AI development services
Easital Technologies Ltd. is an AI development company. We design, build and operate custom AI software: agents, generative AI features, LLM integrations, retrieval systems, chatbots, voice agents and automation. Every project is scoped around one job, tested against real examples and monitored after release, the same way we run the AI products we own.
AI development services are the engineering work that puts a model to use inside a product or a business process: choosing the model, connecting it to your data and systems, testing what it produces, controlling what it costs and keeping it running.
Most projects start from a foundation model, such as a large language model or a speech model, and the work is in the system around it: instructions, retrieval of your data, tools the model may call, checks on its output and monitoring. That system decides whether an AI feature is dependable, and it is what an AI software development company is hired to build.
The first decision is how much to build. An off-the-shelf tool is the right answer for a common task. Custom AI solutions pay off when the feature is part of your own product, depends on your data or rules, or must meet data-handling requirements that a public tool cannot.
| Off-the-shelf AI tool | Custom AI on hosted models | Custom AI on private models | |
|---|---|---|---|
| Fits when | The task is common and the tool already does it | The feature belongs in your product or process | Data cannot leave your network, or volume is high and steady |
| Your data | Goes into the vendor’s product | Is sent to a model API under that provider’s terms | Stays on servers you control |
| Running cost | Per seat or per plan | Per token, per minute or per call | Hardware and operations |
| What you own | An account | The system around the model | The system and the model deployment |
Our AI work is organized into ten services, each with its own page.
A language model plans the steps of a task, calls tools and checks its own work: agentic workflows, tool-calling systems and multi-agent orchestration. See AI agent development.
Features that produce text, speech, structured data or code inside your product, with tests on the output and a budget per request. See generative AI development.
Model selection by test, integration through provider APIs, control of the output format, and fine-tuning when a base model falls short. See LLM development.
Answers grounded in your own documents and records through retrieval-augmented generation, with sources cited and retrieval quality measured. See RAG development.
Chat assistants for support, sales and internal teams that answer from your content, act in your systems and hand over to a person when they should. See AI chatbot development.
Agents that hold a phone call: speech recognition, a language model, a synthetic voice and telephony. Easital has built voice-AI calling infrastructure. See voice AI agent development.
Repetitive work in support, marketing and operations handled by models inside a controlled workflow, with human review where mistakes are costly. See AI automation services.
Lower token spend in a system that already runs: model routing, caching, shorter context and fewer calls, each checked against output quality. See LLM cost optimization.
Open-weight models on your own servers or private cloud, for data that cannot go to a public API. Easital has set up local and on-premise models. See private LLM deployment.
Engineers from Easital who work inside your repository, backlog and review process when your roadmap needs more people. See hire AI developers.
An AI project at Easital runs in eight steps, whatever it produces. The test comes before the build, and the simplest design is tried first.
One job, its owner, and the number that will show whether it worked, such as time per ticket or calls answered. We also record what a wrong output would cost.
What the feature needs to read, where that data lives, who may see it and whether it may go to a hosted model. This settles hosted versus private models early.
Real examples of the task with the result a competent person would produce. Every later change, from a new prompt to a new model, is scored against it.
A single model call before retrieval, retrieval before an agent, a hosted model before fine-tuning. A part is added only when the evaluation shows the simpler design falls short.
Validation of inputs and outputs, limits on what the system may do elsewhere, a path to a person, and human approval for actions that cannot be undone.
We measure tokens and latency per request, then cut both with smaller models for easy steps, cached context and shorter prompts. Easital has built LLM token-cost optimization before.
A small share of traffic first. Every request is traced, unusual results are flagged for review, and alerts fire on error rate, latency and spend.
The system, its evaluation set and its runbook are documented so your team can take over, or we keep operating it. Production failures join the evaluation set.
Four live systems show this work in production: three that Easital owns and runs, and one built for a client.
Easital product
Built and run by Easital
A screen-recording-to-tutorial AI pipeline: one recording becomes a narrated how-to video, a written guide and a share page. Language and speech models from more than one provider run in one pipeline.
Easital product
Built and run by Easital
A vibe-coding platform. Its site describes AI chat, an agentic code editor, a cloud sandbox with live preview and one-click deployment, joined to a marketplace of starter kits and freelance developers.
Easital product
Built and run by Easital
AI automation for online stores. Its site describes one dashboard for building websites, creating AI content, automating marketing and supporting customers.
Client project
Built by Easital for a client in the United States
Voice AI calling infrastructure. The Calldone site describes agents that place and receive real phone calls, with a recording, a transcript and a summary of every conversation.
Ask for evidence of production work, not of demos: a live system you can open, an explanation of how its output is tested, and a plain account of what it costs to run.
An AI feature usually ships inside a larger product, so Easital also builds the product around it and repairs products that were assembled in a hurry.
Models and infrastructure are chosen per project, by test. We work across providers, so this list is by function and names none of them.
Easital works with all major commercial model providers and with open-weight models. Each task gets the model that passes its tests at the lowest cost, and the system around it lets the model be replaced later.
Commercial and open speech engines in both directions, chosen for language coverage, response time and price.
The main cloud carriers, plus phone systems a client already runs, connected over SIP.
Three ways to engage Easital for AI work: a finished system, more engineers or an independent review.
One AI system taken through the eight steps above, then handed over or operated by us.
Best for: a defined use case with an owner on your side.
Engineers from Easital join your team and work in your repository and review process. See hire AI developers.
Best for: teams with an AI roadmap and too few people to deliver it.
We review a system that is unreliable, slow or expensive: its prompts, retrieval, traces, evaluation coverage and spend, then a written list of fixes in priority order.
Best for: AI features that passed the demo and disappoint real users.
An AI development company designs, builds and operates software that depends on a model: it scopes the use case, selects and tests the model, connects it to data and tools, adds checks on the output and monitors the system in production.
Custom AI solutions are AI systems built around one company’s data, tools and rules instead of bought as a general product. They are worth building when the feature is part of your product, has to act inside your systems, or uses data that cannot go into a public tool. For a common task that an existing tool handles, buying is usually the better decision.
Build cost depends on how many systems the AI connects to, the condition of your data, how much testing and human review a wrong output justifies, and whether models run on hosted APIs or your own hardware. Running cost depends on model usage per request and on volume. We estimate both after scoping and do not publish a price list.
Through a service that sits between your application and the model. It holds the prompts, fetches the context each request needs, calls the model, validates the result and records the request. Your application calls it like any other API, so existing code changes little and the model can be replaced later.
Both. Most projects start from an existing model, because a tested prompt or a retrieval layer is cheaper than training. Easital has also done model training and fine-tuning, used when a base model cannot reach the required quality, cost or speed. See LLM development.
Yes. Open-weight models can run on your servers or in your private cloud, so prompts and documents stay inside your network. Easital has set up local and on-premise models. See private LLM deployment.
Describe the job, the data it depends on and what a wrong result would cost. We reply by email with questions and a proposed first step.
Easital is an AI and SaaS engineering company that takes AI software to production, and runs AI products of its own. Founded in 2019.