AI Copilots & Assistants
In-app assistants that help your staff or customers find answers, draft content and complete tasks — embedded directly into the software they already use.

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AI-powered web, mobile and SaaS applications built around your data and your users — from internal copilots and knowledge search to customer-facing products with AI at their core.
AI Engineering
Off-the-shelf AI tools can only take you so far. When AI needs to work with your own data, fit your workflows or become part of a product your customers use, it needs to be engineered properly — with the right model, a secure architecture and a user experience people actually enjoy.
ECNet combines more than fifteen years of software development with hands-on AI engineering. We design, build and integrate AI applications across web, mobile and cloud, using leading large language models from providers such as OpenAI, Anthropic and Google, as well as open-source models when privacy or cost calls for it.
What We Build
In-app assistants that help your staff or customers find answers, draft content and complete tasks — embedded directly into the software they already use.
Ask questions in plain language and get accurate answers drawn from your own documents, manuals, policies and databases, with links back to the source.
Applications that read, classify and extract data from contracts, invoices, forms and reports — turning paperwork into structured, searchable data.
From idea to launch, we help you build AI-first products — architecture, model selection, user experience, billing and scale.
Image and video analysis, including the facial recognition and identity verification expertise behind our airport and biometric platforms.
Content generation, summarization, translation and personalization built into your app — tuned to your brand and your users.
Our Stack
OpenAI GPT, Anthropic Claude, Google Gemini and open-source models such as Llama and Mistral.
React, Next.js, Vue and Angular front ends with Node.js, Python, PHP or .NET back ends.
Native iOS and Android or cross-platform apps with React Native and Flutter.
AWS, Azure and Google Cloud, vector databases and secure API integrations.
Process
We clarify the use case, test it with your real data and prove the AI delivers before major investment.
We choose the right models, data pipeline and security approach, and design the user experience.
We develop in stages, integrate with your systems and test for accuracy, speed and safety.
We deploy, monitor quality and costs, and keep improving the application as models and needs evolve.
FAQ
It depends on your needs. We compare leading commercial models and open-source options for accuracy, speed, cost and privacy, then recommend the best fit — and design the app so models can be switched later.
Yes. Using retrieval-augmented generation (RAG), the application searches your approved content and answers from it, with sources — without training a public model on your data.
We ground answers in your data, add citations, set clear boundaries on what the AI should and shouldn't answer, and test thoroughly before launch.
Yes. Many projects add AI features — search, assistants, summarization or automation — to existing web or mobile applications through APIs.
We design for security from the start: access controls, encryption and, where needed, private or self-hosted models so data never leaves your environment.
A focused proof of concept can often be delivered in a few weeks; full applications depend on scope. We agree on milestones and a timeline before development starts.
Related
Purpose-built platforms, APIs and integrations — the foundation for AI-powered systems.
Learn more →Generative AI for marketing copy, product descriptions and brand-aligned content at scale.
Learn more →Run your AI application on private, self-hosted infrastructure for full data control.
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