An AI development company that builds real, shipped AI.
We build working AI into real products and workflows — LLM apps, chatbots, RAG search and custom machine learning. Not demos that break in a week. Practical features that ship, help real users, and keep working after launch. We work remotely with startups and established teams around the world.
Real AI, not a science project.
AI is everywhere right now. Every week there is a new demo that looks like magic. But there is a big gap between a clever demo on a stage and a feature your customers can trust every single day. Most AI projects fail not because the model is weak, but because nobody engineered around what the model does well — and around what it still gets wrong.
That is the work. Picking the right model. Feeding it the right data. Adding guardrails so it does not go off the rails. Measuring whether the answers are actually good, and building a safe fallback for the times they are not. This is where a real AI development company earns its keep. The tool is the easy part; the careful engineering around it is what turns a shiny prototype into something people rely on.
At Scorpyns, we build AI the practical way. We are a remote-first product and software studio that builds and runs our own products — including AI ones — so we know what it takes to ship a feature that still works well months after launch, not just on the day of the demo. We work with clients worldwide, and we tell you the truth about what AI can and cannot do for your business.
Custom AI, built to ship.
LLM apps & assistants
Apps powered by large language models — assistants, writing tools, summarisers and copilots that do a real job, with a clean, simple interface your users understand.
RAG systems
Let an AI answer from your own documents, help articles and data — and show its sources. This is how you build a trustworthy assistant that knows your business, not just the internet.
AI integration services
Add OpenAI, ChatGPT or Claude into software you already use. We plug AI into your product, dashboard or workflow so it fits in naturally and saves real time.
Custom machine learning
When a general model is not enough, we build custom machine learning — prediction, scoring, classification and recommendations trained on your own data.
Chatbots & support tools
Helpful chat that answers real questions, handles routine requests, and hands off to a human when it should. See our AI chatbot development.
AI agents & automation
Move beyond chat to AI that takes action — filling forms, moving data and running steps for you. See our AI agents work.
The careful parts are never extra.
Anyone can call an AI model in a weekend. Making it reliable, safe and affordable is the real job. All of this comes with every AI project we build — as standard.
- The right model for the job — we compare options on quality, speed, cost and privacy, not hype.
- Grounding in your data — answers based on your real content, so the AI stops guessing.
- Prompt design & testing — carefully built instructions, checked against real examples.
- Guardrails & safety — limits that keep the AI on-topic and out of trouble.
- Quality checks — we measure how good the answers are before and after launch.
- Sensible fallbacks — when the AI is unsure, it says so or hands off to a person.
- Cost controls — we watch model usage so your monthly bill stays predictable.
- Clean integration — the AI fits into your product or workflow through a tidy, documented API.
- Privacy & data handling — clear rules on what data is used, stored and shared.
- Monitoring & logging — so you can see what the AI is doing and catch problems early.
- Documentation & handover — full notes and a walkthrough, so your team can run it.
- Support after launch — we're still here to tune, fix and improve it.
Engineers who ship, and stay.
Plenty of people can wire up an AI demo. Fewer can turn it into a feature that survives real users and real edge cases. We are a senior engineering studio that builds full products, so AI is one strong tool in a much larger toolbox — and we know how to fit it in properly.
We build our own AI products too. We ship and run our own platforms, so when we recommend an approach it comes from real experience of what breaks and what lasts — not a sales script.
We are honest about AI. We will tell you plainly when AI is the right answer, when a simpler tool would be cheaper and safer, and where the model has real limits. No hype, no jargon, no overpromising.
We are remote-first and worldwide. We work with startups and established companies across time zones, with clear written updates and fixed scope, so you always know where your project stands — wherever you are.
From idea to AI in production.
Discovery call
Tell us the problem you want AI to solve. We give honest advice on whether AI fits — and how — at no charge.
Scope & plan
You get a clear plan, one fixed price and a realistic timeline, all in writing, before any building starts.
Prototype
We build a working proof of concept with your real data, fast — so you can see and judge the AI early.
Build & evaluate
We harden it for production, add guardrails, and measure answer quality against real examples until it's solid.
Launch & support
We ship it, set up monitoring and cost tracking, hand over full docs, and stay on to tune and improve.
Proven AI tech, used well.
We use trusted, well-supported tools — and we stay model-agnostic, so you're never locked to one provider.
What AI can do — and where it needs a hand.
Before you spend money on AI, it helps to know what these tools are genuinely good at, and where they still trip up. We would rather explain this clearly than sell you something that does not fit. Here is the honest picture we share with every client.
What large language models are good at
Modern language models are excellent at working with words. They summarise long documents, draft and rewrite text, answer questions, pull key facts out of messy notes, translate, classify messages, and hold a natural conversation. When the job is understanding or producing language, and roughly the right answer is good enough, they are a huge time-saver. That covers a surprising amount of everyday business work.
What AI still gets wrong
Language models can be confidently wrong. They sometimes make up facts, misread a tricky question, or give a different answer to the same prompt twice. They do not truly "know" things — they predict likely text. So for anything where being exactly right matters — money, legal, medical, safety — you cannot just trust the raw output. That is not a reason to avoid AI; it is a reason to engineer around it, which is exactly what we do.
RAG: giving AI your own knowledge
Out of the box, a model only knows what it learned during training. It has never seen your product manual, your policies or your customer records. A retrieval-augmented generation (RAG) system fixes that. It searches your own documents for the most relevant pieces, then asks the model to answer using only those pieces — and to show where each answer came from. The result is an assistant grounded in your real knowledge, with far fewer made-up answers, and sources you can check.
Keeping AI fast and affordable
Every AI request costs money and takes time. Left unchecked, a popular feature can run up a surprising bill. We design for this from day one — choosing smaller models where they do the job, caching common answers, trimming what we send, and setting sensible limits. You get a feature that feels quick to users and stays predictable on cost.
Your data stays yours
We set AI up in your own accounts wherever possible, and we are clear about what data leaves your systems, what is stored, and for how long. Where privacy is critical, we can use models you host yourself so your data never goes to a third party. You always know where your information is going and why.
We don't just talk — we ship.
We built RankMentor, an AI study planner that helps learners plan their days and stay on track. It's a live product, not a demo — proof that we ship AI people actually use. Take a look at the case study, and the rest of our work.
AI questions, answered honestly.
What does an AI development company actually do?
A good AI development company builds AI into real products and workflows — not slide decks or one-off demos. For us that means LLM apps and assistants, RAG systems that answer from your own documents, ChatGPT and OpenAI integration inside software you already use, and custom machine learning models. We handle the whole job: choosing the right approach, building it, testing it, and keeping it working after launch.
How much does custom AI development cost?
It depends on what you want the AI to do and how deeply it plugs into your systems. A small feature added to an existing product costs far less than a full custom model or a large RAG platform. We give you one fixed price after a discovery call, plus a clear view of the running costs, such as model usage, so there are no surprises. Here's how our pricing works.
How long does it take to build an AI feature or product?
Many AI features can be prototyped in two to four weeks so you can see it working with real data early. A production-ready feature usually takes a few weeks more, and a larger custom platform takes longer. On our first call we give you a realistic timeline, and we tell you early if anything changes it.
Which AI models do you work with — OpenAI, or others?
We're not tied to one provider. We work with OpenAI and ChatGPT models, Anthropic's Claude, open models you can host yourself, and classic machine learning where that fits better. We pick the model that gives you the best mix of quality, speed, cost and privacy for your job, and we design things so you can switch later if a better option appears.
What is a RAG system, and do I need one?
RAG stands for retrieval-augmented generation. In plain words, it lets an AI answer using your own documents, help articles or data instead of only what the model already knows. It's how you build an assistant that can answer questions about your product, policies or files — and cite where the answer came from. If you want AI grounded in your own knowledge rather than guessing, a RAG system is usually the right tool.
Can AI make mistakes, and how do you handle that?
Yes. Language models can be confidently wrong, so we're honest about it. We design around it: we ground answers in your real data, add guardrails, show sources, and build fallbacks for when the AI is unsure — such as handing off to a person. We also test quality with real examples before launch and keep measuring it afterwards. AI is powerful, but it's a tool that needs careful engineering, not magic.
Do you work with international and remote clients?
Yes. We're a remote-first studio and we work with startups and established companies worldwide. We collaborate across time zones with clear written updates, scheduled calls and shared access, so distance is never a problem. Most of our projects are delivered fully online, from first call to launch and support.
Do we own the AI system and the code you build?
Yes. You own the code, the accounts, the data and the models we build for you. We set everything up in your own cloud and provider accounts wherever possible, hand over full documentation, and never lock you in. If you want, we stay on afterwards for support, tuning and new features.
Related work & solutions.
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