Goldstone AI Solutions
Put AI to work in your business.
We design, build and manage AI systems that connect to the operations you already run, automate the work that repeats, and help the business do more without adding people to do it. Real systems wired into your tools, not a chatbot bolted onto the side.
Why this is hard
Most businesses already have AI. Almost none of it is connected to anything.
Buying access to a model is the easy part and it is where most companies stop. The difficulty is the part underneath: knowing which work is worth automating, getting the system permission to read and write the right records, connecting tools that were never designed to talk, and deciding what a machine is allowed to do without asking.
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The same information gets typed into two systems every day.
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Software that does not talk to other software.
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Business data nobody has time to look at.
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Documents read and re-keyed by hand.
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Customer questions answered from scratch every time.
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Administrative work expanding faster than revenue.
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AI tools in the business that touch none of its operations.
What we build
Six ways AI shows up in a working business.
None of these is a new product line. Each one is delivered through an offer Goldstone already runs, which is why the card tells you where it comes from. The bands below price the AI work itself; the offer underneath it is what you actually hire.
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AI opportunity assessment
A paid review of how the business actually runs: where the repetitive labor is, what the existing software can and cannot connect to, whether the data is in a usable state, and which applications are worth the money. You leave with a ranked opportunity list and an implementation plan, whoever builds it.
Delivered as: Goldstone Systems Blueprint -
AI workflow automation
One business problem, automated end to end. Routing inbound enquiries, qualifying leads, creating and updating CRM records, processing invoices, moving documents to the right place, producing the status summary somebody writes by hand every Friday.
Delivered as: Business Systems & Automation -
AI agents and operational assistants
An agent that handles a defined job across several steps: sales operations, support triage, document research, reporting. It runs inside explicit permissions, an approved set of tools, a usage budget and an audit log, and consequential actions wait for a person.
Delivered as: Business Systems & Automation -
AI knowledge systems
Your own documents, procedures and manuals made answerable, with permissions respected and every answer carrying its source. Built to say it does not know rather than to produce something plausible, because a knowledge system that guesses is worse than no knowledge system.
Delivered as: Custom Apps -
Custom AI applications
When no workflow tool fits the problem: analysis platforms for a specific industry, operational software with interpretation built into it, dashboards that read the data rather than display it. Usually a module inside a larger system rather than a thing standing on its own.
Delivered as: Custom Apps · Business Operating Systems -
Managed AI operations
What happens after launch. Reliability monitoring, failure detection, output quality checks, model and usage cost reporting, integration maintenance as the tools around it change, and a person to escalate to when something is wrong.
Delivered as: Care · Managed · Growth Intelligence
In practice
What this looks like in a real operation.
Illustrative applications of the categories above. None of these describes a system Goldstone has already deployed for a customer in that industry.
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Service business
Classify inbound enquiries as they arrive, prepare a first estimate, update the CRM record, and schedule the follow up nobody remembers to send.
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Land and real estate
Process incoming lead information, pull terms out of documents, organize acquisition workflows, and assemble the material an underwriting decision needs.
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Home care and in home services
Support scheduling administration, handle intake documents, and run approved internal workflows. Regulated handling is assessed per project before anything is built.
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Professional services
Assist document review, research a question across your own files, draft recurring reports, and carry a new client through onboarding without a person retyping anything.
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Multi location business
Centralize reporting across sites, route work to the right location, support staff with a consistent answer, and keep one process running the same way everywhere.
How it runs
Four stages, in that order.
The order matters more than the stages. Most failed AI projects were deployed before anybody checked whether the thing was worth automating.
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01
Assess
Understand the operational problem before proposing anything. Which work is repetitive, what a correct output looks like, what the manual version costs today.
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02
Pilot
One workflow, bounded scope, measured against the before. Cheap enough that finding out it does not work is a good outcome rather than an expensive one.
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03
Deploy
Integrate it properly, test it against your real data, put the approval gates and logging in place, and release it deliberately.
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04
Manage
Monitor reliability and cost, check output quality, maintain the integrations as the tools around them change, and improve it as the business changes.
Pricing
What AI work costs.
Published in three levels, like everything else Goldstone sells. What moves a project into a higher range is not how advanced the model is: it is how many of your systems the work has to touch, and how much judgment it is allowed to exercise without a person.
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AI Workflow Automation
AI doing a defined job inside an existing workflow: classifying, drafting, extracting, routing or summarizing, with a person still approving the output.
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Operational AI / Agent Systems / Custom AI Applications
Agents that act across several systems under explicit permissions, and applications built around a model rather than merely calling one.
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Enterprise AI Platforms / Multi-System Implementations
Implementations spanning multiple business systems, with the evaluation, access control and oversight an enterprise rollout requires.
Custom
Complex projects are scoped individually based on technical complexity, integrations, security requirements, data migration and business impact.
These are implementation figures. Everything that recurs is listed below and quoted separately, because an AI system has a running cost that moves with use and a build price that does not.
Scope and cost
What you are quoted, and what is separate.
An AI project is quoted in layers, and not every project has all of them. Keeping them apart is what lets you see which number is one time, which is recurring, and which one moves with how much the system is used.
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Assessment
A paid Systems Blueprint where scope is uncertain, credited against the implementation under the published policy.
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Implementation
The build itself, quoted against the floors already published for the service it belongs to.
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Software license
Where a reusable Goldstone platform is part of the system. Licensed rather than sold, and not every project has this layer.
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Ongoing service
An existing plan, where you want the system monitored and maintained rather than handed over.
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Model and infrastructure cost
Billed by the provider per request, plus whatever the system runs on. Yours, itemized, and never folded into the build price.
What recurs, and why it is separate
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Goldstone ongoing service
An existing Care, Managed or Growth Intelligence plan, where you want the system maintained rather than handed over. Always optional.
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Software or platform license
Only where a reusable Goldstone platform is part of the system. A build made entirely for you carries none.
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Model and API usage
Billed by the provider per request. It scales with use, so it is quoted as a rate and a projection rather than a fixed monthly figure.
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Hosting and infrastructure
What the system runs on. Usually modest, and usually yours directly rather than through us.
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Third-party tools
Anything the workflow depends on that we did not build: your CRM, your telephony, your document storage.
None of these is folded into the implementation figure. A complex or enterprise AI platform is scoped per project, so its implementation number comes out of discovery rather than this page.
See published pricingWhere we actually stand
What Goldstone has built.
Active paid client build · Advanced implementation
The systems AI has to plug into.
RoverCT is a mobile veterinary CT service for which Goldstone is building the connected operating system: public site, practice portal, scheduling, service areas, records, invoicing, a payment workflow and an operator dashboard, with several organizations coexisting safely in one system. 23 screens and 172 passing automated tests.
That is the hard part. An AI feature is only as useful as the permissions, records and workflows it can reach.
Read the RoverCT case studyWhat we are not going to show you.
A customer AI result with a percentage attached to it. Goldstone sells AI work today as modules inside the systems above, and the standalone AI case study does not exist yet. Inventing one would be easy and we would rather you found that out from us.
What you can check is the method: published prices, a paid assessment that can conclude you should not build, a pilot sized so that a negative result is cheap, and approval gates on anything consequential.
What businesses ask before an AI project.
What can AI actually automate in my business?
Repetitive work with a checkable right answer automates well. Classifying inbound enquiries, pulling fields out of invoices and forms, drafting a first reply for someone to approve, keeping CRM records current, summarizing a week of activity. Work that needs judgment, relationships or accountability does not, and should not. The useful test is whether you could write down what a correct output looks like. If you can, the work is a candidate. If two people in the business would disagree about the right answer, it is not.
How are our data and permissions protected?
Your data stays in systems you control, and permissions are enforced in the database rather than in the interface. A person using an AI feature sees exactly what that person could already see, because the rule lives at the data layer where it cannot be bypassed by a different request. What we do not do is claim a compliance certification we have not been audited for. If your workflow is regulated, that needs a project specific review before anything gets built.
What happens when an AI gets something wrong?
Something will eventually be wrong, so the system is built assuming it. Anything consequential waits for a person to approve it. Everything the system does is logged, so a bad output can be found and traced rather than discovered later in a customer complaint. Which actions need approval is decided during the build, not left at a default. Money, contracts, legal commitments, anything that leaves the building and anything destructive sit behind a person.
How do AI model and API costs work?
Model usage is billed per request by the provider, so it scales with how much work the system actually does rather than sitting as a flat line. That cost is yours, quoted and itemized separately from the build and from any ongoing service, so you can watch each one move independently. Infrastructure the system runs on is separate again. Before anything goes live we estimate the monthly range at your expected volume, and the system reports its actual usage once it is running.
Can we start with one workflow?
Starting with one is the recommended way in. A single workflow with a measurable before and after proves the approach on your actual data, at your actual volume, for a fraction of the cost of a full program. It also tells us honestly whether the rest is worth building. If the pilot does not produce a result worth having, that is a useful answer and a cheap one. We would rather find that out on one workflow than on five.
Tell us what is slowing the business down.
Describe the work that repeats, the systems that do not talk, or the thing you keep meaning to fix. We will tell you whether AI is the right answer, and say so plainly when it is not.