Intelligent Copilots
In-product assistants that draft, summarise, recommend and complete tasks inside the screen where the work already happens.
AI Native App Development Services
Products designed around intelligence from the first sketch, not retrofitted with a chatbot
What is AI Native App Development?
Most AI features arrive as an afterthought: a chat box added to an existing screen and wired to a model API, with no plan for what happens when the answer is wrong, slow or expensive. An AI-native application starts from the opposite end. The workflow, the interface, the data the model may see, the actions it may take and the way a person stays in control are designed together, before the first line of code is written.
We build AI-native web and mobile products - copilots, knowledge assistants, agentic workflow tools and intelligent SaaS - with model orchestration, grounded retrieval, human approval, evaluation and cost controls built into the architecture. The aim is a product people rely on every day, not a demo that only works on rehearsed inputs.
AI-First Product UX
RAG and Tool Use
Production Monitoring
What We Deliver
AI Product Architecture
Map users, jobs to be done, data sources and model boundaries before build starts. We define what the AI decides, what a person approves, and which fallback runs when a model is unavailable or unsure.
Copilot and Assistant Experiences
Contextual assistants embedded in the workflow: drafting, summarising, recommending and completing tasks in place, with streaming responses, editable output and clear user control.
LLM Orchestration
Prompt management, structured output, tool calling and model routing across OpenAI, Anthropic Claude, Azure OpenAI or self-hosted open models, chosen per task for quality, latency, cost and data privacy.
RAG and Knowledge Apps
Retrieval over your documents, databases and wikis with permission-aware access, source citations and freshness controls, so answers are grounded in your data and traceable to it.
Agentic Workflows and Tool Use
Let the app call CRMs, ERPs, ticketing systems and internal APIs through scoped permissions, approval gates and audit logs, so it completes real work without unlimited autonomy.
Evaluation, Safety and Observability
Evaluation sets, regression tests for prompts and models, guardrails, usage analytics and cost dashboards, so quality is measured before release and watched after it.
AI-Native Apps We Build
One architecture supports very different products. These are the categories we are asked for most often.
Knowledge and Document Assistants
Question answering over policies, contracts, manuals and project history, with citations back to the source passage.
AI-Powered SaaS Products
Multi-tenant products where AI is the core value: per-customer data isolation, usage metering and plan-based limits built in.
Agentic Workflow Apps
Applications that plan and execute multi-step work across your systems under explicit approval rules and a complete audit trail.
Conversational Customer Apps
Support, onboarding and booking experiences that resolve requests end to end and hand over to a person, with full context, when needed.
AI Mobile Apps
iOS and Android apps using voice, camera and on-device or cloud models, designed for unreliable connectivity and real-world input.
AI-Native vs AI-Added
Adding a model to an existing screen and designing a product around one lead to very different results. This is where they diverge.
| Aspect | AI added to an existing app | AI-native app |
|---|---|---|
| Starting point | A feature request on a screen that already exists | The user job to be done, redesigned around what AI can take over |
| Role of the model | One API call behind a button | Orchestrated across models, retrieval and tools |
| Data access | Whatever is convenient to paste into the prompt | Permission-aware retrieval scoped to the user and the task |
| Human control | Accept or discard the answer | Review, edit, approve and undo built into the flow |
| Quality | Judged by eye during demos | Evaluation sets and regression tests before every release |
| Cost | Discovered on the invoice | Budgets, caching and model routing designed in from day one |
| After launch | Prompts drift unnoticed | Monitored, versioned and improved from real usage |
How an AI-Native App Is Built
Every product we deliver is organised into five layers. Each can be tested, replaced and scaled on its own, which keeps the system maintainable as models change.
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Experience
Web and mobile interface, copilot panels, streaming output, editable results, and voice or file input.
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Orchestration
Prompt and workflow management, model routing, tool calling, memory and approval gates.
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Models
Hosted, private-cloud or self-hosted language models and specialised models, selected per task for quality, latency and cost.
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Knowledge and Data
Vector and relational stores with connectors to documents, databases and business systems, plus permission-aware retrieval.
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Governance and Observability
Evaluation, guardrails, audit logs, usage and cost analytics, and prompt and model versioning.
Problems This Solves
“The AI feels bolted on”
Workflow, interface and model behaviour are designed together, so intelligence is part of how the product works rather than a panel beside it.
“Responses are hard to trust”
Grounding in your data, citations, output validation, confidence handling and human review make every answer easier to verify.
“Costs rise as usage grows”
Model routing, caching, token budgets and per-feature cost tracking keep production economics visible and controllable.
“The app cannot take action”
Scoped tool access and approval boundaries let the app complete real work without handing a model unlimited control.
How We Work
01
Product Discovery
Define users, jobs to be done, data sources, AI boundaries and the measures that will show the product is working.
02
Prototype and Evaluate
Build a realistic AI workflow and test it against real tasks, edge cases and cost targets before committing to a full build.
03
Engineer the Product
Develop the interface, model layer, integrations, permissions and monitoring as one system, with evaluation running on every change.
04
Launch and Improve
Release with analytics, feedback capture and prompt and model iteration, so reliability improves from real usage.
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