Generative AI development
Easital Technologies Ltd. is a generative AI development company. We build features that produce text, speech, structured data and code inside your software, and we take them from a tested prototype to a monitored release. Two of our own products, StepVideo and Manob.ai, are generative AI systems that we build and operate.
Generative AI development is the work of building software around models that produce new content, such as text, speech, images or code, in response to an input.
The model is the smallest part of that work. A generative feature in production needs four things around the model: the right context supplied with each request, a defined shape for the output, a check that the output is acceptable, and a limit on what each request may cost. A demo skips all four, which is why a feature that impressed in a meeting can fail once real users arrive.
Generative AI is often confused with agentic AI. A generative feature takes one request and returns one piece of content, and your own code decides what happens next. An agent uses the same kind of model to decide and carry out a series of actions. Many products need only the first, and it is cheaper to build and easier to test.
| Predictive ML | Generative AI | Agentic AI | |
|---|---|---|---|
| What it produces | A score, a class or a forecast | New content: text, speech, images, code | A completed task, through a series of actions |
| Typical use | Fraud scoring, demand forecasts | Drafting, summarizing, narration, extraction | Handling a ticket, a phone call or a code change from start to finish |
| Who decides the next step | Your code | Your code | The model, within the limits you set |
| Main risk | Accuracy drifts as data changes | Fluent output that is wrong | Wrong actions and runaway cost |
When the task needs actions as well as content, see AI agent development.
Our generative AI development services cover six kinds of feature. Each is built on your data and tested against examples of the output you expect.
Drafts, replies, summaries, product descriptions and reports written from your records in your tone. The facts come from data supplied with the request, not from what the model remembers.
Emails, PDFs, transcripts and forms turned into typed fields that your systems can store and act on. Every result is validated against a schema before it is saved.
Speech-to-text, synthetic voice-over and spoken replies. StepVideo writes a narration for each step of a recording and voices it, and the same parts drive a voice AI agent.
Features that write or change code, run it in an isolated sandbox and show the result before anything is deployed. This is the core of Manob.ai, our vibe-coding platform.
Multi-stage jobs with no chat window: a file goes in and finished assets come out. Each stage has its own model, its own check and its own retry, and the job runs on a queue.
A panel in your application that knows the user’s account and current screen, and drafts, explains or fills in on request. For conversational interfaces, see AI chatbot development.
A generative AI build runs in seven steps that start from the output, not from the model. The model is chosen in step three, once there is something to test it against.
We write acceptance criteria for a good result: the facts it must contain, its length, tone and format, and the things it must never say.
Real inputs paired with reference outputs, including the awkward cases: empty fields, long documents, mixed languages. This set scores every later change.
Candidate models run against the examples and are compared on quality, latency and cost per request. We take the smallest model that passes.
Facts are supplied with the request, from a database lookup or from retrieval over your documents, so the model writes from evidence. Retrieval is covered on the RAG development page.
Output schemas, validators and content filters, a second-pass check where the stakes call for one, and a defined fallback when a check fails: retry, a simpler template or a person.
Token limits per request, cached context for repeated instructions, batching for bulk jobs, streaming so that long answers feel fast, and per-user quotas where usage is billed.
A staged rollout with sampled outputs reviewed by people, user feedback captured next to each result, and alerts on cost, latency and failed checks. The example set is run again whenever a provider changes a model version.
Four live systems generate content for real users. Easital owns and runs three of them and built the fourth for a client.
Easital product
Built and run by Easital
Generated narration and guides, with no chat window. The StepVideo site says narration is written per step, and that product and company names are read off the recorded page instead of guessed from the audio. It also lists translation of voiceovers and captions into 58 languages.
Easital product
Built and run by Easital
Code generation as a product. The Manob.ai site describes an AI chat and agentic code editor with a cloud sandbox and live preview, so generated code is run and seen before it is deployed.
Easital product
Built and run by Easital
Generated content for commerce. The mAutomate site describes creating AI content and publishing and replying across Facebook, Instagram, WhatsApp, Telegram, X and LinkedIn from one dashboard.
Client project
Built by Easital for a client in the United States
Generated speech in real time. Calldone’s terms state that its agents use automated speech recognition and large language models to hold conversations and to produce voice output, transcripts and summaries.
A generative AI consulting engagement with Easital ends in a written decision: which use cases are worth building, what each would cost to run, and what to build first.
It suits a company with a list of ideas and no way to rank them. We test the most promising one on a sample of your real data, because a use case that looks strong on a whiteboard can fail on real documents.
Generative AI integration means adding a generative feature to software you already run without rewriting it. The feature lives in a service that sits between your application and the model.
That service holds the prompts under version control, gathers the context for each request, calls the model, validates the result and records what happened. Model credentials stay on the server. Long jobs, such as a batch of documents, run on a queue, and short ones stream their answer to the screen.
Because your application talks to the service and not to a model vendor, the model can be changed later for a cheaper one, a newer one or a private one on your own servers. The same layer enforces per-user quotas and switches to a second provider when the first is unavailable.
Models are selected per feature, by running them against your examples. No feature is tied to one vendor, and the parts are described by what they do.
Every major commercial provider and open-weight models are candidates. The smallest model that passes your examples is used, behind a service layer that lets it be swapped.
Commercial and open speech engines, compared on voice quality, language coverage, speed and price.
Generative AI work with Easital takes one of three forms. Consulting, described above, can come before any of them.
One generative feature taken through the seven steps above, from acceptance criteria to a monitored release inside your product.
Best for: a feature with a clear input, a clear output and an owner on your side.
You can hire generative AI developers from Easital who join your team and work in your codebase and review process. See hire AI developers.
Best for: product teams that have the roadmap and lack the capacity.
We review a generative feature whose output is inconsistent or whose bill is growing: prompts, context, checks, model choice and spend, with a written list of fixes in priority order. See also LLM cost optimization.
Best for: features that were launched from a prototype and never hardened.
A generative AI development company, or gen AI development company, builds software features in which a model produces content: text, speech, structured data, images or code. The work covers defining what a good output is, selecting a model by test, supplying the right context, checking the output and controlling the cost of each request.
Generative AI produces content in answer to a request. Agentic AI uses the same kind of model to choose and carry out a series of actions toward a goal. A generative feature is simpler, cheaper and easier to test, so we use an agent only when the task needs decisions across several steps. See AI agent development.
Invented output cannot be ruled out, so the system is built to make it rare and to catch it. Facts are supplied with the request, the model is told to answer only from them, the output is validated against a schema or checked by a second pass, and a failed check leads to a retry or to a person.
A shortlist of use cases, a working test of the most promising one on your own data, a running-cost estimate from measured token counts, a recommendation on hosted or private models, and a staged build plan. The result is a written decision you can act on, whether or not Easital does the build.
Yes. Generative AI integration services place a service between your application and the model. The application calls it like any other API, so existing code changes little, credentials stay on the server and the model can be replaced later.
That depends on the provider and the plan, so we read each provider’s data terms before any of your data is sent and set up the account accordingly. Where data may not leave your network, the feature can run on an open-weight model on your own servers. See private LLM deployment.
Describe the input, the output you expect and how many requests you foresee. 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.