AI Development & Integration for Enterprises

iGates builds AI capabilities into real systems that run in production — not demonstrations. We develop AI agents, retrieval and reasoning over enterprise data, document processing, computer vision, and models that run on the device itself, then integrate them with the core systems you already operate.

What separates a working AI system from a convincing demo is not model access. It is the engineering required to make an AI capability secure, stable, observable and predictable in cost — and to make it fit an organisation that already exists.

What AI development actually means

AI development in a business context is not training models from scratch. It is building a new capability inside an existing product or system: an agent that completes a task, search that understands a question in plain language, a classifier that routes documents, or a model that detects a fault in real time.

Connecting a model and getting an answer is the easy part. Everything that decides whether the project reaches production is harder: where the data comes from, who is allowed to see what, what happens when the model is wrong, what it costs at scale, and how you know six months later that it still works.

That is ordinary software engineering. It is what we have done since 2010.

Our AI services

• AI capabilities inside an existing product — agents, assistants, retrieval over enterprise data (RAG), document and form processing, classification and routing, anomaly detection.
• Business process automation — reducing manual work in back-office processes, request handling, document processing and prioritisation.
• End-to-end AI product development — from specification to production, including infrastructure, monitoring and cost control.
• Private AI for regulated organisations — models running locally on your infrastructure, with no data leaving your environment.

In each case we take responsibility for the whole system, not only the model layer.

Private and on-premise AI

For defence organisations, public bodies and companies under regulatory constraints, the first question is not what the model can do. It is where the data goes.

We run open-weight models on your infrastructure — in your private cloud, in your data centre, or in an environment fully disconnected from the internet. Your data does not leave your environment and does not reach a third-party provider.

This is possible because of depth most software houses do not have. We have worked at the operating-system and hardware level for years — embedded Linux, drivers, and Android Internals — so deploying a model into a closed environment is familiar engineering for us rather than a research project. See Embedded Development.

Where there is no regulatory constraint we also work with the leading providers — OpenAI, Anthropic and Google — selecting per task rather than by habit.

Edge AI

Not every AI capability belongs in the cloud. When a response has to be immediate, when connectivity is unreliable, when bandwidth is expensive, or when the data is too sensitive to leave the device, the model has to run at the edge.

We develop models that run on devices and controllers: local inference, optimisation for memory and power, and integration with dedicated hardware. This connects directly to our work in embedded systems and IoT.

Our technical stack

Models: self-hosted open-weight models, plus OpenAI, Anthropic and Google.
Retrieval: pgvector on Postgres, self-hosted vector databases (Qdrant, Weaviate, Milvus), and managed services where no regulatory constraint applies.
Knowledge bases: structured Markdown and Obsidian knowledge bases — human-readable, editable, and fully ownable by the client.
Orchestration: LangChain, LlamaIndex, Semantic Kernel, and custom development where frameworks fall short.
Computer vision: image and object recognition, real-time processing, on-device inference.
Classical ML: forecasting, classification and anomaly detection over sensor and transaction data.
Infrastructure: monitoring, quality measurement, cost control, and CI/CD.

Work in the field

AI21 Labs

We have developed for AI21 Labs, one of Israel's leading artificial-intelligence companies.

Defence customer

AI capabilities built for a classified environment, including local models running without internet connectivity.

Computer vision in a consumer product

For LEGO Life of George we developed real-time image recognition connecting physical objects to a digital game.

Internal research and development

Some of our capabilities were built and tested internally first, including agent systems for software development. We do not sell a capability we have not run ourselves.

How we work with AI

Every interaction with a model sits inside an engineering process rather than replacing one.

• Architecture, specification and documentation happen before a model is involved, and the stack is chosen by us.
• Every agent output is reviewed by trained reviewer agents before it reaches an engineer.
• Development cycles are preserved in full — continuous integration, unit tests, QA and deployment.
• DevOps stays in our hands and under your control, not inside a third-party vendor's limited environment.
• Proprietary information and intellectual property are not passed through AI models.
• We avoid capabilities and integrations that would expose you to data leakage or security vulnerabilities.
• AI use is disclosed before the proposal, because it affects the pricing model.

What we do not do

• We do not train foundation models. We build systems on top of them.
• We do not jailbreak or abuse AI providers' services. Working within their terms is a compliance matter for you as well as for us.

How an AI project runs

1. Discovery — the business task, the data that exists, and what success means.
2. Focused feasibility — a fast test against your real data, before committing to full development.
3. Architecture — model, retrieval layer, permissions, monitoring, and an operating-cost estimate.
4. Iterative development — working versions with measured quality, not demonstrations.
5. Production and maintenance — monitoring, cost control, and updates as models and conditions change.