AI Integration Services for Business

We find the tasks in your company where AI actually pays off, build a pilot on your real data, measure how well it works, and then put it into daily use with the controls your team needs.

When it makes sense to talk to us

AI integration is worth it when a lot of people spend a lot of time reading, sorting, answering or retyping text. It is not worth it just because competitors mention AI in their press releases.

Your team answers the same customer or staff questions every day, and the answers are already written down somewhere in documents, a wiki or old tickets
Invoices, contracts, applications or forms arrive as PDFs and emails, and someone copies the data into a CRM or accounting system by hand
Incoming requests pile up in a shared inbox and have to be read, classified and routed before anyone can work on them
You have a mobile or web product and want to add search, summaries, recommendations or a chat assistant that works on your own content
You already tried ChatGPT or Claude inside the company and want the same thing connected to your systems, with access rules and without staff pasting data into public tools
Management asked for an AI strategy and you need an honest list of what to build first, what to postpone and what to skip

What we build

AI opportunity assessment

We go through your processes with the people who run them and list the tasks where AI can save time or reduce errors. Each candidate gets a note on data availability, risk and effort, so you can choose the first project on facts rather than on enthusiasm.

Assistants on company data

Chat assistants and internal search built on GPT or Claude that answer from your documents, knowledge base and ticket history (retrieval-augmented generation, RAG). Answers cite the source, and each user only sees what their access rights allow.

Document and email processing

Extraction of fields from invoices, contracts, applications and scanned forms, classification of incoming emails, and drafts of replies. The result lands in your CRM, ERP or accounting system instead of a spreadsheet someone has to retype.

AI agents for processes

Agents that do a sequence of steps on their own: read a request, look up the customer in the CRM, create a task, send a reply or escalate to a person. We define exactly which actions the agent may take and where a human has to confirm.

AI features in apps

Smart search, summaries, content generation, chat support and personalisation inside your existing iOS, Android or web product. We handle the backend part too: prompts, model calls, cost limits and fallbacks when the model provider is slow or unavailable.

Support and improvement

Models change, prices change, and your documents change every week. We keep the solution working after launch: update the knowledge base, re-run quality checks, move to newer models when it makes sense and fix what users report.

Start with the task, not the model

Most AI projects that disappoint started with a technology and looked for a problem later. We do it the other way round. First we look at where time is actually spent: how many requests come in, how long each one takes, how often mistakes happen and what they cost. Then we check whether the data needed for AI exists and is usable. Some tasks turn out to be a better fit for plain automation without any model at all, and we say so.

A short list of use cases ranked by expected benefit, risk and effort
For each one: which data it needs, what can go wrong, how we will measure success
A clear recommendation on what to build first and what to leave for later
An honest note where a simple rule, form or integration does the job better than AI

Your data, your rules

Before any code, we agree where the data will live and which models are allowed to see it. For many companies a commercial API such as OpenAI or Anthropic with business terms and no training on your data is enough. Where it is not, the options include EU-hosted cloud deployments of these models or open-weight models running on your own servers, so the data never leaves your infrastructure. We design access so the assistant cannot show an employee a document they are not allowed to open. Our legal entity is in Estonia, so we work under EU rules on personal data by default.

A written data map: what goes to the model, what is stored, where and for how long
A model choice that matches your confidentiality requirements, including self-hosted options
Role-based access to documents carried through to the AI answers
Removal or masking of personal data where the task does not need it
Logs of prompts and actions, so you can review what the system did and why

Quality you can measure, errors you can catch

Language models sound confident even when they are wrong, so we do not judge a pilot by a few impressive demos. Together with your team we build a test set of real questions and documents with correct answers, and run every version of the system against it. In production we add the checks that fit the risk: confidence thresholds, source citations, validation of extracted fields against your records, and human review for decisions that matter. On the regulatory side, the EU AI Act sets different obligations depending on how AI is used. We help you identify which of them are likely to apply to your use case and build in things like clear disclosure to users that they are talking to AI, while the legal assessment itself stays with your lawyers.

A test set based on your real cases and a repeatable quality score for each release
Guardrails: citations, refusals when the answer is not in the sources, field validation
Human-in-the-loop steps where a wrong answer would be expensive
Monitoring of answer quality, cost per request and user feedback after launch

How an AI project runs

Discovery

Interviews with the people who do the work, a look at the data and systems involved. You get a ranked list of use cases and a recommendation for the pilot.

Pilot on real data

A working prototype for one use case, connected to a sample of your real documents or requests. You see what it does with your material, not with a vendor demo.

Quality evaluation

We measure the pilot against an agreed test set and review the errors with your team. You get a clear go, adjust or stop decision based on results.

Production rollout

Integration with your CRM, ERP, email, app or intranet, access control, logging and monitoring. Your team gets a tool it can use daily and instructions for it.

Support and iteration

We track quality and cost, update the knowledge base and prompts, and move to better models when they appear. You get regular reports and a list of next improvements.

Models and tools

Language models

Retrieval and data

Backend and integration

Where AI shows up

How we can work together

Fixed-scope pilot

A defined use case, an agreed test set and a working pilot at the end. A good way to find out whether AI is worth it for you before a larger commitment.

Dedicated team

A team that rolls out several use cases in sequence and keeps them running. Suits companies that treat AI as an ongoing programme rather than a single project.

Hourly

Consulting, audits of an existing AI solution, prompt and quality improvements or support. You pay for the time actually spent.

Reviews

We treat each client and his project with love.

Revol team continues to streamline the client’s development capabilities through their high-quality work and reliable support. They communicate effectively and exhibit a strong understanding of the client’s needs and business.

Tomas

CEO (Yukon, Canada)

The work from Revol, have fully met expectations and satisfied the client. Their fresh approach and ability to remain available for support have been valuable assets. Customers can bring them on for a communicative, client-oriented team to achieve their goals with.

Andrey

CEO, Stellium (USA)

Frequently asked questions

Where do we start if we have no AI experience at all?

With a short discovery: we talk to the teams that handle requests, documents and customer questions and look at the data they work with. The outcome is a ranked list of use cases and one pilot to test first. You do not need to prepare anything special beyond access to people and sample data.

Will our data be used to train someone else's model?

Not if we set it up correctly. Business API terms of the major providers exclude training on your data, and where that is not enough we use models hosted in the EU or open-weight models on your own servers. Which option fits is decided at the start and written down, together with what is stored and for how long.

How do you deal with wrong or made-up answers?

We cannot make a language model infallible, so we design around its errors. Assistants answer only from your sources and cite them, refuse when the answer is not there, and are tested against a set of real questions before each release. Where a mistake is costly, a person confirms the result before anything happens.

What about GDPR and the EU AI Act?

We build with GDPR in mind from the first day: data minimisation, access control, EU hosting where needed and clear records of what is processed. For the AI Act we help you understand which obligations are likely relevant to your use case and implement the technical side, such as telling users they are talking to AI and keeping logs. The legal conclusions should come from your own counsel.

Can you add AI features to our existing app or website?

Yes. We add the backend part that talks to the model, connect it to your content and user data, and build the interface in your iOS, Android or web product. This includes limits on cost per user and a fallback for when the model provider is unavailable.

When would you advise against using AI?

When the rules are clear and stable, a normal integration or form is cheaper, faster and more predictable. When there is almost no data to learn from, or when every answer must be exactly right and cannot be checked, AI adds risk instead of removing work. In those cases we say so and usually suggest plain process automation instead.

How do we know the project paid off?

We agree on the measure before the pilot: time per request, share of requests handled without a person, number of corrections, response time to customers. We record the baseline, then compare the same numbers after launch. If the numbers do not move, you find out in the pilot, not after a full rollout.