A data-driven approach to model an in-demand product that includes business analysis, proof of concept (POC), design concept, and project estimate.
A data-driven approach to model an in-demand product that includes business analysis, proof of concept (POC), design concept, and project estimate.
A complex human-centered process of developing a valuable product that blends business goals and user needs with design thinking in mind.
Uinno starts by mapping the business process you want to automate, identifying every input, decision point, and integration it touches. AI agents help during this phase by analyzing your existing data flows and ranking automation candidates by impact, so you know which workflow to build first.
The team maps every API, data source, and login flow your workflow needs. This step catches rate limits, data format issues, and access problems early, before they become expensive surprises during development.
Uinno builds each agent on top of existing models from OpenAI, Anthropic, and Google, tuned to your specific data and processes. AI agents also help the engineering team by generating connection code, drafting API connectors, and scaffolding the coordination logic, with every output reviewed by a human.
The team builds everything that makes the workflow reliable in real use: what happens when something fails, how the system retries safely, how it recovers without losing progress, and how your team gets alerted. This step is typically the largest portion of the engineering work.
Every workflow goes through testing with unexpected inputs, missing data, and failure scenarios that mirror real production conditions. AI agents generate test cases and run them automatically, catching problems that manual testing plans miss, especially in workflows where multiple agents interact.
The finished workflow ships with documentation, monitoring dashboards, and a runbook so your team can operate and extend it independently. Uinno provides post-launch support to catch any issues that only surface under real-world usage.
Two to three connected agentic workflows with orchestration, error handling, human-in-the-loop checkpoints, and cross-system integrations.
Includes retry logic, structured monitoring, and the infrastructure layer that makes multi-agent coordination reliable in production.
An end-to-end AI automation layer across multiple business functions.
Best works for teams that have validated their first workflows and are ready to expand agentic AI across the organization.
Full control over your workflows but
long hiring cycles, high overhead, hard to find engineers with production-grade agent experience.
Cost: $150K to $500K+ per year in salaries before the first workflow ships.
Team has already solved problems across multiple projects. Faster time to first deployment.
Team works with you to establish clear scoping upfront to stay on budget and timeline.
Cost: $5K to $250K+ depending on scope, with a working system in weeks
Fast for isolated tasks like building a single agent or connecting an API.
Limited experience with multi-agent systems, no team behind them for infrastructure, monitoring, or long-term support.
Cost: $50 to $200+ per hour, but total cost grows quickly once complexity increases.
We are not attached to a particular technology stack. We solve your business challenges using the most fitting programming languages and frameworks.
An agentic AI workflow uses autonomous AI agents that can reason, plan, and make decisions without step-by-step human instructions. Traditional automation follows rigid, pre-defined rules where if X happens, the system does Y. Agentic workflows let AI agents break complex tasks into subtasks, call external tools, evaluate results, and adjust their approach in real time. This makes them ideal for processes that involve unstructured data, variable inputs, or multi-step decision-making that rigid rules cannot handle.
AI workflow automation delivers the highest return in industries with high-volume, repetitive processes that still require human judgment: fintech for fraud detection and compliance checks, healthcare for patient intake and claims processing, e-commerce for order routing and customer support, and SaaS for onboarding and usage-based billing. Any business that spends significant time on document processing, data entry, or multi-system coordination is a strong candidate for agentic automation.
Custom AI workflow automation connects to your existing tools through APIs, webhooks, and native integrations. Common integration points include CRMs like Salesforce and HubSpot, ERPs, databases, communication tools like Slack and email, and cloud platforms like AWS, GCP, and Azure. Uinno builds the integration layer first during discovery to confirm feasibility, validate rate limits, and test authentication flows before writing any agent logic.
A single agentic workflow typically takes 4 to 6 weeks from scoping to deployment. A multi-agent system with 2 to 3 connected workflows, monitoring, and API integrations takes 8 to 12 weeks. Full production deployment with error handling, scaling, security, and compliance adds another 4 to 8 weeks depending on requirements. The fastest path is automating one high-impact workflow, proving the value, then expanding to the next.
you have a vision
