AI agent development and automation that works in production
From a working prototype to an agent that runs on schedule, with guardrails your team can trust.
- Python
- FastAPI
- AI agents
- REST
- Google Cloud
What we build
AI agents and automation we deliver
Most AI pilots stop at a demo. For Cracken.ai we build autonomous agents that plan, schedule and orchestrate security testing in production, and for Uptim.ai we link engineering desktop applications to their AI solver and automate the task handoff between them. We bring the same discipline to your workflows: tool use, retrieval over your own data, guardrails, evaluation and monitoring, so an agent keeps doing its job long after the demo ends.
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01
Autonomous agents for real workflows
Agents that plan, call tools and take multi-step actions in your systems, not single prompt-and-response scripts.
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Scheduled and event-driven orchestration
Agents triggered on a schedule or by an event and coordinated across steps, the way we run agentic testing for Cracken.ai.
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Retrieval over your own data
Answers and actions grounded in your documents, tickets and databases, not only the model's general knowledge.
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04
Guardrails and human-in-the-loop approval
Clear limits on what an agent may do alone, with an approval step for actions that carry real risk.
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05
Evaluation, monitoring and cost control
Test sets that catch regressions before release, plus logging of latency, failures and token cost once the agent is live.
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Integration with your existing systems
Agents wired into the tools, APIs and desktop applications you already run, as we link Uptim.ai's solver to their engineering apps.
Typical problems we solve
Questions we hear before an agent ships
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A demo worked, but production did not.
Demos skip the hard parts: retries, rate limits, partial failures and monitoring. We design for those from the first version, not bolted on after something breaks.
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Will the agent ever do something it shouldn't?
We scope what each agent can do on its own, require approval for actions that are risky or hard to reverse, and log every step so its behavior stays traceable.
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Where does our data actually go?
We choose the model and hosting to match your privacy needs, keep retrieval and storage inside systems you control, and document what leaves your infrastructure and why.
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Is this even a job for an AI agent?
Not always. Where the rules are fixed and inputs are structured, plain automation is cheaper and easier to test, and we say so instead of reaching for a model by default.
Relevant work
Work we have delivered.
A selection of projects our engineers have built and shipped. Open any of them for the details.
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AI agents that pentest on their own
Agentic scheduling and orchestration for Cracken.ai
For Cracken.ai we build an AI-driven penetration testing platform: autonomous AI agents plan, schedule and orchestrate security testing, with agentic task scheduling that runs reliably and securely in production.
![Preview of the Cracken.ai website]()
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An engineering ecosystem around an AI solver
Website, automation, engineering desktop apps and Škoda Auto integration for Uptim.ai
For Uptim.ai we built the software around their AI solver: the website, automation and task delegation, desktop applications for engineers that cover pre-processing, calculations and post-processing, the link between those applications and the solver itself, and the integration into Škoda Auto's engineering workflow.
![Preview of the Uptim.ai website]()
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Automation behind enterprise backends
Backend and process automation for Oracle
Backend and automation work for Oracle: stable services, data processing and automation tooling that remove repetitive work and connect systems that must cooperate reliably.
How we work
From prototype to production agent
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01
Scope the workflow
We map the task, the systems it touches and where a human should stay in control before any agent code is written.
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02
Prototype against real data
An early agent runs against your actual documents, tools and APIs rather than sample data, so we learn the hard cases fast.
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03
Harden for production
Guardrails, retries, evaluation sets and monitoring turn the prototype into something that runs unattended and on schedule.
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04
Operate and improve
We watch cost, latency and accuracy after launch and adjust prompts, tools and models as your workflows change.
How every engagement runs
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Clear scope and estimate
You know what you get, when, and what it costs before work starts.
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Working software early
Short iterations with results you can see and test.
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Transparent communication
Direct contact with the engineers, regular updates and no surprises.
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Support after release
We stay responsible for what we delivered.
Questions and answers
Frequently asked questions
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How do you keep an agent from doing the wrong thing?
We scope each agent's tools narrowly, require approval for actions that are risky or hard to reverse, and log every step it takes. Evaluation sets built from real cases catch regressions before a change reaches production, the same discipline behind the agent orchestration we run for Cracken.ai.
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Where does our data go when we use an AI agent?
That depends on the model and hosting you choose, and we help you choose it. Retrieval and storage can stay inside infrastructure you control, and we document exactly what data reaches which model provider so you can make an informed call.
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Can we switch models later if a better one comes out?
Yes, if the agent is built with that in mind. Keeping prompts, tools and evaluation sets separate from any one model lets us test a new model against your real cases and switch without rebuilding the agent around it.
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Do we need an AI agent, or would a normal script do the job?
Often a normal script does the job better: cheaper, faster and easier to test. We recommend an agent where the task needs judgment, varies too much for fixed rules, or has to work with language, and plain automation everywhere else.
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What keeps an agent reliable after it goes live?
Monitoring for latency, cost and failure rate, a test set that runs before every prompt or model change, and clear ownership of the automation once it ships. We build this in from the start, based on running agentic task scheduling for Cracken.ai in production.
Contact
Tell us about the workflow to automate
Describe the task, the systems it touches and how much autonomy it should have. We will reply with concrete next steps and the trade-offs that matter for your case.
- We get back to you as soon as possible.
- A short call to understand your goals and scope.
- A written proposal with an estimate. No obligation.
Message sent!
We'll get back to you soon.
Capabilities
Other services
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Websites
Fast, search-friendly websites that explain what you do and turn visitors into enquiries.
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Web applications
Customer portals, internal tools, dashboards and workflow systems on backends built for real load.
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Desktop programs
Cross-platform desktop applications for demanding workflows, specialized hardware and data visualization.
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Mobile applications
Android and iOS apps, from public app-store products to internal tools for teams in the field.
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Embedded software
Firmware and control software for devices and specialized hardware, connected to the apps and systems around them.
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Consulting and architecture
Requirements, technology choices, architecture review and delivery planning, so you invest in the right thing from day one.
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Maintenance and support
Updates, fixes and new features after launch, for software we built and for systems you already run.
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Not sure which service fits?
Describe your project and we will recommend the right approach, even if it is not us.