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AI & Automation

AI integration and workflow automation

Automation usually pays off before AI does. We start with the repetitive work: reminders, status checks, reports and data imports that can run on a schedule. Where a language model genuinely helps, we would add it with limits: a clear task, a test set we set up to check its output, a cost cap and a person who approves the result. In our own trading research platform, Cognix, a scheduled Claude agent proposes one research idea a day. Each idea must pass backtests, an 8-stage validation process and paper trading, and a person decides whether it is promoted.

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From $1,000 ยท fixed quote after we agree the scope

Technology we have used

  • Claude (Anthropic)
  • Kotlin
  • Spring Boot schedulers
  • Python
  • PostgreSQL
  • Telegram Bot API
  • GitHub Actions

Is this right for you?

Problems this service solves

AI & Automation usually suits teams in one of these situations.

  • Teams spending hours on manual checks, reminders or report building
  • Products that want to try an LLM feature without risking user trust
  • Businesses with data that needs regular imports, scoring or alerts

When we are not the right fit

We do not sell chatbots for the sake of it. If a plain rule or a scheduled job solves the problem more cheaply and reliably, we will recommend that instead.

How we test it

We design automations to log each run and alert on failure. For LLM features, we set up fixed test cases to check output before a change ships, and we keep a person responsible for the final decision. In Cognix, AI-proposed ideas go through backtests, validation and paper trading before anyone promotes them.

What you get

Deliverables

What a typical engagement includes. We agree the exact scope with you before work starts.

  • Automation audit

    A list of manual tasks ranked by time saved and risk, with what to automate first.

  • Scheduled jobs

    Reminders, status checks, imports, backups and daily reports that run without anyone pressing a button.

  • Rule engines

    Clear, testable business rules for routing, pricing, eligibility or alerts.

  • LLM features

    Prompts, evaluation sets, guardrails, cost limits and a human approval step.

  • Bots and alerts

    Telegram or messaging bots that post updates and follow-ups automatically.

Our approach

How the work runs

The same five steps on every project, with working software to review every week.

Scope your project

  1. Share your idea

    Tell us what you want to build, who it is for and what has to be true for it to succeed.

    You get: A short written summary of the problem and a suggested first release.

  2. Plan and design

    We agree the scope, the screens and the technical approach before writing production code.

    You get: An agreed scope, designs for the key flows and a milestone plan.

  3. Build and demo weekly

    We build in small steps and give you a working version to try every week.

    You get: A tested, working product that grows each week.

  4. Launch

    We prepare the release, from store listings and privacy pages to hosting and monitoring.

    You get: Your product, live for real users.

  5. Grow

    After launch we fix, improve and extend the product based on what users actually do.

    You get: A product that keeps improving, or a smooth handover.

Related work

Case studies

Projects where we have done this kind of work. Client names appear only where the client agreed.

All work

Industries

Where we have applied it

Sectors where we have built this kind of software.

All industries

FAQs

AI & Automation: common questions

Have you built AI features for clients?

Our hands-on LLM work so far is inside our own product, Cognix, where a scheduled agent proposes research ideas that are then tested automatically and approved by a person. For clients, most of our automation work has been scheduled jobs, rule engines and messaging. We are open about this and scope AI work carefully.

Which AI models do you use?

We have used Anthropic's Claude in production automation. The right model depends on your task, budget and data rules, and we keep the integration simple enough to switch providers if needed.

Is our data safe with an AI feature?

We send the model only what the task needs, avoid personal data where possible, and check the provider's data-retention terms with you before anything goes live.

Have a question that is not here? Ask us directly.

Let's build it

Have a project like this in mind?

Tell us what you are trying to achieve. We will reply with honest feedback on scope, approach and next steps.

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