How much does AI implementation cost in a company?

In our offer, AI implementation costs from 2,000 PLN net for a single automation to more than 100,000 PLN net for a production agent system or RAG with integrations. The spread comes from the process scope, data, integrations, production requirements, and error risk.

We provide our own indicative prices for August 2026, not market averages. A final estimate is prepared after the scope is agreed.

Ranges by implementation type

What we build	Net range	Indicative time
Single process automation	2,000–8,000 PLN	1–3 weeks
Automation package / document workflow	8,000–25,000 PLN	3–8 weeks
Simple AI assistant on a website	3,000–8,000 PLN	1–3 weeks
Agent serving a real process	8,000–30,000 PLN	3–8 weeks
Advanced single agent	30,000–120,000 PLN	from 2 months
Multi-agent system pilot on one process	15,000–40,000 PLN	timeframe after process discovery
Production multi-agent system with integrations	40,000–150,000 PLN	timeframe after pilot
RAG pilot on a knowledge subset	12,000–30,000 PLN	3–5 weeks
Production RAG with integrations	30,000–120,000 PLN	from 2 months
Single LLM integration in one process	5,000–20,000 PLN	2–6 weeks
AI audit with opportunity map	2,000–6,000 PLN	1–2 weeks

Maintenance may be added: 1,500–8,000 PLN net/month, depending on scope, volume, responsibility, and agreed response time.

Eight things that move the estimate

Each factor’s impact depends on the project, so treat this list as a checklist rather than a universal ranking:

1. The state of your data. Ordered, accessible data shortens analysis and tests. Contradictory versions, no data owner, or unclear permissions add work before model construction.
2. The number and quality of integrations. Every system to connect means authentication, API limits, data mapping, and error handling. Cost depends more on interface quality and edge cases than the count of integrations.
3. Security and compliance requirements. Personal data, processing in a given region, and auditability add architecture work, tests, and documentation. The scope must be agreed before production data is allowed in.
4. Required quality and the cost of error. The higher the acceptance threshold and the consequence of a mistake, the more testing, safeguards, and human control are needed. We first define a measurable level sufficient for the process.
5. The number of exceptions in the process. Every important “it depends” variant needs a rule, test data, and a decision about what happens when the system is uncertain.
6. Whether this is a pilot or production. A demonstration can omit observability, backups, cost limits, access management, and failure handling. A production system must include them, so compared offers need the same scope.
7. Who is available on your side. A project where decisions wait two weeks takes longer and costs more.
8. The need to migrate from a legacy solution. Work nobody remembers at the idea stage.

Ongoing costs often omitted from an estimate

This is easy to miss when comparing offers, so we set it out directly:

• Model cost. Providers bill use according to their own pricing, usually including tokens or completed operations. The invoice depends on volume, input and output length, the chosen model, caching, and retries. Good retrieval in RAG can limit unnecessary context.
• Server and infrastructure. Database, queues, backups, monitoring, and environments. Cost depends on availability, volume, and security requirements.
• Maintenance and tuning. Models, data, integrations, and the process itself change. Without observability and periodic evaluation, declining quality can go unnoticed.
• Quality evaluation. A set of examples run after changes. Its cost must be included because safe system updates are difficult without a comparable test.

How to buy without burning the budget

Use gates. Rather than decide on 150,000 PLN at the start, divide the path into stages, each with its own value and “continue or stop” decision:

Audit (2–6 thousand PLN net) → pilot or MVP (12–35 thousand PLN net) → full implementation (by scope) → maintenance.

After an audit, you can implement the resulting document with any provider. A well-designed pilot lets you measure answer quality on your own data — a proposal alone cannot settle this. We deduct the audit amount from the project estimate if we continue.

When it is not worth spending on AI

• When the process is performed rarely. Calculate annual saving before calculating cost.
• When ordinary automation is sufficient. A model layer adds cost, output variability, and the need for evaluation. If a rule achieves the required result, it will usually be simpler to control — see where to start with process automation.
• When the process itself is the problem. Adding AI to a disorganised process can preserve mistakes rather than remove their cause.
• When there is nobody to supervise it. A production system needs an owner.

The recommendation “no model for now — improve the process first” is a valid result of an AI audit when the value and risk calculation does not justify implementation.

Two things worth checking before choosing a provider

• Does the offer include production? Ask directly about monitoring, backups, cost limits, and failure handling. If they are absent from the estimate, establish who provides them and whether the offer covers only a pilot.
• Does the provider maintain anything? Not “have they built something?”, but “are they responsible today for a monitored production service, including interruptions and recovery?”. Our own system with continuous collectors is the TimescaleDB-based analytics platform.

Frequently asked questions

Can it be financed with a grant?

Programme availability, criteria, and application deadlines change over time. Check current information with the institution operating a particular programme or a grant adviser. We do not acquire grants; we can prepare a technical description and estimate, but their compliance with a specific call’s requirements must be verified before applying.

Why do market offers differ tenfold?

First check whether they cover the same outcome: pilot or production, data preparation, integrations, tests, monitoring, transfer of rights, and maintenance. The “AI agent” label alone does not describe scope, so a price-only comparison can mislead.

How much do the conversation and estimate themselves cost?

Nothing. The first conversation and estimate are free. Only the audit, which is analytical work with a concrete document as its outcome, is paid.

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If you want to turn these ranges into a specific number for your case, contact us or see the products with the lowest entry threshold: AI audit and first automated process.