AI Integration Services for Business

Your AI pilot impressed everyone in the demo and falls apart on the tenth real run. We build and repair AI in production: LLM features inside live products, assistants on your own data, and agent pipelines that resume after a failure, show what they actually checked and cost what you expected.

When it makes sense to talk to us

We come in when AI has to do real work every day, inside a product or a process, and someone has to answer for the result.

An agent pipeline runs for hours, fails halfway and starts again from zero
Checks pass green on results nobody actually inspected, and the errors surface at the customer
Nobody can say what one run costs or where the time goes
You have a live product and want AI features in it, plus an engineer who owns them after launch
Your team answers the same questions every day from documents, a wiki or old tickets, and wants an assistant that cites its sources
Your developers use AI coding tools and you want rules, checks and review that make the team faster without lowering quality

What we build and fix

Agent pipelines in production

Multi-step pipelines on Claude, OpenAI models and similar tools: isolated steps, checkpoints and resumable runs, queues and workers instead of one long run, and a fixed set of tools for each step.

Checks you can trust

Deterministic checks and coverage tracking instead of trusting a script’s green light. Every run ends with a report of what was checked and what was not, plus the time and cost of each step.

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.

AI features in live products

Smart search, summaries, content generation, chat support and personalisation inside your iOS, Android or web product. We handle the backend part too: prompts, model calls, cost limits and fallbacks, and we stay with the feature after launch.

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-assisted development for your team

We set up AI coding tools such as Claude Code in your repository: rules for your codebase, sub-agents with defined roles, checks before merge and a model choice per task by the cost of an error. Measured on real tasks before and after.

Pipelines that survive the tenth run

Agent pipelines fail in ways ordinary software does not. A scripted check reports success on a page nobody compared. The agent leaves the process and starts building itself a new tool. One headless browser crash kills a run that has been going all night. We start by running your pipeline on a real task and measuring coverage, time and token cost per step. Then we fix what the numbers show: a process the agent cannot leave, tracked coverage instead of trust, and runs that resume from the last checkpoint. For a new use case the order is the same: first the task and the data, then the model.

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

Audit or discovery

For an existing pipeline or feature: a run on a real task with measured coverage, time and cost. For a new one: interviews and a look at the data. You get a written report and a ranked plan.

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

Pipelines and tooling

Backend and integration

How we can work together

Audit or pilot

A measured run of your existing pipeline, or a pilot for one new use case on your real data. A written report at the end either way.

Repair and build

The top fixes from the audit, or a new pipeline or feature, with before and after numbers. Fixed scope or hourly.

Monthly care

Failed runs handled within an agreed response time, updates for new model and tool versions, and new steps as your work grows, within a monthly hour cap.

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.

Our agent pipeline keeps failing. Will you rebuild it from scratch?

Usually not. We work inside what you already have, measure a real run first and fix the points that cost the most. A rewrite is the easy recommendation and rarely the right one.

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.