Service
AI Development
We build AI features, agents and automations for teams with repetitive, document-heavy work and for founders whose product has AI at its core. Every project starts with a clear problem and a small prototype on real data, then moves into production with monitoring and cost tracking.
What is included
- 01 AI agents and LLM features in your product or internal tools
- 02 Workflow automation across email, CRM, documents and spreadsheets
- 03 Prototypes that test the idea on real inputs before a full build
- 04 SaaS products with AI, multi-tenant logic and Stripe billing
Who it is for
We work with three kinds of clients: teams whose repetitive, document-heavy work slows them down, founders building a product where AI is part of the core experience, and operators who want to test an AI idea before paying for a full build. We find the problem worth solving, check it against the data you have and build the smallest version that proves it. Prompts, retrieval and evaluations are set up so the AI behaves predictably in production, with a person in the loop where it matters.
What we build
From the first use case to a system that runs, is monitored and stays affordable.
Strategy and prototypes
AI features
Automation
Run and host
Workflow automation
We connect the repetitive steps between email, CRM, documents and spreadsheets and let them run on their own. Incoming requests get sorted, data moves between systems without copy and paste, and reports write themselves. Your team spends its time on judgement instead of admin. Automation projects are scoped individually, based on the steps and systems involved.
- Request sorting
- Data sync between systems
- Automated reports
- Document handling
SaaS products with AI built in
We still build complete SaaS products on Laravel: architecture, multi-tenant logic, dashboards, subscriptions and Stripe billing. AI features sit where they shorten work for users or operators, not as decoration. If the product also needs a brand, a marketing site or a mobile app, branding, web design and app development come from the same studio.
- Multi-tenant architecture
- Dashboards
- Subscriptions
- Stripe billing
- AI features
From idea to production
-
1–2 weeks
Scope
We define the problem, look at the data you have and agree on how success is measured, for example hours saved or requests handled. If the idea does not hold up, you hear it here, before anything is built.
Problem definition Data review Feasibility check Success metrics -
2–4 weeks
Prototype
We build the smallest version that proves the idea with real inputs, not demo data. You see how it handles your own documents, requests or records, and then decide whether to take it further.
Working prototype Test on real data Prompt and retrieval setup Go or no-go decision -
4–8 weeks
Build
We build the production version in two-week cycles, with demos, evaluations and a staging environment. Prompts, retrieval and the choice of model are tuned per task, so quality and cost stay in balance.
Two-week cycles Evaluations Staging environment Model choice per task -
1–2 weeks
Launch
We go live with monitoring and cost tracking in place, so you can see what the AI does and what it costs. You also get a plan for the next improvements, based on what real use shows.
Monitoring Cost tracking Improvement plan
Timeline and pricing
A focused MVP takes 8 to 16 weeks, and a full product build takes 4 to 9 months. Automation and proof-of-concept projects are scoped individually. The price depends on scope, integrations and usage costs, which are the fees the AI models charge per request. Whether usage costs are included or billed separately depends on the package.
After launch
AI systems need care after go-live: models change, data shifts and new use cases appear. Partner retainers cover monitoring, improvements, new features and model updates, so the system keeps doing the job it was built for.
Related projects
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A proof of concept or a single automation is scoped individually, based on the steps and systems involved. A focused MVP takes 8 to 16 weeks, and a full product build takes 4 to 9 months. We confirm the timeline at the end of the scoping step.
We mostly build on OpenAI models and supporting tooling. We choose the model per task, so a simple sorting step does not run on the most expensive model, and quality and cost stay in balance as usage grows.
We sign data processing agreements and keep personal data out of prompts unless it is needed and agreed with you. Which data the system may use is part of the scope, so it is settled before anything is built.
Scoping and proof-of-concept work are quoted as smaller, separate pieces, so you can test an idea without committing to a full build. MVPs and products are priced on scope and integrations. Whether usage costs are included or billed separately depends on the package, and we track them from launch, so you see what each feature costs to run.
You do. The code, prompts, evaluations and data pipelines we build are handed over with the project. Your own team can run or extend the system later without starting from zero, or we can stay on through a partner retainer.
Yes. Partner retainers cover monitoring, improvements, new features and model updates. When models change, we check the system against its evaluations before switching, so quality stays where it was at launch.