Transparent process
- Pain
- Unclear stages and hidden costs.
- Solution
- We break down the tasks in detail and lock in the scope before we start.
- Outcome
- You know exactly what you are paying for.
AI implementation cost
AI implementation cost: what drives the estimate
There are no off-the-shelf plans for AI implementation: the price depends on the use case, your data and the integrations. Here is what makes up the estimate and what gets fixed in the contract.
- A custom estimate for your use case
- The scope of work is fixed before we start
- Support after launch
What makes up the estimate
The cost of an AI implementation is defined by the scope of work, not a price plan. We look at the use case (a chatbot, document processing, analytics), the state of your data, the number of integrations with internal systems and the requirements for answer quality. After the brief we break down the tasks, agree on the scope and fix it in the contract, with no hidden costs.
What affects the cost of an AI implementation
Six factors define the final quote. Every project is estimated individually, with no hidden costs.
01
The use case and its complexity
Answering standard questions, working with documents or multi-step processes: the more complex the logic, the more configuration and validation work is needed.
02
Data volume and quality
Scattered, unstructured or outdated data needs preparation before it can feed an AI system: cleaning, structuring and labelling.
03
Integrations with your systems
Connections to CRM, ERP, knowledge bases and messengers. Closed systems and non-standard APIs increase the effort involved.
04
Answer quality requirements
The acceptable error rate, control over wording and handover of complex cases to an operator are validated on test sets and affect the tuning effort.
05
Security and access
Requirements for data storage, access control and handling confidential information shape the architecture of the solution.
06
Support after launch
Answer quality monitoring, retraining on new data and further improvements are matched to how critical the use case is.
AI implementation stages
Each stage affects the final cost and timeline. We define the tasks and agree on the scope of work in advance.
- 1 Brief and audit
We study the task, your data and systems. We define the use case, integration points and risks.
- 2 Design
We choose the architecture and models, agree on the scenarios, quality metrics and scope of work.
- 3 Development and integration
We configure the AI modules, connect data and internal systems, and set up handover of complex cases to people.
- 4 Testing
We validate answer quality on real examples and refine the scenarios until they meet the agreed criteria.
- 5 Launch and support
We go live, train your team, monitor quality and keep improving the solution.
What gets fixed in the contract
Before work starts you receive a document that locks in the scope, timeline and acceptance criteria.
- The use cases and boundaries: what the solution does and does not do
- The list of integrations and data sources
- Answer quality criteria and the acceptance procedure
- Stages, timelines and the cost of each stage
- How data and access are handled
- Support terms after launch
Why it pays to work with us
A transparent quote, an engineering approach and support after launch.
Engineering approach
- Pain
- AI gets deployed for show, disconnected from real processes.
- Solution
- We start with the use case and the data, not the model. The technology is chosen to fit the task.
- Outcome
- The solution is embedded in real workflows.
Support and growth
- Pain
- Answer quality drops after launch and no one is there to help.
- Solution
- We monitor quality, retrain on new data and refine the scenarios.
- Outcome
- The solution keeps working and improving.
What people who've worked with us are saying
On process, engineering maturity, and timelines - from the people who saw it from the inside.
«A strong level of engineering maturity: not just formal delivery, but a thorough analysis of the brief and decisions optimised for the company's goals. I confidently recommend the team for technology projects.»
- Analytical mindset engages with the business need rather than mechanically executing the spec
- Proactivity goes beyond the minimum scope of the agreement
- Outcome-driven proposes alternative technical paths aligned with business goals
«I haven't seen a system this strong with any other seller.» the company's CFO
«Any change on the partner side is handled quickly - our integration runs without disruption.»
«Clear plan, transparent process, delivered on time. I confidently recommend them as a strong team.»
«Everything now lives in one portal: our whole team and our clients work in a single system, with no third-party tools.» Video testimonial
«There was no ready-made solution on the market: Axium built one for us in about a month, and it has been running reliably ever since.» Video testimonial
«Axium quickly built an app for our community and got it onto the App Store despite every obstacle. Every fix was handled promptly.» Video testimonial
«Everyone who uses it likes it. We ended up with a great product.»
The cost depends on the use case and the data
We give a precise quote after reviewing the task: which use case we are implementing, what state the data is in and which integrations are needed. We provide a preliminary estimate within 1-2 business days of the brief.
Pricing FAQ
Why are there no AI implementation prices on the site? ＋
Because no two projects are alike: a chatbot on a ready knowledge base and document processing integrated with an ERP differ in scope several times over. A one-size-fits-all price would say little about your task. So we review it and estimate the specific scenario: that way the number reflects the actual scope of work.
What is included in the cost estimate? ＋
Analysis of the task and data, solution design, development and integrations, answer quality testing, launch and basic support. The quote is itemized by stage, and additional tasks are discussed separately.
Can we start with a small pilot? ＋
Yes, and that is what we usually recommend: first a pilot on one use case with a limited amount of data, then scaling. A pilot reduces risk and lets you validate answer quality before a larger investment.
Get a consultation and an estimate
Leave a request and an expert will get in touch, clarify the details and prepare a tailored proposal.
Working hours: Mon-Fri 09:00-19:00 · Email: hello@axium.uz
What happens next
- Intro call: We clarify business goals and context. If needed, we sign an NDA immediately.
- Requirements analysis: We review your operational flow, identify technical risks, and map integration points (1C, CRM, ERP).
- Plan and estimate: We prepare a transparent proposal (SOW) with stages, timeline, and a fixed budget framework.
Confidentiality guaranteed • Engineer response, not a bot