AI-Native Custom Software Development
We build products where the model is part of the architecture, not a chat box added at the end, with the data handling, testing and controls that real users need.
What it is
AI as a core part of the product, not a plugin
Adding a chatbot to an existing app is quick. Building software where AI does real work, reliably, is a different job. The model needs the right context, its answers need to be checked against sources, its failures need a fallback, and its costs need to stay predictable as usage grows.
AI-native development means we design for those concerns from day one. Data pipelines, retrieval, prompt and model versioning, evaluation and access control are part of the architecture, alongside the usual web or mobile application work. We have built websites, web apps and custom software since 2015, and as models became production-ready our work shifted toward this kind of product.
Who it's for
Teams with a specific product idea and real data behind it
- Product owners and CTOs who want an AI feature inside an existing platform and need it to meet the same standards as the rest of the product.
- Founders building a new product where the AI capability is the main reason customers will pay.
- Firms with large document collections, such as legal, financial services and healthcare organizations, that want staff or clients to get answers from those documents quickly and verifiably.
- Organizations serving multilingual audiences who need an assistant that works in the languages their users actually speak.
If you want to test an idea quickly before committing to a full build, our Production Grade MVP sprint is often the better first step.
What we build
Examples of AI-native products
Document question answering with citations
Nthlex, an AI legal platform, answers questions about contracts and case files and points to the page each answer came from. This pattern, usually called retrieval-augmented generation (RAG), splits documents into searchable passages, retrieves the most relevant ones for each question, and has the model answer only from those passages. The citation lets a reviewer check the answer in seconds instead of trusting it blindly.
Multilingual assistants
Kromium Health is a digital health platform with an AI assistant, Dr. Lola, that speaks with users in local Nigerian languages, alongside records, doctor bookings and pharmacy orders. Work like this involves testing the model in each language with real phrasing, and designing clear hand-offs to people and to the rest of the platform.
AI tools inside larger platforms
Deal.ai is an AI marketing platform with more than 75 tools. Products with many AI features need shared infrastructure for prompts, usage tracking and model changes, so each new tool does not become its own maintenance problem.
How it works
How a custom AI build runs
- Discover. We look at your users, your data and the decision or task the AI should support. We check early whether the data is good enough, because that usually decides what is possible.
- Design. We define the architecture, the model approach, how answers will be evaluated and what happens when the model is unsure. Scope is fixed in writing before price.
- Build. We ship working software early and often, usually starting with the hardest AI component so risk surfaces first. Each cycle includes test sets of real questions and expected answers.
- Launch. We deploy to your infrastructure with monitoring for quality, latency and cost.
- Grow. We review real usage, improve weak answers and add features based on what users do, not what we guessed.
Deliverables
What you get, and what we need from you
You get: the deployed application, full source code in your repository, model and vendor accounts in your name, an evaluation set you can rerun after any change, and documentation another team could pick up. You own the code, accounts and data.
We need from you: a product owner who can make decisions, representative data (anonymized where needed), access to any systems the product must connect to, and a few people willing to test early versions and tell us where they fall short.
FAQ
Frequently asked questions
How do you price custom AI development?
We scope the work first, then quote a fixed price for that scope. The main cost drivers are the number of features, the state of your data, integration work and how strict accuracy and compliance requirements are. Ongoing model usage is billed by the provider to your own account.
How long does a build take?
A focused first release typically takes four to twelve weeks, depending on scope and data readiness. A narrow proof of value can be much faster. We agree a timeline alongside the scope rather than guessing up front.
How do you stop the AI from making things up?
We ground answers in your data through retrieval, show sources where it matters, instruct the model to say when it does not know, and test against a set of real questions before every release. No method removes errors entirely, so we design review steps for high-stakes outputs.
Is our data used to train public models, and who owns it?
We use provider settings and agreements that keep your data out of model training where the provider offers this. You own the code, accounts and data.
Which systems can the product connect to?
Typical integrations include CRMs such as HubSpot or Salesforce, Google Workspace, Microsoft 365, Slack, accounting tools, document stores, databases and your own internal APIs.
Next step
Talk through your idea with an engineer
Bring the product idea and a sample of the data behind it. We will tell you what is realistic, what is risky and how we would approach the first version. Get in touch.
Have an idea
worth building?
Let’s turn your idea into a digital product that works.