Not every task needs AI. Exact rules may be more reliable for deterministic decisions. Models are useful with unstructured text when the result can be checked.

What is included

A process map, data requirements and responsible owners.
A pilot using agreed examples and quality criteria.
Integrations, execution records and failure handling.
Access controls, usage budgets and escalation to a person.

How we work

Measure manual work and recurring sources of error.
Compare rules, existing services and AI on representative examples.
Run a bounded pilot and assess quality with the team.

How we estimate cost

Implementation and ongoing API, infrastructure, support and review costs are estimated separately. The decision depends on total cost and output quality, not just generation speed.

What to bring to the conversation

Bring anonymised examples, your current systems and data-handling rules. Identify who can confirm whether the result is correct.

Where could AI help with daily work?

Answer recurring customer questions

An assistant uses approved product and service materials, with clear limits and human handoff when information is missing.

Find answers in company documents

Knowledge search with sources and staff access controls. Evaluate quality using real team questions before launch.

Triage incoming requests

Classify messages, extract agreed fields and draft replies. A responsible employee retains important decisions.

Stop copying data manually

If rules and APIs solve the workflow, AI may be unnecessary. Consider a conventional system integration.

Custom business software ↗

From a pilot to a working AI solution

A pilot for one task

One workflow, agreed examples and a separate evaluation set. Deliver a prototype and comparison with the current process: quality, staff corrections and total cost.

Integration into daily work

If the pilot meets agreed criteria, connect the required channel and system, configure access, human handoff and error monitoring. Additional workflows are estimated separately.

What implementation includes

Data preparation, model connection, integrations, a review interface and monitoring after launch. Define company-provided materials, who approves answers and which actions the system may take. Model usage is priced separately from development.

Checks before expanding the pilot

Evaluate correctness, unsupported statements and manual corrections on a separate set; agree acceptable levels in advance.
Questions outside the sources and model downtime follow an agreed manual fallback.
Document access and action permissions match user roles; important actions require agreed confirmation.
Measure staff review time and processing costs; the responsible person understands usage limits and how to stop the pilot.

Who operates the solution

Deliver agreed code, configuration and instructions for updating sources, reviewing quality and disabling automation. Name the person responsible for materials and errors. Model replacements, new sources and workflow changes require another quality evaluation.

Recurring costs and limits

Model usage, servers and storage, external APIs, knowledge base updates and staff review time. A pilot cannot guarantee error-free answers; measured results determine whether to expand it.

Before we start

Can a process run without human involvement?

That depends on the task and the consequences of mistakes. Important decisions and external actions should include review and explicit approval.

Do we need a custom model?

Not necessarily. We first assess existing tools and ordinary rules against quality, data, latency and cost requirements.

How do we measure value?

Compare handling time, correction rates and total cost before and after the pilot. Agree metrics and measurement methods upfront.

Is an AI assistant the same as a regular Telegram bot?

No. A regular bot follows defined steps. AI handles free text but needs grounded information, answer evaluation and cost controls. They can work together.

Can you connect ChatGPT or another model to our workflow?

We can discuss an available API, data handling, access and usage cost. Start with one bounded workflow and an evaluation set.

How do we evaluate the return on AI?

Record task volume, staff time and acceptable errors before a pilot. Compare quality, model spend and manual corrections afterwards; savings cannot be promised without measurement.