AI integration services that put AI inside the systems you already run, and keep it working

AI integration services connect AI models to the software you already use, so AI reads your real data and acts inside your real workflows. That covers choosing the integration pattern, building the APIs, connectors or MCP servers, setting permissions and human approvals, testing accuracy on your own cases, and running it after launch with monitoring, evaluations and cost control. The goal is AI your team uses every day, not a separate demo.

We start from what the system already offers: a REST API, webhooks, a database we can read safely, file exports, or as a last resort the screens your team uses. The AI gets its own service account with only the permissions it needs, reads data through that connection and writes back only through approved actions. Older systems without an API usually get a small connector service in front of them, so the core system itself does not change.

Calling an AI API takes a few lines of code. Integration is everything around it that makes the result safe and useful: pulling the right data from your systems, checking the answer, asking a person before risky actions, logging every step, handling errors and outages, and measuring quality and cost over time. We are not tied to one model vendor; we pick commercial or open-weight models per use case and can switch later without rewriting your integration.

MCP, the Model Context Protocol, is an open standard that lets AI assistants and agents use your systems through a defined set of tools, such as look up an order or create a ticket. An MCP server is that set of tools for one system. You need one when you want AI agents or assistants to work with your own data and actions in a controlled way. We can build them with narrow tools, OAuth 2.1 sign-in, scoped permissions, an audit log of every call and approval steps for anything that changes data.

MLOps is the practice of deploying, monitoring and retraining machine learning models, such as forecasts or computer vision models. LLMOps applies the same discipline to language-model features: test sets, prompt and model versioning, quality and drift monitoring, guardrails and cost tracking. Both matter because AI quality changes after launch as your data, your users and the models themselves change. Without them, accuracy drops and costs rise without anyone noticing.

Before launch, we build an evaluation set from your real cases and score every version of the prompt, model or pipeline against it, so a change that makes answers worse is caught before release. After launch, we monitor quality signals, user feedback, response time and cost per request, and alert when any of them moves. Costs are controlled by routing simple tasks to smaller models, caching repeat work and setting budgets per feature.

An integration review of your systems and use cases takes a few weeks. A first production integration for one workflow usually takes longer, depending on how many systems it touches and how clean their APIs are. We quote a fixed price for each stage before it starts, and the monthly run-and-improve service is priced separately, so you can see both costs.

Yes. Our engineering team is in Jaipur, India, and we work with businesses in the US, UK, Europe, the UAE, Australia and India. Workshops run on video calls in your time zone, the integration can run in your own cloud account, and you own the source code and credentials from day one.