Tasks finished
A chatbot replies. An agent looks up the record, checks it against policy, prepares the update and hands it over for approval. We measure it on tasks completed correctly.
AI agents · Custom development
AI Agent Development Services for work that has to be done correctly
We build custom AI agents that read your documents, call your systems and complete multi-step tasks, with a person approving anything that cannot be undone. AI Superior is an AI engineering company founded in 2019 by AI scientists, so the work starts with whether an agent is the right design for your process at all.
- Agent or simpler workflow: decided before any build
- Tools scoped to least privilege, with approval steps
- Evaluated on your own cases before go-live
- 57% of our team hold a PhD
- German company, working under the GDPR
Discuss your project
5.0 18 reviews on Clutch Go Global Awards winner
Trusted by enterprises, scale-ups and non-profits
What you gain
What a production agent needs to deliver
Limits you can explain
Every tool has a defined scope, every run has a step limit, and every action is logged. Your auditor can see what the agent did and why it was allowed to.
Changes you can measure
A test set built from your real cases shows whether a new prompt, model or tool made the agent better or worse, before your users find out.
What we deliver
Our AI agent development services
Any of these can be a project on its own. Use case selection comes first when the process has not been assessed yet.
- 01
Use case selection and feasibility
We look at the process, its data and the cost of a wrong action, then recommend an agent, a fixed workflow or no AI at all. Many business processes have a known shape, so a workflow costs less to run and is easier to test.
- 02
Custom AI agent development
Single agents built around your task: model choice, planning loop, tool design, state handling and stop conditions, written as software your team can read and maintain.
- 03
Tool and system integration
Connections to your CRM, ERP, ticketing, document stores and internal APIs, through direct integrations or MCP servers, each with narrow permissions.
- 04
Retrieval over company knowledge
RAG pipelines that give agents access to internal documents with references to the source, and that respect the access rights your users already have.
- 05
Multi-agent systems
Orchestrator and worker designs for tasks that split into independent parts. We add them when a single agent demonstrably falls short, because every extra agent adds failure modes and cost per run.
- 06
Guardrails and human approval
Prompt injection defences, input and output checks, spending and step limits, and approval steps for irreversible actions such as payments, deletions and outbound messages.
- 07
Evaluation and monitoring
Test sets from your own cases, traces of every run and tracking of quality and cost per task in production, so regressions show up on a dashboard and not in a complaint.
- 08
Private deployment
Agents running on models in your private cloud or on-premises, when your data cannot go to a third-party API.
Not sure which of these you need? Describe the problem and we will recommend the approach.
Discuss your projectCase studies
All case studiesResults we delivered for our clients
Professional Services · Generative AI, NLP
Custom LLM chatbot with document references for a consulting firm
In-house LLMno reliance on third-party LLM services
A private, hosted chatbot built on a custom large language model, giving a consulting firm answers with source references while keeping full control over its data.
Read the case study
NLP chatbot for centralised meeting scheduling
3,000 man-hours saved per month
A natural language processing chatbot for a centralised meeting scheduling system that automated the process and reduced the number of human operators needed.
Automated information extraction from invoices
25x faster invoice processing
A web service that extracts data from different types of invoices, automating data entry for a multinational equipment distributor and saving 850 man-hours per month.
Risk estimation model for a medical insurance company
Outperformed statistical baseline models
A neural network model, trained on five consecutive years of historical medical data, that estimates the risk of economic loss so a niche health insurer can optimise its pricing policies.
Why AI Superior
Research-grade AI, delivered like a product company
Many vendors can build a demo. Making AI work reliably on real data, inside real business processes, is the hard part, and it needs a different kind of team.
01
A research-led team
AI Superior was founded in 2019 by AI researchers with published papers and patents, and the whole team works to that research standard.
- PhD57%
- MSc28%
- BSc15%
02
Built to reach production
Every project is de-risked in stages, with a go/no-go decision at each milestone, so budget only goes into approaches that have worked on your data.
4× our proof-of-concept success rate compared with the market average
03
A complete team from day one
Strategy, models and software are designed by one team, so nothing gets lost in hand-overs between vendors.
- Product AI product owner, business analyst, project manager
- Data Data scientists, ML engineers, BI analyst
- Development Software engineers, QA, DevOps
04
Your data stays under control
When data must not leave your organisation, we build on private, self-hosted models instead of third-party AI services.
- GDPR, German company
- Self-hosted LLMs
- Transparent methods
Our team
The experts behind your project
Your project is led by senior data scientists and engineers with research backgrounds, supported by our product and development teams.
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Dr. Ivan Tankoyeu CEO PhD in Computer Science. Recognised by the IEEE GRSS Society, winner of the Global AI Hackathon 2017, Kaggle TOP500 data scientist (2014).
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Dr. Sergey Sukhanov Advisor PhD in Machine Learning, data scientist since 2013. Publications, patents and 30+ implemented AI use cases; several IEEE awards.
Client reviews
5.0 Rated 5.0 by the people we build for
18 verified reviews on Clutch
“Overall, our experience with AI Superior has been extremely positive, and we have been highly satisfied.”
Delivered a highly specialized AI chatbot with custom AI logic for a foundation supporting children and young adults.
“I appreciate their approach, expertise, and the quality of information they provide.”
AI and machine learning consulting to identify the right use cases for a web design and graphics company.
“AI Superior responded to all of our needs very carefully and considered them in the project.”
Built AI models and a dashboard for a pilot program that detects waste in drone imagery.
How we work
A staged AI project life cycle
Every stage ends with a result you can check, and you decide on the next one only after seeing the previous one work. You keep control of scope and budget.
- Estimate before you commitYou see scope and expected results before the build begins.
- Go/no-go after every stageEach stage ends with a result you can check and a decision on the next step.
- Risks reported openlyWe share risks and opportunities as soon as the analysis shows them.
- Go / no-go decision
Discovery
We work through the business problem with your team and define the direction of the solution.
You get: Scope, approach and a high-level estimate of effort and expected results
- Go / no-go decision
Data and feasibility
We get to know your team and data and check whether AI is the right tool for this problem.
You get: A data assessment and a clear feasibility verdict before any build starts
- Go / no-go decision
Proof of concept / MVP
We start small, using the data already available, to test the solution in practice.
You get: Measured results on your own data and a basis for the investment decision
- Go / no-go decision
Integration and scaling
We integrate the solution into your existing systems, fine-tune the models and adjust them where needed.
You get: A solution running inside your processes, compatible with your data and systems
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Evaluation
We review the results with you and make sure they are read correctly.
You get: What the solution delivered and where to improve next
Awards and recognition
Ranked among the top AI companies
Recognised by international business awards and by independent B2B platforms that rank companies on verified client reviews.
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Top Artificial Intelligence Company Germany 2026 · Clutch -
Top Machine Learning Company Germany 2026 · Clutch -
Top Computer Vision Company Germany 2026 · Clutch -
Top Generative AI Company Germany 2026 · Clutch -
Go Global Awards Winner 2021 · International Trade Council -
Best Data Science & AI Service Provider, Europe 2021 · German Business Awards -
Top Artificial Intelligence Company 2023 · Clutch -
Top Machine Learning Company 2023 · Clutch -
Clutch Champion Fall 2023 · Clutch -
Clutch Global Fall 2023 · Clutch -
Top BI & Big Data Company Germany 2023 · Clutch -
Top IT Services Company Germany 2023 · Clutch -
Top Artificial Intelligence Companies 2023 · TrueFirms -
Top Machine Learning Companies 2021 · Techreviewer -
Most Reviewed IT Services Companies Germany · The Manifest
What does an AI agent development company actually do?
It designs and builds software in which a language model chooses the next step, calls tools and works towards a goal within set limits. Calling the model is the small part. The work is in tool design, state handling, permissions, evaluation, monitoring and integration with your systems.
Do we really need an agent, or would a workflow do?
Often a workflow is enough. If the steps are known in advance, as in invoice extraction or ticket triage, a fixed sequence is easier to test, costs less per run and is easier to explain to an auditor. An agent earns its place when the path depends on what the system finds along the way. We tell you which applies before anything is built.
How is this different from your chatbot development?
A chatbot mainly answers questions in a conversation. An agent acts: it calls systems, changes records and completes tasks over several steps. That makes permissions, approvals and testing far more demanding. Many projects combine both. See our AI chatbot development services if answering questions is the main need.
Which models and frameworks do you use?
We choose per project, based on the task, your data constraints and what your team can maintain. That can mean a commercial model through an API or an open model in your private environment, with an agent framework or plain code. We avoid tying the design to one vendor where a thin abstraction will do.
How do you keep an agent from doing something wrong?
With several independent limits, because no single one is reliable: narrowly scoped tools, step and spending limits, separate credentials, checks on inputs and outputs, and a person approving irreversible actions. Prompt injection has no complete fix today, so we design on the assumption that the model can be misled and limit what a misled model is able to do.
How long does a project take, and what does it cost?
Both depend on the process, the number of systems involved and how much risk an error carries. We work in stages: scoping, a pilot on real cases, then production rollout, and you get an estimate after scoping. Our article on AI agent development cost explains what drives the effort.
What do you need from us to start?
A process owner, a description of the task as it runs today, access to examples of real cases including the difficult ones, and someone who can decide what the agent may do alone. Clean data helps, but knowing what a correct result looks like matters more.
Start your project
Tell us what you want AI to do for your business
Share a few details and our AI team will take it from there. Here is what happens next:
- We review your request and reply by email.
- A call with an AI expert to understand your problem, data and goals.
- A clear recommendation: the approach we suggest and a high-level estimate.