Hazel & Helm’s four-week AI Operations Sprint starts at $2,500. It includes reviewing three workflows, selecting one opportunity, building or testing one bounded improvement, a team handoff, and a 90-day roadmap. Final scope and price are agreed before work begins.
That is the direct answer. The more useful answer is why two projects that both sound like “automate our follow-up” can have completely different budgets.
What you are actually paying for.
AI implementation is often described as though you are purchasing a quantity of intelligence. You aren’t.
You are paying for someone to understand a business process, choose an appropriate approach, configure or build the workflow, test it with real examples, manage risk, help people use it, and document what happens next.
The AI feature may be a relatively small part of that work.
I learned this long before the current AI rush. In web implementation, a request that sounds like “add a form” might mean placing six fields on a page. Or it might mean conditional logic, payment, CRM routing, notifications, document generation, permissions, spam protection, analytics, and training. The visible feature is not the scope. The path behind it is.
The same is true here.
How clearly the workflow is defined.
A business that can show where work starts, who handles it, and what “done” means is easier to scope than one where every person describes a different process.
Discovery is not wasted money. But if the process is still being invented, the project needs more discovery.
How many systems need to connect.
A workflow inside one well-supported platform is usually simpler than one moving across email, spreadsheets, a CRM, accounting software, and a custom database.
Every connection creates questions:
- Is an integration available?
- Does the current subscription include it?
- Can the right fields be accessed?
- How is authentication managed?
- What happens when one system is unavailable?
- Who owns each account?
“It should connect” is not the same as “it can connect securely on the plans you have.”
How much data preparation is needed.
An AI drafting assistant grounded in ten current, approved documents is different from one expected to search eight years of shared-drive material containing duplicates and outdated policies.
Data cleanup, document review, labeling, migration, and permissions can easily become a project of their own. If those costs are hidden during sales, they appear later as delays.
What level of risk and review is involved.
A system that drafts internal meeting summaries does not need the same controls as one handling sensitive records or influencing a consequential decision.
Higher-risk work may require specialist security or legal advice, stronger testing, detailed logging, access controls, and more human review. That costs more because it should.
Whether the work needs custom software.
Mainstream tools can make a first implementation faster and less expensive. They also come with subscription costs, usage limits, and vendor constraints.
Custom software offers more control but requires engineering, hosting, testing, maintenance, and someone who can support it later. A small business should have a real reason before choosing that path.
How many exceptions the workflow has.
The happy path is usually easy to demonstrate. Cost lives in the exceptions.
What happens if the submission is incomplete? If a customer belongs to two categories? If the source document is unreadable? If the AI is uncertain? If the normal approver is away?
A useful workflow needs answers. The more variation, the more design and testing it requires.
How the team will adopt the change.
A technically successful build can still fail if it adds steps, appears in the wrong tool, or asks employees to trust an output they cannot evaluate.
Training, documentation, feedback, and workflow refinement are part of implementation—not optional decorations added at the end.
What an implementation estimate should include.
When comparing proposals, ask whether the estimate includes:
- Discovery and workflow mapping
- Tool selection and configuration
- Integration setup
- Testing with representative examples
- Revisions after team feedback
- Documentation and training
- Subscription or usage fees
- Ongoing monitoring and maintenance
- Data preparation or migration
- Security, legal, or compliance review
- Support after launch
Also ask who owns the accounts, configurations, documentation, and resulting workflow. A low initial price becomes less appealing if the business cannot operate the system without the original vendor.
Why a bounded engagement helps.
Nobody can responsibly promise exact savings or a final implementation outcome before seeing the workflow. At the same time, “We’ll investigate indefinitely at an hourly rate” is not a comfortable starting point for a small business.
A bounded engagement sits between those extremes.
The scope should define:
- How many workflows will be reviewed
- How one project will be selected
- What kind of implementation or proof of concept is included
- What is explicitly outside scope
- How much client participation is required
- What the team receives at the end
- Which costs require separate approval
That gives both sides room to make an informed choice without pretending every unknown is already known.
How to start with a smaller project.
Start with one workflow, not a department.
Use representative examples rather than migrating an entire archive. Work with existing software when it can do the job. Put one person in charge of decisions. Agree on the human review step early. Separate “useful first version” from “everything we may eventually want.”
And be willing to hear that the first fix is simpler than AI.
Paying someone to confirm that a form, rule, or built-in feature solves the problem is less exciting than commissioning a custom system. It is also considerably cheaper.
How to plan your AI implementation budget.
There is no honest universal price because the scopes vary too widely. A consultant may charge hourly, by project, through a fixed discovery engagement, or with an ongoing retainer.
For planning purposes, separate the budget into four buckets:
- Assessment: understanding and prioritizing the work
- Implementation: configuring or building the workflow
- Tools: subscriptions and usage charges
- Ownership: training, monitoring, maintenance, and future changes
If a proposal only prices the build, ask who is covering the other three.
You can review exactly what Hazel & Helm includes on the AI Operations Sprint page. If you have a workflow in mind, tell us what is getting stuck; the introductory conversation is for fit and scope, while the detailed assessment happens in the paid Sprint.
Start with a clearly defined workflow, decide whether existing tools or AI fit the job, and plan how to measure early results.
Frequently asked questions.
How much does Hazel & Helm’s AI Operations Sprint cost?
Engagements start at $2,500. The final fee depends on the selected scope and is agreed before work begins. Third-party subscriptions, major data cleanup, custom software, and complex integrations are outside the standard Sprint and can be scoped separately.
Are software subscriptions included in implementation fees?
Usually not. Subscription and usage fees are generally paid directly to software providers. Ask which tools are required, which plan levels are needed, and how costs could change as usage grows.
Does custom AI software cost more than using existing tools?
Usually. Custom development involves engineering, infrastructure, testing, maintenance, and ongoing support. Existing platforms can reduce initial cost when their capabilities and constraints fit the workflow.
Can an AI consultant guarantee savings?
Not before the workflow is assessed and measured. A responsible project establishes a baseline, tests the change, and evaluates early results. Some benefits may take longer than the initial implementation period to confirm.