Project work
The AI work that is too specific to come out of a box.
Project work: ScaleSight AI’s team builds a specific AI workflow, template, or agent for your business and hands it over working.
AI Build Projects is ScaleSight AI’s engagement work. The client names a piece of recurring work, and ScaleSight AI’s team builds the AI workflow, template, or agent that does it, then hands it over documented and in use. Managed AI is the managed service that runs your company’s AI month to month. AI Build Projects has a scope, a start, and an end, and is available with or without the managed service.
It depends on how your company quotes, estimates, schedules, or answers customers, and on data sitting in systems only you use.
ScaleSight AI’s team scopes that work in writing, builds it, tests it against your real past cases rather than invented examples, and hands it over with the people trained to run it.
What we deliver
The kinds of work we build
Most engagements combine two or three of these. The program management is not an optional line item. It is the difference between a project and a series of invoices.
Workflow build
The recurring, multi-step work that eats somebody’s week. As an example of the method: a request for quote arrives as an email with a drawing or a spec attached, and the workflow pulls out the parts, quantities, and dates, matches them against past jobs, and drafts the quote for your estimator to price and sign. The estimator still owns the number.
- Sitting with the team doing the work before designing anything
- Naming which work is worth automating and which is not
- Building the steps, the inputs, and the approval points
- Testing against real past cases rather than invented examples
- Writing down what the AI does, what the person checks, and what the AI does not do unsupervised
- Handover with documentation and trained users
Agents on your own documents
Assistants that answer from your material, such as policies, contracts, product data, and past work, rather than from the open internet.
- Choosing the sources, and excluding what should not be in scope
- Existing permissions carried through, so an agent cannot answer past what the person is allowed to see
- Defined behavior when the answer is not in the source: say so rather than guess
- Evaluation against the questions your team actually asks, with the failures written down
- Refresh as the underlying material changes
Templates for a department
The how-this-company-does-this-task artifacts: the quote, the estimate, the summary, the reply, the report. Captured from whoever already does it best, then made available to everybody.
- Capture how the task gets done by whoever does it best today
- Turn it into something anyone on the team can run
- Publish it into the shared workspace where the work happens
- Revise it once real use shows where it breaks
Connecting AI to the systems the work lives in
Most useful AI work needs data sitting in line-of-business systems. ScaleSight AI connects them, with the access rules you already have preserved rather than re-invented. Any connection like this is project work, never part of the managed service.
- CRM, ERP, ticketing, file storage, finance, and other line-of-business systems
- Read paths and write paths defined separately, so nothing writes back by accident
- Existing permissions preserved rather than rebuilt alongside them
- Logging, so what the AI did is reviewable afterwards
Rebuilds and model migrations
Workflows drift. Models change, prices change, and what worked in January is weaker by September. ScaleSight AI re-points, re-tests, and retires.
- Re-point a workflow to a newer or cheaper model
- Re-test outputs against the cases the workflow was accepted on
- Retire the automations that stopped earning their place
- Rework what was built on somebody else’s licenses
How we run them
Six commitments that decide whether a project lands
None of this is exotic. It is simply what is missing from most of the projects we get called in to rescue.
-
Scoped honestly, before the quote
We would rather lose the work than win it on a number we already know is wrong. An assessment that finds a problem is a cheaper outcome than a change order that finds it in week six.
-
Sequenced around your business
Discovery sessions, testing, and go-live land against your calendar, not ours. Month-end, busy season, and audit windows come out of the plan before dates are set.
-
One accountable owner
A named person responsible for the outcome, accountable for the plan and the dates. Not a coordinator who forwards emails.
-
A defined way back
Anything that writes into another system ships with a way to turn it off and a decision point for taking it. Discovering there is no path back is not a thing to find out after it has run for a week.
-
Written status against milestones
Where the project actually is compared to where the plan said it would be, in writing, on a schedule. Verbal reassurance is how projects get to eighty percent complete for three months.
-
Documentation handed over
What the workflow does, what the person checks, the prompts and templates behind it, and a runbook, delivered at closeout, so your team can run what we built without calling us.
And afterwards
A build is a good moment to decide who maintains it
Building one workflow surfaces things nothing else does: what data you actually have, where it lives, who touches it, and which processes are held together by one person and a spreadsheet. That is the same ground a managed AI engagement starts from.
There is no obligation and no bundle. But if you are already mapping one process end to end, it is a cheap moment to ask what you would want running on top of it.
Tell us what you are trying to build.
Send the scope, the constraint you are worried about, and the date it needs to be done by. We will come back with an honest read on whether that is achievable and what it would take, before anyone writes a proposal.