AI/ML & Agentic AI Services
Upvantix delivers a working POC for one qualified AI use case in five business days and can turn the validated use case into a secure, integrated and observable production capability through a focused 30-business-day build.
The 5/30 AI Delivery Commitment
5
Business days
Working AI POC
30
Business days
Production AI use case
Built to scale and operate after launch.
The 5/30 AI Delivery Commitment
Start with a qualified use case, prove that it deserves to move forward, then build the approved production capability.
Validate the workflow, data, quality and technical feasibility before approving a production build.
Turn the validated POC into one secure, integrated and observable production capability within the approved scope.
Qualification
Discover confirms readiness before either delivery clock begins. Customer, vendor or third-party delays pause the schedule; major migrations, extensive data remediation and regulated validation require separate scope.
The adoption gap
A production capability must work with enterprise data, applications, controls, economics and accountable teams—not only demonstrate a model.
Give AI secure, permission-aware access to the information required for a useful answer or action.
Connect models and agents to ERP, CRM, ITSM, databases, APIs and real business workflows.
Evaluate accuracy, control undesirable behavior and keep people accountable for material decisions.
Monitor cost, latency, failures and quality as a successful proof becomes a production capability.
Our AI engineering capabilities
Depending on the use case, Upvantix combines the appropriate strategy, AI, data, integration and production-engineering capabilities required to achieve the agreed outcome.
Start with the business problem—not the model.
Identify valuable AI opportunities, establish feasibility and determine what should actually be built.
Outcome
A prioritized roadmap connecting business problems, AI opportunities, architecture and implementation.
Bring enterprise knowledge into the flow of work.
Build AI applications that help employees and customers find information, understand complex content and make better-informed decisions.
Outcome
A useful AI application grounded in enterprise knowledge and embedded in the workflow where it creates value.
Move from AI that answers to AI that gets work done.
Design agents around real workflows so they can gather context, coordinate steps and use enterprise tools within defined controls.
Outcome
A controlled agentic workflow that can assist, recommend or act with the right approval and accountability.
Use the right AI for the problem.
Apply predictive, analytical or perception models when they are a better fit than a large language model.
Outcome
A fit-for-purpose model designed around the decision, signal or prediction the business actually needs.
Connect AI to the systems where business actually happens.
Engineer the data, application and integration layer that brings models and agents into existing business workflows.
Outcome
An integrated AI capability that works with the customer’s enterprise architecture rather than sitting beside it.
Production AI needs more than a working model.
Establish the engineering discipline needed to evaluate, monitor, govern and continuously improve AI in production.
Outcome
A more observable, governable and repeatable operating model for production AI workloads.
Agentic AI
Agents can gather context, use enterprise tools and coordinate work. The architecture determines where they may act and where a person remains accountable.
Understand intent
Gather enterprise context
Plan required actions
Coordinate specialized agents
Use enterprise tools
Request human approval
Execute and validate
Record the result
Human approval is an architectural control. Approval gates can be introduced whenever a decision, transaction or operational action requires human accountability.
Start with the objective, systems, permissions, exception paths and expected result.
Retrieve customer context, prepare responses, update service systems and escalate exceptions.
Classify incidents, investigate events and coordinate approved remediation steps.
Process documents, validate information, reconcile records and route approvals.
Analyze requirements, compare vendor information and prepare procurement workflows.
Research accounts, summarize CRM history and prepare meetings and follow-up work.
Answer employee questions, interpret policies and assist HR service workflows.
Assist requirements, code analysis, testing, documentation and delivery workflows.
Use asset context and technical knowledge to assist maintenance and field service.
Machine Learning & Applied AI
Select the approach that fits the outcome—Generative AI, an agentic workflow, a predictive model, computer vision or a combination of techniques.
Integration philosophy
Meet customers where their systems and data already live. Connect AI to existing applications and workflows instead of making migration a prerequisite for value.
Production operations
Production AI needs evaluation, observability, governance and lifecycle management from the beginning.
Evaluation and quality metrics
Guardrails and data-access controls
Observability and distributed tracing
Cost, latency and performance management
Model, prompt and workflow lifecycle
Audit records and human approvals
How Upvantix delivers AI
A practical delivery journey keeps business usefulness, engineering quality and production operations connected from the beginning.
Qualify the business problem, data, access requirements, success criteria, integration scope and delivery readiness.
Output
AI Opportunity & Implementation Plan
Build a focused POC that validates usefulness, data availability, workflow, quality, technical feasibility and production economics.
Output
Working AI POC + Production Recommendation
Turn the validated POC into one secure, integrated and observable production capability within the approved scope.
Output
Production AI Application or Agentic Workflow
Expand successful capabilities across data sources, workflows, teams and business units.
Output
Enterprise AI Capability
Monitor, evaluate, secure, optimize and continuously improve AI after deployment.
Output
Managed, continuously improving AI
Forward Deployed AI Engineering
Multidisciplinary delivery pods carry context from discovery and POC delivery into production engineering, reducing handoffs and avoiding a restart after the proof. This is an implementation-focused engagement model—not staff augmentation.
Architecture, model strategy and system design.
Generative AI, agents and application implementation.
Enterprise data, pipelines, knowledge systems and integrations.
Modeling, evaluation and Machine Learning.
Deployment, observability, CI/CD and production operations.
Business workflow, requirements and adoption.
The Upvantix difference
Four principles guide how we shape, build and operate AI capabilities.
Generative AI, Machine Learning and agentic systems are core engineering disciplines.
Design for integration, security, governance, observability and operations from the beginning.
Start with the business workflow and decide where AI should assist, recommend or act.
Stay involved from architecture and implementation through operations and continuous improvement.
Technology positioning
Use appropriate AWS AI, data, application and cloud services as the engineering foundation. Technology follows the architecture—not the other way around.
The 5/30 AI Delivery Commitment
We will assess its fit for the 5/30 delivery model and define the data, access, success measures and production scope required to begin.
