AI consulting
AI features that do real work, measured and reviewed
AI consulting from senior engineers: where AI helps, features and agents built on Anthropic’s Claude models through the Claude API, Amazon Bedrock or Google Cloud, and Claude rolled out to your teams. Each feature is tested against real examples, reviewed by people and shipped with its limits written down.
- Typical timeline
- Scoped in the proposalStarting with a readiness review or a small pilot
- How it’s quoted
- IndividuallyAfter a 30-minute call
- Who edits it
- Your teamWith the prompts, evaluation sets and runbooks we hand over
- Stack
- Claude modelsThrough the Claude API, Amazon Bedrock or Google Cloud, in TypeScript, .NET or PHP
In your name Accounts and repositories, from day one Yours The code and documentation, under the signed agreement
On this page
Is it the right call?
When it’s the right call
- You want AI in your product, and a plain answer on where it helps and what it risks
- A process runs on documents, email or knowledge that people search through by hand
- Your systems need to give an AI assistant safe, limited access to tools and data
- Your teams are starting with Claude and need setup, a usage policy and training
When it isn’t
- Training or fine-tuning a foundation model of your own We’d suggest: A machine learning research team
- Decisions that must be fully automatic, with nobody able to check them We’d suggest: Keep them with people, or with rules you can audit
- A ready-made AI product already does the job well We’d suggest: Buy it, and we can connect it to your systems
Sound like you? The first step is a 30-minute scoping call with the engineer who would lead the work.Request a scoping call
The smallest way to start
- Fixed scope, usually 2–3 weeks
Discovery sprint
Research into your users, systems and constraints, the architecture decisions written down, and a written plan and quote, yours to take to any team.
Start with a discovery sprint What the written plan covers
Standard equipment
What we usually hand over on a project like this. Your proposal lists exactly what’s included.
- Amazon Bedrock
- Google Cloud
- Model Context Protocol
- TypeScript
- Claude API
- Claude Code
- .NET
- PHP
Readiness review
Where AI can help, the data it would touch, the risks and a phased plan, with a build-or-buy recommendation in writing.
Evaluation set
Real examples with the results you expect, so every change to a prompt, model or tool is measured before it ships.
AI features in production
Assistants, document processing or search over your own content, inside your software, with logging and a way for people to check results.
Tools and integrations
Tools, internal APIs and Model Context Protocol servers, each limited to the access its task needs, with every call logged.
Usage policy and training
Workspace setup, a written usage policy and training for your teams, including Claude Code for engineers.
Limits and runbooks
What the feature should and shouldn’t do, how it fails, how it’s monitored and who reviews what, in plain language.
Four ways we help
AI is worth adding where it does a real job: reading the documents people now read by hand, answering questions from your own content, or taking routine steps across systems. We build those features on Anthropic’s Claude models, and we start by asking whether AI is the right tool at all.
AI strategy and readiness
Where AI would help your business, and where it wouldn’t. We review the processes and data a feature would touch and the risks: privacy, security, wrong answers and what a mistake would cost. Then we compare building, buying an existing product and waiting. You get a phased plan in writing, with the first step scoped.
AI features in your apps
Claude-powered features built into the software you already run, through the Claude API directly or through Amazon Bedrock or Google Cloud: assistants that answer from your own content, document processing that reads and sorts what arrives, and search over your own content that understands plain language. They are built in your stack, such as TypeScript, .NET or PHP, and pass the same tests, reviews and quality gates as the rest of the code, whether that is a custom application or a system you already run.
Agents and integrations
Agentic workflows, where Claude works through a task in steps and uses tools, suit jobs with clear edges. We connect Claude to your systems through tools, your internal APIs and Model Context Protocol servers. The Model Context Protocol is an open standard that Anthropic introduced in 2024 and has since donated to the Agentic AI Foundation, a fund under the Linux Foundation. Each tool gets only the access its task needs, actions that change data can wait for a person’s approval, and every call is logged.
Team rollout and enablement
Rolling Claude out to your teams and developers: the workspace set up on a Claude Team or Enterprise plan, a usage policy people can follow (what may go in, what needs checking), training built around your own work, and Claude Code, Anthropic’s agentic coding tool, for engineering teams, with code review and quality gates kept in place. Rolling out Claude at a small company: a practical checklist →
How an engagement runs
Three stages, and you decide at the end of each whether to go on.
- Readiness. A review of the processes, data, systems and risks, ending in a written plan: what to try first, what it should achieve and what it must not do.
- A scoped pilot. One task, built against real examples. Before building, we collect an evaluation set: real inputs with the results you expect. The pilot is measured against it and reviewed by the people who do the work today. Its length and what it measures are scoped in the proposal.
- Production. The feature goes into your software with logging, monitoring and a way for people to check and correct its results. The evaluation set runs on every change, so a new prompt or model version is measured before it ships.
AI output can be wrong. Where an answer would affect a customer, money or a record, a person reviews it before it is used, and what the feature shouldn’t be trusted with is written down in plain language.
Examples of what we build
These are examples of the kind of work, not client projects.
- An assistant in a customer portal that answers from your help articles and the customer’s own account, and hands over to a person when a question is outside what it covers.
- Document intake that reads invoices, forms or applications, pulls out the fields and flags anything uncertain for a person to check.
- Search over internal documents, policies and tickets that takes a question in plain language and links the sources behind each answer.
- An agent that prepares routine work across systems, such as a reply drafted from an order history, for a person to approve.
- A Model Context Protocol server that gives Claude read-only access to an internal system, so staff can ask questions of it.
- A Claude Code rollout for an engineering team: setup, a usage policy and the checks that keep every change reviewed.
Data, security and privacy
Training. Anthropic’s published policy is that, by default, it doesn’t use inputs or outputs from its commercial products, including the Claude API and the Team and Enterprise plans, to train its models. That changes if your organization reports feedback or bugs, or otherwise chooses to allow it, and owners of Team and Enterprise plans can turn off the feedback button for their members.
Retention. For the Claude API, Anthropic’s standard practice is to delete inputs and outputs within 30 days, except for services you choose that keep data longer (such as the Files API), a separate agreement, enforcement of its usage policy and legal requirements. Enterprise plans can set custom retention periods. Some Claude API and Claude Code Enterprise customers can have zero data retention, subject to Anthropic’s approval; safety classifier results are still kept, and the models Anthropic designates as covered models have their own 30-day retention for safety review, which can apply even with zero retention.
Your cloud. Through Amazon Bedrock or Google Cloud, Claude is reached with your cloud account’s credentials and access controls, and billed on your cloud bill. We help you choose the route that fits your data and the setup you already have, alongside the cloud infrastructure it runs in.
Our side. We set up access so a feature sees only the data its task needs, keep keys and credentials out of code, decide with you what is logged and for how long, and follow any limits your agreement sets on AI tools. The vendor terms above are Anthropic’s, as published on 25 October 2026; they can change, so we check them with you at the start of each project.
Claude and Anthropic are trademarks of Anthropic. SnowDriven isn’t affiliated with or endorsed by Anthropic.
Questions about AI consulting
Is our data used to train Claude?
Not by default. Anthropic’s published policy is that it doesn’t use inputs or outputs from its commercial products, including the Claude API and the Team and Enterprise plans, to train its models, unless your organization sends feedback or bug reports or otherwise chooses to allow it. We use only AI tools whose terms or settings keep client data out of model training, we go through those settings with you during setup, and we can point you to the policy we rely on.
Do we need a big data project before we start?
Not necessarily. A useful feature can often start from the documents, records and systems you already have, read through access limited to what the task needs rather than copied into a new platform. The readiness review checks what a feature would need, how complete and current that data is and who is allowed to see it, and it says plainly if something has to be fixed first.
How do you check an AI feature is good enough?
Before building, we agree what a good result looks like and collect real examples with the answers you expect: an evaluation set. Every change to a prompt, model or tool runs against it, and people review the results, not only an automated score. Where a wrong answer would matter, a person checks the output before it is used. What the feature shouldn’t be trusted with is written down. AI output can be wrong, so we design for checking rather than promise accuracy.
Can it run in our own cloud account?
Yes, if you prefer. Claude models are available through Amazon Bedrock in your own AWS account and through Google Cloud in your own project, billed on your cloud bill and under your cloud’s access controls. You can also use the Claude API directly. Features can differ between these routes, so we check what yours supports, compare the options for your data and existing setup, and recommend one in writing.
Are you affiliated with Anthropic?
No. SnowDriven is an independent studio, not affiliated with or endorsed by Anthropic, and we don’t hold any status in an Anthropic program. We work with Anthropic’s publicly available products, such as the Claude API, Claude Code and the Claude plans for organizations, on the terms Anthropic publishes, and we say so when another tool would fit the job better.
How long does a pilot take?
It depends on the task, the data and the systems the feature connects to, so the length is scoped in the proposal after the first call, together with what the pilot will measure and the point at which you decide whether to go further. A pilot is deliberately small: one task, real examples and a clear test of whether the feature is worth building.
Every build, any stack
One standard.
Performance
A performance budget is set before launch and enforced on every change.
Accessibility
Built to WCAG 2.2 AA and tested against it, with automated checks and manual keyboard and screen-reader passes on client builds.
Security
Least-privilege access, secrets kept out of code, dependencies monitored and security headers configured.
Handover
Accounts and repositories in your name from day one. The code and documentation are yours under the signed agreement, written so another team could take over.
Start a project
Tell us where AI might help.
A process, a product idea or “our team wants to use Claude” is a fine place to start. We reply within one business day, with questions or a time to talk.
Pittsburgh, PA · Since 2015 · First step: a 30-minute call