AI
Pick one workflow. Make it cheaper and faster. Then pick the next.
Most AI programs stall because they start with the technology and look for a use. We start with a workflow that has a measurable cost per unit, and we only keep what beats the number it replaced.
What’s included
Concrete deliverables, not capability statements. Anything not on this list is out of scope until we agree otherwise in writing.
- Workflow selection: we rank candidate workflows by volume, cost per unit, and tolerance for error
- Support triage: classification, routing, drafted responses with a human approving before send
- Sales research: account briefs assembled from your CRM and public sources before each call
- Content operations: drafting, repurposing, and tagging with an editorial review gate
- Document handling: extraction from invoices, contracts, and intake forms into structured fields
- Forecasting and anomaly alerts built on your warehouse rather than a vendor black box
- An evaluation harness with a labelled test set, so a model or prompt change is measured before it ships
Scope, and how we price it
Every engagement is scoped and quoted before any build starts. The Map phase produces a ranked list of what to do; the Plan phase turns the part you approve into a fixed scope, a fixed number, and named owners. You see the figure before you commit to the work it pays for.
We do not quote from a rate card, because the same service costs very different amounts depending on how much of your stack already works. What we will do on a first call is tell you the rough order of magnitude, so nobody spends a second call finding out we are the wrong size for each other.
You can stop after Map or after Plan. Both are complete deliverables, priced on their own, and useful even if the build goes to someone else.
How we do it
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Map
We measure the workflow as it runs today: volume, minutes per unit, cost per unit, and current error rate. Without that baseline there is nothing to compare against later.
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Plan
We define the acceptance bar, the human review gate, and the failure behaviour before choosing any model. We also write down what would make us stop.
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Build
We build the evaluation set first, then the system, then run both in shadow mode against live traffic while humans still do the work. Only then does it take load.
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Run
Monthly review of accuracy, cost, and drift against the baseline. Models and prices change quickly; the harness is what lets you switch without guessing.
A recent example
Hospitality
+14%
direct booking share over two quarters
A 210-room Gulf Coast resort — Joined PMS stay history to the campaign platform, then rebuilt pre-arrival and winback on behaviour instead of send date.
Read the full studyWhat we need from you
- Historical examples of the workflow, ideally a few hundred, with the correct outcomes
- A subject-matter expert for roughly six hours across the engagement to label edge cases
- A decision on the human review gate: who approves, and what they are accountable for
- A clear answer on what data may leave your environment, before we design anything
Questions we get asked
Will this replace people on our team?
In the workflows we take on, it usually removes the queue rather than the person. We will tell you plainly if a project would reduce headcount, because that changes how you should communicate it internally.
What about hallucination and wrong answers?
Every workflow ships with a measured error rate and a human gate wherever an error is expensive. We do not deploy generative output straight to a customer without review unless the error cost is genuinely near zero.
Does our data train someone else’s model?
No. We use enterprise API tiers with training disabled and data retention configured, and for regulated data we scope the design to your environment. The specifics go in the contract.
Which models do you use?
Whichever passes the evaluation for that workflow at an acceptable cost, and we re-test when a new one ships. The harness makes that a measurement rather than an argument.
What if it does not beat the baseline?
We report that and recommend stopping. That has happened. Finding out in six weeks for a fixed fee is the point of scoping one workflow at a time.
AI in your industry
- Hospitality Review triage and drafted responses across the platforms that matter, and forecasting that reads occupancy alongside pace rather than last year’s curve.
- E-commerce Support triage across pre- and post-purchase questions, product content generation with an editorial gate, and return-rate anomaly alerts by SKU.
- Retail Demand forecasting per location and per SKU, plus markdown timing recommendations reviewed by a merchandiser before they take effect.
Tell us what isn’t shipping.
Thirty minutes, no deck. Bring the thing that’s stuck and we’ll tell you how we’d approach it, whether or not you hire us.
Or email hello@fusionads.ai · Florida, US