From workflow discovery and autonomy design to integration, production monitoring, and ongoing maintenance, we build Agentic AI systems that execute multi-step business processes within defined permissions, approval controls, and governance policies.
A leading luxury hotel chain needed a smarter way to manage guest inquiries, service requests, and support operations across multiple channels while maintaining a premium customer experience.
Improved guest support efficiency, streamlined issue resolution, and enhanced customer satisfaction through AI-driven automation.
BigOhTech enables controlled autonomy through permissioned tools, human approval gates, and auditable execution.
You are not buying autonomy as a slogan. You are buying a team that proves one agentic KPI, hooks it into your systems, and keeps it safe when tools fail.
Autonomy only helps when it matches how your industry moves work. We shape agents around the systems and decisions you already trust.
Our six-step process takes Agentic AI from workflow discovery and POC evaluation to MVP development, production deployment, enterprise integration, and continuous optimization
Start with a scoped autonomy pilot, expand to a full agentic build, or embed a dedicated team. Pricing stays Custom until we see your tools and risk limits.
Explore real-world AI implementations across industries, showcasing how BigOh Tech helps businesses streamline workflows, improve operational efficiency, and deliver measurable business outcomes.
Chatbots and RPA stop when the next step needs judgment and tools. Compare what you need when multi-step autonomy has to survive production.
Explore common Agentic AI use cases for document processing, support automation, operations management, research workflows, and enterprise copilots.
Stack choice follows your cloud standards, tool surface, and how much autonomy you can defend. We stay on platforms BigOhTech already delivers with.
These platforms enable secure model hosting, enterprise integrations, scalable agent orchestration, and flexible deployment across cloud, hybrid, and on-premises environments.
We build agents that plan and act inside your workflows so autonomy shows up as finished work, not another demo chat. Why do so many agent experiments never become scaled Agentic AI Solutions?
These pieces combine so your agents can plan, act, and stop safely when the next step is too risky.
Versioned prompts and tools, eval re-runs, rollback plans, and scheduled updates when your workflows change.
An AI Agent is a single autonomous entity designed to perform a specific task. Agentic AI is a broader system that may include one or multiple AI agents working together, making decisions, using tools, and coordinating actions to achieve complex business objectives.
An Agentic AI development project can include workflow discovery, KPI definition, agent architecture, tool and API integration, data and knowledge setup, guardrails, human approval controls, testing, production deployment, monitoring, and operator training. The exact scope depends on the workflow, systems, data, security requirements, and level of autonomy required.
"Cost scale with autonomy and integration depth. A single-workflow POC starts at around $4,500. MVP builds with one channel to run $10,000–$25,000. Multi-agent systems and enterprise rollouts with CRM/ERP write-back and compliance typically range from $40,000 to $150,000+. Ongoing maintenance — evals, prompt tuning, monitoring — runs 15–20% of the original build cost per year."
We evaluate Agentic AI systems using task-success rates, tool-call accuracy, response quality, escalation performance, failure rates, latency, cost per workflow, and safety checks. Historical and staged data can be used to test the workflow before wider production deployment.
Agentic AI solutions can be built using various large language models, including OpenAI GPT models, Anthropic Claude, Google Gemini, Meta Llama, Mistral, and other open-source or enterprise-grade models. The choice depends on performance, security, deployment requirements, and business objectives.
Yes. Agentic AI can be deployed on premises, in a private cloud, or in a hybrid environment depending on security, compliance, and data residency requirements. This is particularly important for highly regulated industries such as healthcare, banking, and government sectors.
ROI is typically measured through business outcomes such as reduced operational costs, time savings, increased productivity, faster response times, improved process efficiency, and higher customer satisfaction. Organizations often evaluate ROI by comparing implementation costs against the measurable benefits delivered over time.
Yes, when implemented with proper governance controls. Enterprise-grade Agentic AI solutions can include role-based access controls, audit logs, approval workflows, data encryption, compliance monitoring, model governance, and secure integrations to ensure data protection and operational security.
Share your project goals, timeline, and technical requirements. We'll review your requirements and send a tailored solution with an indicative estimate within 48 business hours.
