Unclear business case
Projects start with tools instead of workflows, owners, risk boundaries, and success measures.
LTCorps | AWS and Microsoft veteran-led
Enterprise AI Development Consulting
We help leadership teams move from AI ideas and isolated prototypes to secure, cloud-native systems that can run inside real business operations.
- Cloud depth AWS, Microsoft, and production platform architecture
- AI engineering LLM apps, agents, data workflows, and product delivery
- Enterprise lens Security, governance, cost controls, and adoption planning
Built for organizations that need AI to survive procurement, security review, budget scrutiny, and production load.
AI product strategy
Cloud-native delivery
Responsible automation
Executive clarity
The gap LTCorps closes
Most AI efforts fail between the demo and the business process.
The hard part is not prompting a model. The hard part is connecting models to data, systems, approvals, users, security controls, and measurable outcomes without creating a fragile science project.
Prototype architecture
Early demos skip identity, observability, data quality, evaluation, and failure modes.
Cloud cost drift
Teams underestimate model, retrieval, storage, orchestration, and support costs at scale.
Adoption friction
Users reject AI when it is bolted onto work instead of embedded into the way decisions happen.
Capabilities
AI systems designed for production, not theater.
LTCorps combines cloud architecture, AI product engineering, and operating discipline so your teams can build systems people trust and use.
AI Opportunity Architecture
Prioritize the use cases that deserve investment, define workflow owners, and turn AI ambition into a practical delivery roadmap.
LLM Product Engineering
Build internal copilots, customer-facing assistants, retrieval systems, model evaluation loops, and human-in-the-loop controls.
Agentic Workflow Automation
Design agents that coordinate tools, APIs, approvals, and data safely instead of running unmanaged automation in the background.
Cloud and Data Modernization
Prepare the platform layer for AI with secure data access, cloud-native services, scalable integration patterns, and observability.
AI Governance and Security
Embed policy, privacy, auditability, model risk checks, identity boundaries, and cost guardrails into the delivery process.
Executive and Team Enablement
Equip leaders and delivery teams with decision frameworks, technical standards, and operating rituals for sustained AI execution.
Veteran-led delivery
Seasoned perspective from AWS and Microsoft environments.
LTCorps brings the practical judgment that comes from building around cloud platforms, enterprise constraints, and real operating teams. The work is not about chasing every new model release. It is about choosing the right architecture, then shipping the system responsibly.
- Architecture decisions that account for security, cost, scale, and maintainability.
- AI delivery plans that executives, product teams, engineers, and operators can all understand.
- Implementation support that transfers capability instead of creating permanent dependency.
Delivery method
A disciplined path from strategy to shipped capability.
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01
Frame
Clarify the business workflow, stakeholders, decision rights, risk posture, and measurable outcome.
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02
Blueprint
Define the solution architecture, data flow, model approach, guardrails, budget assumptions, and release plan.
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03
Build
Develop the AI product, integrations, evaluation harness, cloud infrastructure, and operational controls.
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04
Harden
Test quality, latency, privacy, security, cost, fallback behavior, and user acceptance before launch.
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05
Transfer
Document the system, train the owning team, and establish the operating rhythm for continuous improvement.
Technical fluency
The work spans AI, cloud, data, product, and governance.
Next-generation AI websites talk about transformation. Production AI teams need someone who can translate transformation into backlog, architecture, controls, budgets, and adoption.
AI applications
Copilots, assistants, RAG, agents, workflow automation, evaluation, and prompt operations.
Cloud platforms
AWS architecture, Microsoft ecosystem alignment, containers, serverless, identity, and infrastructure automation.
Data foundations
Knowledge retrieval, secure data access, pipelines, metadata, permissions, and operational observability.
Enterprise controls
Governance, compliance readiness, privacy, model risk, audit trails, cost management, and change management.
Operating standard
Every build should answer the questions enterprise buyers actually ask.
What data does the model use, and who approved that access?
How do we know the output is useful, traceable, and safe enough?
What happens when the model is wrong, slow, expensive, or unavailable?
How will this change the workflow for the people expected to use it?
Engagement models
Choose the right level of support for the decision in front of you.
AI Strategy Sprint
For leaders who need a grounded AI roadmap before funding a build.
- Use case inventory and prioritization
- Architecture and risk review
- Executive implementation roadmap
Production Build Pod
For teams ready to build an AI product, automation layer, or internal platform capability.
- Solution design and engineering delivery
- Cloud, data, and integration implementation
- Evaluation, launch, and operating handoff
Architecture Advisory
For executives or technical leaders who need senior guidance around AI and cloud decisions.
- Design reviews and risk calls
- Vendor and platform decision support
- Team enablement and delivery governance
Start the conversation
Bring LTCorps into the room before the AI roadmap becomes expensive guesswork.
Share the business workflow, AI idea, cloud constraint, or executive question you are working through. We will respond with a practical next step.
info@ltcorps.com
United States based | Remote and executive advisory