yes. The code is yours, on infrastructure you control, and it is written to be readable by whoever maintains it next.
Next step
Tell us what nobody has time to do
If there is a job in your organization that is too big, too repetitive or too constant to keep up with, describe it to us. We will tell you honestly whether it is worth automating.