Start with the manual task, not the technology
The clearest sign of a good AI candidate is an employee who does the same thing every day with text, images, or spreadsheets. Sorting incoming requests, triaging tickets, rewriting product descriptions, reviewing documents, answering the same questions in chat over and over.
Before you call a developer, do a simple exercise. Pick one of those tasks and work out: how many times it happens each day, how long each instance takes, and what that person's time costs per hour. That gives you the monthly cost of the operation - your budget ceiling for automating it, and your best argument in a conversation with a vendor.
If the task costs you relatively little each month, an expensive development project will never pay off. If it is consuming forty hours a week across three people, the math gets interesting. That calculation takes half an hour and kills half the bad ideas before they start.
Use cases that deliver consistent results
There is a class of tasks where modern language models deliver stable results almost immediately. What they share: the input is text or an image, the output is structured data or a short answer, and errors are easy to spot.
Notice that in none of these scenarios does the AI make a final decision on its own. It prepares a draft; a person confirms. That is what keeps the system from becoming a source of problems.
- Parsing incoming requests and emails: extract the name, contact details, and nature of the request, assign a category, route to the right team or CRM.
- Answering common customer questions against your knowledge base - with a clean handoff to a human when the model is uncertain.
- Document processing: invoices, contracts, resumes. The model extracts the fields; a person reviews the ambiguous ones.
- Content drafts: product descriptions from spec sheets, emails, posts. Editing stays with you.
- Semantic search across a large archive - contracts, support tickets, internal policies - when keyword search fails to surface what you need.
Where AI is not needed - better to say so upfront
If a task can be solved with rules, solve it with rules. Routing a request based on a single form field, calculating a price from a rate card, validating a phone number format, sending a template email after payment - all of this is done with ordinary code, runs instantly, costs nothing per call, and does not make mistakes.
AI is not the right fit for tasks where an error is expensive and no one will catch it. Automated tax calculations, legal conclusions, medical outputs, final financial decisions with no human in the loop - the model will confidently produce a plausible wrong answer, and you will be the last to find out.
A separate case: you simply do not have the data. The idea of letting AI predict demand based on forty transactions a year will not work - there is nothing to predict from. An honest vendor will suggest building out data collection first rather than selling you a model.
And the most common one: we need AI because our competitors have it. That is not a use case. Projects like this almost always end up as a button nobody uses.
How costs break down
A budget for an AI service differs from a website budget in one key way: there are two types of costs - a one-time development fee and recurring charges for using the model.
We publish starting-price ranges for each service area on our website - Telegram bots, web services, e-commerce, support. AI functionality within a project is typically scoped as a separate work block on top of the base price for that area. An exact quote comes after a briefing, because the cost depends on how many data sources need to be connected and how strict the result-checking needs to be.
The one-time portion covers: scoping the task and use cases, integrations with your existing systems, preparing the data and knowledge base, building the model wrapper, an operator interface, and testing against your real examples. That last item is often underestimated, but it is critical - without running the system through a hundred of your actual requests, you do not know whether it works.
The recurring portion is the cost of model API calls. It depends on text volume and request count, so it can be estimated upfront: share your expected monthly volume and ask the vendor to work out the rough cost. If they cannot do that, they do not understand what they are building.
How to choose a vendor and what to ask
A good test: ask them to start with a pilot on your real material. Not a presentation - actual processing of thirty of your requests or documents, with results you can see. It is inexpensive, takes little time, and immediately tells you whether there is a reason to continue.
Questions worth asking before you sign:
- What happens when the model is not confident in an answer? There should be a clear handoff to a human, not a made-up response.
- Where is my data stored, and what gets sent to an external service? Especially if customer personal data or sensitive documents are involved.
- How will I know when the system makes a mistake? You need logs and a dashboard showing requests and outcomes.
- What will a month of operation cost at my volume? The answer should come with specific numbers and stated assumptions.
- What happens if the model provider raises prices or goes offline? A good answer: the architecture allows swapping the model without rewriting the service.
A rollout sequence that reduces risk
Start with one use case and one team. Not AI across the whole company - parsing inbound website requests. Run it in suggestion mode: the system recommends, a person confirms with one click. After two or three weeks you will have real data on how often the suggestion was correct.
Once accuracy on your data satisfies you, some decisions can be handed to the system entirely - but only the ones where a mistake is reversible. A request routed to the wrong team can be fixed. A refund not issued to a customer is harder to fix.
This sequence has another benefit: staff see that the system helps them rather than replacing them from day one. Internal resistance kills rollouts more often than technical problems do.
Start with one repeating task: calculate its monthly cost, ask for a pilot on your real data, and launch in suggestion mode before handing anything to the system fully. If you want to discuss a specific use case and get a quote based on your situation, get in touch with EFIMOV DEV - you will talk directly with the person who will lead the project, and starting-price ranges for each service area are on our website.
Frequently asked
Can an off-the-shelf solution work instead of custom development?
Often yes, and it is worth checking first. If the goal is answering standard questions in chat or transcribing calls, subscription services exist for that. Custom development makes sense when the AI needs to work inside your processes: read your database, write to your CRM, follow your logic. In that case, an off-the-shelf solution either cannot connect or ends up costing more over a year than something built for you.
How accurate will the system be?
There is no universal figure - accuracy depends on the task and the quality of your data. That is exactly what a pilot is for: you run thirty to a hundred of your real examples and get a concrete number for your specific case. Any vendor who quotes an accuracy figure before work begins is guessing.
Will my data end up in someone else's model?
This is an architecture question, and it needs to be settled before the project starts. Some data can be anonymized before it leaves your environment, some can be processed locally. For sensitive scenarios, there are models that can be deployed on your own server - more expensive, but the data never leaves your infrastructure. Ask the vendor to describe the data flow in plain language.
What does post-launch support cost?
Beyond the cost of model API calls, there is ongoing support: updates to the knowledge base, adjustments to scenarios, and responding to changes at the model provider's end. We publish starting-price ranges for support on our website; the exact figure depends on the scale of the service and is agreed after a briefing.