Question 1

What services does iShiftAI offer?

Accepted Answer

We help organizations design, pilot, and scale agentic AI solutions on Azure. Typical engagements include AI strategy, Azure AI Foundry architecture, multi-agent workflow delivery, knowledge assistants, secure integrations, and production hardening.

Question 2

What industries do you specialize in?

Accepted Answer

Our strongest experience is in financial services, healthcare, manufacturing, retail, and enterprise operations. The common thread is building secure AI systems where governance, reliability, and measurable business value matter.

Question 3

Do you work with startups as well as enterprise teams?

Accepted Answer

Yes. We work best with teams that need senior architecture guidance and want to move quickly on Azure-native AI. Startups often engage us for roadmap clarity, while larger enterprises rely on us for production patterns and governance.

Question 4

How long does a typical engagement take?

Accepted Answer

Strategy sprints typically run 2 to 3 weeks, pilots usually take 4 to 6 weeks, and production launches often span 8 to 16 weeks depending on integrations, security review, and rollout complexity.

Question 5

What does a proof of concept include?

Accepted Answer

A proof of concept focuses on one high-value workflow, success metrics, a secure Azure environment, and working user flows. We also include evaluation criteria, telemetry, and a clear recommendation on what to productionize next.

Question 6

How do you measure success?

Accepted Answer

We define success in business terms before we start. That usually means cycle-time reduction, automation rate, quality improvements, adoption metrics, cost-to-serve changes, or risk/compliance improvements tied to a baseline.

Question 7

Why do you recommend Azure AI Foundry?

Accepted Answer

Azure AI Foundry gives teams a strong combination of model access, governance controls, enterprise identity, and integration options. It fits especially well when security, compliance, and managed deployment matter as much as model quality.

Question 8

What is Semantic Kernel and when do you use it?

Accepted Answer

Semantic Kernel is Microsoft's orchestration SDK for building AI-powered applications with tools, memory, and planning patterns. We use it when teams need modular, testable orchestration between prompts, tools, and specialized agents.

Question 9

What do you mean by agentic AI?

Accepted Answer

Agentic AI refers to systems that can plan, use tools, make bounded decisions, and coordinate work toward a goal instead of only generating one-off responses. In practice, that means more automation, but also a bigger need for guardrails and observability.

Question 10

How do you price engagements?

Accepted Answer

We use a mix of fixed-fee strategy and pilot packages, milestone-based delivery for production launches, and monthly retainers for ongoing architecture support. We recommend the model that best fits your team's level of clarity and execution capacity.

Question 11

Do you offer fixed-price projects?

Accepted Answer

Yes, especially for strategy sprints and tightly scoped pilots. For broader delivery programs, we usually combine milestone pricing with clear assumptions so both scope and risk stay visible.

Question 12

What is a typical budget range?

Accepted Answer

Most early engagements start between $7k and $60k depending on whether you need advisory, a pilot, or a more integrated proof of value. Larger production implementations usually scale with the number of workflows, integrations, and governance requirements involved.

Question 13

How do I get started?

Accepted Answer

The easiest path is to book a strategy call or share your use case through the contact form. We will align on your goals, current architecture, and where an AI initiative can create the fastest measurable value.

Question 14

What should I prepare before the first meeting?

Accepted Answer

Bring your top business goals, the workflow you want to improve, known data or integration constraints, and any security or compliance requirements. That context lets us recommend the right engagement model quickly.

Question 15

What happens after the first meeting?

Accepted Answer

We summarize the opportunity, recommend the next best engagement model, and outline the technical and business milestones needed to get results. If there is a good fit, we move into a scoped sprint or pilot with explicit success criteria.

Answers for teams building Azure-native AI

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Credentials and recent delivery

Answers for teams building Azure-native AI

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