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AI-powered apps.

Plug AI into the systems you already run.

We build AI directly into your existing products, platforms, and operations. Not as a side feature, not as a chatbot bolted onto the corner of the screen. As part of the product, where it changes how the system actually works.

  • LUMC
  • pvdd
  • ToursTickets
  • Sire
  • Ziz
  • Denso
  • Kinsyn
  • Sci Sure

What AI-powered apps actually do

Most AI today lives in a separate tab. A chatbot here, a summary tool there, a copilot somewhere else. The user has to know it exists, remember to open it, and copy the result back into the system they were already using.

AI-powered apps work differently. The AI sits inside the workflow it's meant to improve. It surfaces when it's useful, runs in the background when it's not, and produces outputs the system can act on directly. The user doesn't think "let me ask the AI." They think "this feature is faster than it used to be."

Done properly, the AI becomes invisible. The product just works better.

Proven across industries

We've built AI-powered features for healthcare platforms, telemedicine portals, telecom operations, and clinical workflows. Different products, different data, same engineering approach: AI inside the product, in production, observable, maintainable.

Different decisions need different integrations.

The way AI plugs into your product depends on where the value is. We recognize four patterns most often, each suited to a different moment in the user's workflow.

Embedded AI features

AI inside the screens your users already use.

Smart search that understands intent. Auto-tagging and classification at the moment of upload. Personalization that adapts to the individual user. Content generation triggered from a button, not from a separate tool. Summaries that appear next to long documents. Recommendations that update as the user's behaviour changes.

We embed AI inside web apps, CMS-driven platforms, and customer portals where the AI feels like a feature, not a chatbot. The user doesn't switch tools. The product just got smarter.

AI-augmented workflows

Predictions and suggestions delivered at the moment of decision.

Underwriting systems that score risk while the application is being filled out. Sales tools that suggest the next best action while the rep is on the call. Clinical platforms that surface relevant history while the provider is reviewing a patient. Triage systems that route incoming requests based on content, urgency, and team capacity.

The AI doesn't take the decision. It puts a calibrated suggestion in front of the person who's already making it. Faster decisions. Fewer mistakes. Consistent quality across the team.

Generative features

Drafting, summarizing, and structuring inside your product.

Long documents condensed into the points that matter. Initial drafts generated from structured inputs. Repetitive communication produced from templates and context. Data points pulled from unstructured text. Customer messages turned into structured records the system can route and report on.

We build generative features on top of the major foundation models — OpenAI, Anthropic, AWS Bedrock — with prompt management, output validation, version control, and the kind of guardrails that keep generative work production-safe.

Agentic features

Multi-step AI that takes actions, not just produces text.

Agents that read a customer message, look up the relevant records, draft a response, and surface it for approval. Agents that monitor incoming data, flag anomalies, and trigger downstream workflows. Agents that orchestrate a sequence of model calls, API requests, and human handoffs to complete a multi-step task.

Agentic work is the newest frontier. We build with caution: clear boundaries on what the agent can do unattended, audit trails on every action, and human-in-the-loop checkpoints where the stakes demand them.

What makes AI-powered apps different from "AI features"

The phrase "AI feature" is everywhere. Most of what gets called an AI feature is a chatbot, a generic search box, or a thin wrapper around someone else's model. We build a different thing.

Built into the product, not bolted on
The AI lives inside the workflow it's meant to improve. No separate tool. No new tab. No "ask the assistant" button. The product just behaves more intelligently than it used to.

Tuned to your data and your users
A foundation model out of the box treats every user the same. We tune the AI to your domain, your terminology, your users' patterns. Retrieval-augmented generation where it makes sense. Fine-tuning where it earns its complexity. Prompt engineering grounded in real production traffic, not demo prompts.

Production-grade reliability
Rate limiting, cost monitoring, failover when the model is slow, fallback responses when it's down, and observability that catches problems before users do. The AI-powered app keeps working even when the underlying model has a bad day.

Designed for the moment of use
The AI surfaces when it's useful and stays out of the way when it's not. Good AI UX is invisible UX: the user doesn't think about the model, they just notice the product is faster, smarter, or more personal than it was.

What we mean by "production-ready"

The most common shape of a website

Same standard as everything else we ship.
  • The AI lives behind versioned, monitored endpoints. No "calls to OpenAI or other model scattered through the codebase." Clear abstraction layers, easy to swap models, observable in production.
  • Outputs are validated, not just trusted. Structured outputs are schema-checked. Generative outputs are tested for relevance, safety, and adherence to your guardrails. We don't ship features that occasionally produce nonsense.
  • Cost and latency are tracked from day one. You know what every AI feature costs, per call and per user, before the bill arrives. Latency budgets are set during design, not discovered in production.
  • Failure modes are designed for. What happens when the model is slow, down, or wrong? The product still works. The user still gets a useful response. The team still gets notified.
  • A maintenance path exists. Foundation models update. Prompts drift. Performance changes. We document what to monitor, what to retrain, and when to revisit decisions.

You probably recognize the symptoms

You probably recognize this

  • You've added a chatbot to the product, and nobody uses it.

  • Your AI features are technically working but users haven't noticed them.

  • You're paying for AI APIs but can't explain where the value shows up in the product.

  • Your team built an AI prototype that everyone loved in the demo and nobody can deploy.

  • Every AI feature in the product calls the same foundation model with the same prompt.

  • You're worried your competitors are building AI faster, and you're not sure where to start.

The questions we get asked.

  • Do we need our own model, or can we use OpenAI / Anthropic / Bedrock?

    Almost always the second. Foundation models are good enough for most use cases, and the cost and complexity of training your own model is rarely justified. Where the work is, is in how you use the foundation model — prompts, retrieval, validation, fine-tuning where it matters. We start from foundation models by default and only build custom when the problem genuinely demands it.

  • How long does an AI feature take to build?

    A focused feature, designed and shipped to production: 6 to 12 weeks. Larger AI-driven products with multiple features and complex integrations: 4 to 6 months. We scope properly before we quote.

  • Will our users actually adopt it?

    Depends on whether the AI is built into the workflow or sits outside it. Features that require users to switch tools have low adoption. Features that make the existing workflow faster have high adoption. We design for the second.

  • What about cost and rate limits at scale?

    We design for them. Cost monitoring, caching, batching, fallback strategies, and prompt optimization are part of the build, not an afterthought. We've shipped AI features that handle thousands of daily users without budget surprises.

  • What about hallucinations and safety?

    We architect for them. Structured outputs with schema validation. Retrieval-augmented generation grounded in your real data. Human-in-the-loop checkpoints where the stakes demand them. Guardrails on what the AI can and can't say. Audit trails on every output. Hallucinations don't disappear — they get managed.

  • Can you integrate AI into our existing platform?

    Yes. AI ships as APIs your platform calls or as features embedded inside the CMS that powers your product. We've integrated AI into Craft CMS sites, custom platforms, healthcare portals, and operational dashboards.

Got an AI feature you want to ship?

Tell us what you want the product to do that it doesn't do today. We'll tell you straight whether AI is the right answer, what it'll take to build, and what it'll cost.

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