Founded in
2018
Since 2018, Primocys has built SaaS platforms, mobile apps, backend systems, APIs, and AI-powered products. That experience gives us the engineering foundation to build complete AI agent systems from idea to production.
Primocys delivers custom AI agent development services that automate repetitive work across your CRM, support, and ops tools, with human approvals built in, so your team saves hours every week and scales without adding headcount.
Reliable AI agent development takes more than an LLM. Tell us your workflow and we’ll engineer planning, tools, memory, permissions and evaluation.
Multi-step execution paths where the agent understands the goal, tracks workflow state and knows when to stop, so tasks finish without constant supervision.
Secure connections to approved APIs, databases and business systems, with validated inputs, retry rules and clear action limits, so agents never act outside scope.
Session context, workflow state and business knowledge kept separate, so your agent remembers what matters and never carries unauthorized data forward.
Role-based permissions and approval gates for high-impact actions like refunds, record changes and outbound messages, so your team stays in control.
Traces, logs and evaluation sets test task completion, tool choice and failure cases, with fallbacks for uncertain outputs, so issues surface before users do.
We decide when one agent is enough or specialists should coordinate, and route each step to the right model or rule, balancing quality, latency and cost.
Building AI agents requires more than prompting an LLM. Our team combines AI engineering, backend systems, integrations, deployment, and production operations to deliver AI agents that work reliably in real business environments.
2018
Since 2018, Primocys has built SaaS platforms, mobile apps, backend systems, APIs, and AI-powered products. That experience gives us the engineering foundation to build complete AI agent systems from idea to production.
We build and operate our own AI products—not just client projects. That gives our engineers real production experience with AI agents, subscriptions, knowledge retrieval, workflows, permissions, and continuous product operations.
AI Customer Support Agent for Businesses
AI Storytelling & Learning Companion
One engineering team delivers the complete AI agent stack—from LLM orchestration and backend APIs to frontend applications, cloud deployment, business integrations, and production monitoring.
Every AI agent project follows a structured delivery process—from discovery and workflow design to knowledge integration, evaluation, production deployment, monitoring, and continuous improvement based on real business usage.
We build, deploy, and manage production-ready AI agents for customer support, workflow automation, and business operations that deliver real results.
Tell us your workflow and we’ll build an AI agent around your systems, permissions and the level of autonomy you need, ready to deliver results from day one.
01
Purpose-built agents for a specific business workflow, covering orchestration, tools, permissions and deployment, so your team gets a reliable agent from day one.
02
Agents that coordinate multi-step work across internal systems and route exceptions to humans, so your team spends less time on manual handoffs.
03
Support agents that use approved business knowledge, look up accounts through allowed tools and hand off to humans, so customers get faster answers.
04
Agents for lead research, qualification support, CRM preparation and follow-up drafting, so your reps spend more time selling and less on admin.
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Agents that combine private knowledge retrieval with approved actions, so every next step is based on your real business context.
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Specialist agents with shared state and controlled handoffs for complex workflows, so work stays organized instead of relying on one overloaded prompt.
Every product below was designed, built and is operated by our own team — not a proof-of-concept slide deck. See how our AI engineering holds up in live, everyday use.
The same agent architecture can power very different products. We define the workflow first, then decide whether the best implementation is an internal operations agent, embedded product feature, customer-facing assistant or multi-agent system.
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Research, qualification support, account preparation, structured CRM updates and approval-based outreach workflows.
Knowledge retrieval, customer context lookup, ticket classification, suggested resolution and controlled actions.
Extract, compare, classify, summarize and route business documents before triggering approved downstream steps.
Search approved company knowledge, reason over relevant context and complete bounded tasks using internal tools.
Coordinate repetitive operational tasks across databases, forms, email and internal systems with clear exception handling.
Embed tool-using or knowledge-grounded agent capabilities inside an existing SaaS, web platform or mobile product.
Get AI agents that actually finish the work. We build each one around your tasks, integrations and human approvals, with clear controls. See how businesses use them in our AI agents for business guide .
Move repeatable research, data lookup, routing, drafting and system updates away from manual handoffs where the workflow is suitable for automation.
Let one controlled agent coordinate work across approved CRM, ERP, ticketing, email, document and internal API layers instead of forcing people to copy data between them.
Allow low-risk steps to continue automatically while exceptions and higher-impact actions are routed to the right person for review.
Define where autonomy stops, what can be read or changed, and what must be approved so automation does not become uncontrolled access.
An agent project is rarely just an LLM task. It usually touches APIs, databases, user roles, frontend experiences, auditability, cloud infrastructure and existing application logic. Primocys brings those pieces into one product-engineering engagement.
We define task completion, permissions, failure paths and monitoring before treating the demo as finished software.
AI, backend, web, mobile and cloud work can be handled within the same delivery team when the product requires them.
Provider choice follows the use case. The application architecture should not make one model vendor the permission system or the entire product.
Source-code ownership, third-party components, infrastructure and licensing are defined clearly in the signed project agreement.
We do not force every project onto one framework. A typical production stack may combine model providers, orchestration, APIs, retrieval, databases, queues, monitoring and application infrastructure depending on the workflow.
The LLM is one component. Production quality depends on how goals, tools, state, data access, approvals, evaluations and failures are managed around it.
User request, business rules, state machine, routing, stopping conditions and completion criteria.
Validated interfaces to CRM, ERP, email, calendar, databases, internal APIs and other approved business systems.
Session context, durable state, retrieval, metadata, access rules and business knowledge appropriate to the task.
RBAC, action allow-lists, human review, audit events and permission boundaries for higher-impact operations.
Tracing, task-completion tests, tool failure handling, retries, cost monitoring, provider fallbacks and production feedback.
The model should never become the permission system. Application-level controls decide what the agent is allowed to read, change or trigger.
We start with the job the agent should complete, not with a framework. This prevents unnecessary multi-agent architecture and gives the team measurable success criteria before implementation.
You do not need to restart just because the first version was built internally, with a no-code tool, by another vendor or as a proof of concept. We can review what already works and identify the gap between the current implementation and production requirements.
Orchestration, tools, prompts, state, retrieval, permissions, failures and deployment.
Add authentication, controls, monitoring, evaluation, fallbacks and production infrastructure.
Integrate the agent into an existing SaaS, CRM, web app, mobile app or internal platform.
Stabilize useful work, replace fragile parts and continue from a clearer technical roadmap.
AI agents are not one-size-fits-all. Every industry has different workflows, compliance needs, and integration requirements. Primocys has built and deployed custom AI agents across all these verticals.
Your AI agent cost depends less on the model API and more on tools, integrations, workflows, data access and approvals. Know your budget before you build: read our AI agent development cost guide .
Number of steps, branches, stopping rules, exceptions and retries the system must manage.
CRMs, ERPs, databases, email, calendar, third-party APIs and internal systems the agent must use.
Ingestion, permissions, metadata, retrieval, citations, freshness and knowledge administration.
Read-only workflows are different from agents allowed to update records, send messages or trigger transactions.
Representative test sets, trace review, failure scenarios, fallbacks and task-completion criteria.
Authentication, dashboard, admin controls, mobile/web UI, audit logs and production infrastructure.
The questions every founder and CTO asks before building an AI agent. Don’t see yours? Ask us directly .
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