Founded in
2018
Years of product engineering across mobile, SaaS, backend, and web systems give us the foundation to turn generative AI models into complete, working software products.
Primocys is a Generative AI Development Company that designs and develops AI applications for text, documents, images, multimodal experiences, and structured content workflows. We engineer production-ready GenAI products with context control, guardrails, model routing, evaluation, backend systems, and cost-aware deployment.
Start your GenAI project →Services explain what Primocys can build for your business. Core capabilities explain the engineering behind those services—from model selection and context engineering to structured outputs, evaluation, guardrails, and usage controls that make generative AI production-ready.
Evaluate model capability, latency, context length, multimodal support and cost for each task, with routing or fallback patterns where one model should not handle every request.
Design system instructions, examples, dynamic context and conversation state so the model receives the information needed for the specific job without uncontrolled prompt growth.
Use schemas, typed responses, validation and deterministic post-processing when generated output must enter APIs, databases, workflows or product interfaces instead of remaining free-form text.
Combine text, image, document, audio or other supported modalities when the product genuinely benefits from multimodal understanding or content generation.
Apply application-level input/output validation, moderation, sensitive-data handling, policy checks and human review where product risk requires more than model instructions alone.
Build representative evaluation sets, trace model behavior, monitor failures and usage, and control context, caching, model choice and limits around the economics of the product.
Real engineering experience you can verify, not inflated numbers. As a generative AI development company, here’s what actually backs our delivery — the team, the products we run ourselves, and the process we follow on every project.
2018
Years of product engineering across mobile, SaaS, backend, and web systems give us the foundation to turn generative AI models into complete, working software products.
We don’t just build generative AI for clients — we build and run our own products too. That gives our team hands-on experience with real production releases, prompt and cost controls, subscriptions, and the operational realities most teams only read about.
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One team, the whole stack — LLMs, RAG, agents, guardrails, evaluation, backend, and deployment. No handoffs between vendors, no gaps between the model layer and the product it lives in.
Every project follows a clear path from discovery to deployment — we define the use case, review your data and systems, select and evaluate models, design prompts and guardrails, build it, test it, then release and keep improving based on real production evidence.
From startups to global enterprises, Primocys builds innovative technology solutions backed by 1,200+ products delivered, 650+ clients served, and 30+ experts.
These are commercial engagements clients can hire Primocys for. Each service may use several of the core capabilities above, but the final architecture depends on the content type, workflow, data, model requirements and production environment.
01
End-to-end GenAI products for creating, transforming or structuring content, including application UX, backend, model layer, controls and deployment.
02
Text and document applications using commercial or open-weight language models with structured outputs, context management and evaluation.
03
Controlled generation workflows for product descriptions, marketing content, reports, documents and other repeatable content tasks.
04
Products combining text with images, documents, vision or audio where multiple input or output modalities are central to the experience.
05
Review and improve your existing prototype or live app across model choice, prompts, context, output reliability, latency, cost, and production operations.
06
Review and improve an existing prototype or application across model choice, prompts, context, output reliability, latency, cost and production operations.
Explore real-world AI products developed and operated by Primocys. Discover how we turn innovative ideas into practical digital experiences with measurable outcomes.
We build GenAI products around what users need to create, transform or accomplish—from focused AI features within existing software to complete AI-native applications.
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Generate drafts, reports, proposals and structured documents using templates, business rules and review workflows.
Build tools for drafting, rewriting and adapting content with controls for tone, format, brand voice and workflow.
Create products that generate or transform visual assets, with controls for editing, storage, moderation and downstream workflows.
Turn long-form content into concise summaries, action points, structured data or formats suited to different tasks.
Generate tailored stories, learning content, recommendations or guidance using relevant, approved context.
Integrate drafting, rewriting, extraction, summarization and generation into SaaS products, mobile apps, web platforms or internal tools.
Generative AI can help products create, transform, explain and structure information beyond what fixed rules can easily handle. We define each use case around a real user need—not simply adding a generic “Ask AI” button.
Generate first drafts, descriptions, summaries, documents and creative assets, with review and editing built into the workflow.
Convert lengthy or inconsistent text and documents into concise summaries, structured fields, classifications or reusable formats.
Generate tailored content or guidance using permitted user, product or workflow context instead of delivering the same static output to everyone.
Develop products where generation is a core capability, supported by the accounts, billing, usage controls, administration and production infrastructure needed to operate.
Hear how founders partner with Primocys to turn GenAI ideas into real-world AI products and workflows.
We select technologies according to modality, quality, context, latency, privacy, deployment and cost requirements. Not every product needs every provider or framework listed here.
We define what the model must do and how success is measured, keeping model choice, prompts and evaluation tied to real product needs, not endless experiments.
Production GenAI architecture separates the product from the model provider, giving teams control over context, outputs, policies, evaluation, performance and cost as the application scales.
A working prompt or prototype is a useful starting point. Primocys reviews the existing implementation and identifies what is needed for production across model behavior, context, backend architecture, user management, evaluation, cost controls and deployment.
Review model fit, system instructions, context growth, output consistency and failure cases to identify what needs improvement before production.
Turn the prototype into a usable product with accounts, UI, backend, storage, billing, limits, admin and operational controls.
Identify opportunities around model choice, context size, output length, caching and request patterns to improve production efficiency.
Integrate generation, summarization, extraction or other GenAI capabilities into an existing SaaS, mobile app, web platform or internal system.
A GenAI feature becomes useful software when the model, product experience, backend, permissions, billing, evaluation and production operations work together. Primocys engineers these layers as one product-development engagement.
Provider choice follows the task, quality, latency, multimodal needs and economics rather than forcing every feature onto one model.
AI, backend, web, mobile, SaaS and cloud engineering can be combined when the product requires them.
Representative inputs and expected behavior are used to evaluate quality instead of relying only on a few successful demo prompts.
Context size, output limits, caching, routing and usage controls are considered as part of product architecture.
GenAI development cost depends on more than the model API—scope, integrations, context, evaluation, infrastructure and expected usage all matter.
Number of generation steps, output formats, templates, editing flows, automation and product rules.
Text, image, vision, audio, long-context or specialized generation requirements can change model and infrastructure needs.
Dynamic context, conversation history, RAG, user data and document processing can increase engineering and usage requirements.
Evaluation datasets, moderation, structured validation, human review, failure handling and safety controls add to production scope.
Authentication, multi-tenancy, billing, web/mobile interfaces, admin tools, analytics and user management shape the overall build.
Model choice, context size, output length, caching, usage limits, concurrency and infrastructure influence ongoing AI operating costs.
From healthcare RAG pipelines to FinTech copilots — we build GenAI features with the compliance, security, and eval rigor each industry actually needs.
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