S/05 · AI & automation

AI integration and workflow automation

AI where it earns its place: assistants, document handling and workflow automation inside the tools you already use. We test it on your real cases before building it in.

Get a project estimate How it works

What's included

  • AI features inside your existing product
  • LLM integrations and assistants
  • Smart routing of requests and tickets
  • Automation of repetitive workflows
  • A practical view of where AI helps and where it does not

01What we build

Typical projects

Document intake

Read invoices, forms or emails, pull out the fields you need, and send uncertain results to a person to check before they are saved.

Assistants

Assistants that answer questions from your own documents and data and show where each answer came from.

Smart routing

Each incoming request, ticket or message classified and sent to the right person or process, with the reason recorded.

Summaries and automation

Summaries of long threads, calls or reports, and repetitive manual steps automated so people spend time on work that needs judgment.

02Proof

URLNOW.ME

We do not have an AI product of our own to point to. The relevant proof is the plumbing: URLNOW.ME runs its background work through queues, using Laravel Horizon on Redis, with errors reported to Sentry. An AI step needs the same kind of job pipeline to run reliably in production.

Read the case study

Tools we use for this

  • LLM APIs
  • Python
  • Laravel
  • Laravel Horizon
  • Redis
  • MariaDB
  • Sentry

03How it works

The process, step by step

Each step ends with something you can read, click or ship.

Start with discovery

  1. Pick one workflow

    We look at one repetitive workflow end to end and collect real examples of what it handles today: the documents, messages or tickets, and what a good result looks like.

    You get A workflow map and a set of real examples to test against

  2. Test before building

    We run the AI step on those examples and check how often it gets them right. Then we decide together whether AI is worth it, and where a person has to review.

    You get A written test result with a recommendation and a written estimate

  3. Build it into your tools

    The AI step runs as a background job with retries and logging, inside your product or connected to the tools your team uses, with a review screen for uncertain cases.

    You get A working pipeline on staging, with a review screen for flagged cases

  4. Roll out and monitor

    We start with part of the volume, watch errors, accuracy and usage costs, and widen the rollout once the results hold up.

    You get A production rollout with monitoring of errors, usage and API costs

04Good fit

A good fit when…

  • Your team repeats the same manual steps every day.
  • You want AI built into a product you already run.
  • You want a practical answer on where AI would pay off, and where it would not.
  • You need the AI feature and the software around it built by one team.

Candidly

Probably not a fit when…

  • Every result must be right with no human check, and a mistake would be costly. AI should assist a person there, not replace them.
  • The task follows fixed rules. Plain automation will be cheaper and more predictable than AI.
  • There is no specific workflow yet, only a wish to add AI.

05FAQ

Common questions

Ask us directly
Do we need to rebuild our product to add AI?

Usually not. Most of this work adds AI features and automation to products and workflows that already exist, through their APIs, databases or the tools your team already uses.

What is an LLM workflow?

A process where a large language model handles one or more steps, such as reading, classifying, drafting or summarizing text, inside a larger automated flow. The steps around it, such as fetching the input, validating the output and saving the result, are ordinary software.

Will you recommend AI for everything?

No. If a task follows fixed rules, plain automation is cheaper and more predictable, and we will suggest that instead. We use AI where the input varies too much for rules, such as free-text emails or documents in many layouts.

What happens when the AI gets something wrong?

We design for it. Outputs are checked against rules where possible, uncertain results go to a person to review, and every run is logged so mistakes can be traced and the examples used to improve the setup. For high-stakes steps, a person approves before anything is saved or sent.

What happens to our data?

We send the AI model only the data a step needs, and we check the provider’s data-use terms with you before choosing one. Sensitive fields can be removed or masked before a request is sent, and the results stay in your own systems.

How much does AI integration cost?

Cost depends on the number of workflows, how varied the documents or messages are, the integrations with your systems, the review screens your team needs, and the testing needed before rollout. There is also an ongoing usage cost from the AI provider, which grows with volume. After a short discovery and a test on your own examples, we send a written estimate that includes the expected usage costs.

06Related

Often paired with

S/01

Custom Software & SaaS

Business platforms, portals, CRMs and SaaS products built around how your company actually works.

S/02

Web Applications

Customer-facing web apps, admin panels, APIs and dashboards that work on phone, tablet and desktop.

S/06

Cloud, DevOps & Support

Deployments, hosting, monitoring and maintenance so your software stays fast, patched and online.

Next step

Let's talk about your ai & automation project.

Share the goal, rough budget and timing. We reply with questions, a next step, or an invitation to a short call. NDA on request.

Get a project estimate WhatsApp us
Get an estimate