AI MVP Development
AI integration services for products and platforms you already run
AI integration services for a product or platform that already has users, data and a release process. As an AI integration company, we find the places where a model changes what users get, prototype the strongest one in your own screens and build it into the system you run, with a plan for when the model is wrong. We added AI to a legacy video surveillance platform, and built AI features into a sales platform and an HR platform for a large energy company.
The situation
The model works in the demo and nowhere near your data
A vendor demo answers every question in the room. Back at the office, the data it needs sits in three systems, the release process has no slot for a model whose answers change, and nobody has decided what a user sees when the answer is wrong.
AI integration is less about picking a model than about where it plugs in: which data it may read, which screen shows its output, who checks it, and how it ships with the rest of the product. Our AI integration solutions start from those four questions.
AI integration into a legacy platform without stopping what it already does
A US maker of video surveillance hardware and software ran a legacy platform that large companies use to monitor machinery and equipment. It needed a new interface and AI. We began with a discovery and a product audit, which gave new specifications, a prioritized feature list and a roadmap, then redesigned the product and built the AI module into it.
The module recognizes and classifies objects, detects anomalies, creates reports with time, pictures and video, and tags footage so the right clip is easy to find. The AI services run on Python next to the existing services on Nest.js, hosted on AWS. The redesigned platform became a bestseller among the client's products and brought new corporate customers who buy a license and connect their cameras.
AI features inside a product built on the data it already holds
A US tech company that already ran several sales tools wanted one platform to connect them. Its AI helpdesk, built as generative AI integration, suggests replies to support tickets and can shorten, lengthen or summarize them, and its knowledge base recognizes a question and drafts the answer from what the company has written.
For a large US energy company we built an HR platform where HR managers get AI analysis of performance metrics that spots trends and the employees who need attention, and a scheduler connected to calendars suggests free slots and sends reminders. In both, the AI sits inside the screens people already use, not in a separate chat window.
How an AI integration runs audit, prototype, build
The AI Opportunity Audit is where our AI integration consulting services begin. It goes through your product and the data it holds, and ranks the places where a model would change what a user gets, with the reasoning and what happens when the model is wrong. The strongest idea comes back as a clickable prototype in your own screens, with an estimate.
The build runs on the Artkai AI SDLC: agents do the work inside each stage, and one accountable person authorizes what leaves it. Each AI feature ships with what the user sees when the model is unsure: a draft to approve, a source to check or a person to ask.
Case studies
AI-powered video surveillance platform
A legacy video surveillance platform redesigned, with AI object detection, anomaly detection and reporting added.Case study →
AI-powered sales platform
A sales platform that keeps customer data in one place: CRM, a helpdesk with suggested replies, a workflow builder and a knowledge base.Case study →
AI-powered human capital management
An HR platform for a large energy company: goals, performance tracking and rewards, with AI-assisted analysis for HR managers.Case study →
Questions
What do your AI integration services include?
An audit of where AI fits your product and its data, a clickable prototype of the strongest idea in your own screens, and the build into the system you run, on the Artkai AI SDLC. Each feature comes with a plan for when the model is wrong.
How is AI integration different from AI MVP development?
AI MVP development starts a new product. AI integration starts from a product or platform you already run, with its users, data and release process, and adds AI where it changes what users get. When the AI should take a step of a business process rather than a feature of a product, that is Business Process Automation.
Can you add AI to a legacy system?
Yes. For a US maker of video surveillance software we audited a legacy platform, redesigned it and built in an AI module for object recognition, anomaly detection and reporting, next to the services it already ran.
Who decides what goes live?
On the Artkai AI SDLC each stage ends in a document one accountable person on your side authorizes, so the scope, the prototype and the release of every AI feature are signed off before they move on.
Do you offer enterprise AI integration services?
Yes. Enterprise AI integration services here mean AI added to platforms that large companies already run, such as the HR platform we built for a large energy company, with each release signed off by one accountable person on your side.
What do AI system integration services cover?
They cover how the AI connects to the system you run: which data it may read, which screen shows its output, who checks it and how it ships with the rest of the product.
Related
AI MVP Development
A new product scoped, prototyped and taken to its first release on the Artkai AI SDLC, then maintained under the same method.
AI MVP Development
When the product itself is new, not one you already run.
Generative AI
When the feature is generative: a draft, a summary or an assistant inside your product.
AI Agents
When the AI should take a step of a business process rather than a feature of a product.
Start with a call
A 30-minute call with someone who has delivered this kind of work — sales and an engineer together.