AI and software engineering services, from the agent to the cloud.

They are the engineering work that puts AI and software into production inside a business. That covers AI agents and LLM applications, the custom platforms they work in, the modernization of older systems, the data engineering they draw on, the cloud they run on, and the managed services that keep them running afterwards. DigyAi provides each one on its own or as one program.

Enterprise AI services design, build and run AI inside a company's own systems and under its rules, such as assistants over company records, document intelligence, AI features in a product, or agents that complete work in business applications. What makes the work enterprise grade is everything around the model, from permissions and an audit trail to evaluation on the company's own cases, cost control and support after launch.

Start from the problem. If a pilot worked but never reached production, AI development. If the work should be done and not just answered, AI agent development. If the numbers disagree with each other, data engineering. If an old system blocks everything else, application modernization. If you are not sure, describe the problem and we will map it to the services it needs.

AI development builds applications that answer, read, predict and generate, such as an assistant over your records or an AI feature in your product. AI agent development builds software that acts. An agent operates your systems and completes tasks, and it waits for a person wherever a mistake would be expensive. Many programs need both, and the agent usually stands on the AI development work.

With a short, fixed-scope discovery. We grade the pilot on a set of your own cases, find out what stopped it, whether that was data access, quality, a security review, cost or an integration, and write a plan that closes each gap. Whatever holds up in the pilot is kept. Most stalled pilots need AI development and data engineering; some also need the system they read from modernized.

With a few sentences about the problem. We come back with the questions that matter or a first step, then a short call, then a fixed-scope discovery. Discovery needs access to the systems involved and time with the people who run the process today, and it ends in a written plan you keep whatever you decide, with the architecture, the risks and the milestones for each later phase.

Discovery usually starts within one to two weeks of signing. If we cannot start when you need us, we say so before you sign.

A discovery, an audit or one workflow put into production are all good first projects. A large program is built the same way, one workflow live first and then the next, with each phase scoped and approved in writing before it starts.

By what drives the work. The main drivers are how many systems have to be connected, the state of the data, the quality bar the work must meet, the volume it runs at, and how much of it we run after launch. Each phase is scoped and approved in writing before it starts, beginning with discovery, so you commit one phase at a time.

No. Invoices follow milestones you have accepted, in USD, GBP, EUR or INR, so there is no upfront payment designed to make leaving expensive.

New scope is written down and estimated before it is built, and you decide whether it goes ahead. If an estimate turns out wrong, we tell you as soon as we know, with the reason and the options, and nothing outside the approved scope is billed without your written approval.

Yes, and most clients do. Each service stands on its own, in your environment and your repository, and works alongside your team or your other vendors. When a program needs several services, the same engineers carry it across all of them, so nothing falls between contracts.

If you can describe the finished thing, choose scoped phases. Each phase is written up and approved before it starts, and you commit one phase at a time. If priorities will change from month to month, a dedicated team fits better, with named engineers working on your priorities every month under the same standards, so nothing has to be re-scoped to change direction. Once the system is live, either one can continue as managed services.

We work in your own tools, such as Slack or Teams, Jira or Linear, and GitHub or GitLab, within a daily overlap window agreed in the contract. For UK and European teams that covers most of the working day, and for US teams it is a morning window. You get a written update every week, a demo every two weeks and, on longer engagements, a monthly review of scope, risks and spend. The engineers who scope the work are named in the proposal and build it, and none of them changes without your written approval.

Yes. We start with a written audit of the code, infrastructure and runbooks, stabilize what is fragile, then improve it or run it under managed services. The audit is yours to keep whatever you decide.

Yes. We build in your AWS, Azure or Google Cloud account or in your own data center, sign in through your identity provider and work under your keys. Nothing important lives only on our side.

You do. Code, prompts, pipelines, infrastructure and runbooks live in your repository from the first commit, intellectual property is assigned to you in writing, and your data stays in your environment.

Handover is a deliverable with its own acceptance criteria. It covers documentation, decision records, runbooks and working sessions with your team, and because the code has been in your repository all along, there is nothing to transfer.