Most AI fails at the point of integration. This is where it gets fixed.
Integrating AI into the systems your business depends on - the hard part, done right.
Pilots die the moment they have to touch the systems you run on, handle real volume, and be trusted with decisions that matter. That is the part we do.
Production AI is not model development.
Getting AI to work once is easy.
Making it work reliably inside your systems is the problem.
- Integration with operational systems
- Human oversight and decision control
- Security and compliance requirements
- Monitoring and operational debugging
If these pieces are missing, pilots succeed and production fails.
We have built and run systems at this scale for two decades - See the systems we have built.
Redesigned decision flow
One system, three layers, held to a number.
Production AI is not one capability. It is workflows, models, and data foundations engineered as one system, and held to their committed outcomes by Outcome Assurance.
Operational Workflows
IntelligenceThe redesigned workflows where impact is created - executed, orchestrated, and routed with human judgment gates engineered into the flow of work.
AI Decision and Automation Layer
ModelsThe model stack matched to each task - foundational LLMs, domain-specific and modality-centric models, and classical ML, monitored and evaluated in production.
Data and Systems Foundation
ContextThe domain context AI reasons over - core systems, pipelines, retrieval, and knowledge graphs integrated with the systems your business depends on.
Outcome Assurance
HarnessThe harness spanning every layer - engineered into the build and carried into operation, so committed outcomes stay on track in production, not just at launch.
How Outcome Assurance worksPlatform and deployment stack.
Platform-agnostic by design: from governed Azure deployments to full Claude-native applications, the stack follows the workflow, not a vendor preference.
AI Orchestration
- Azure AI Foundry
- Claude Agent SDK
- Semantic Kernel
- LangChain
Model Providers
- Anthropic Claude
- Azure OpenAI
- Vertex AI
Cloud Infrastructure
- Microsoft Azure
- Google Cloud
- Amazon Web Services
- Multi-cloud
Integration
- MCP
- REST / GraphQL APIs
- Azure Integration
- MuleSoft
Data and Retrieval
- Azure AI Search
- Vertex AI Search
- PostgreSQL pgvector
Observability
- Azure Monitor
- Datadog
- Custom audit pipelines
The quality of your technical team was outstanding. Architech clearly showed a 5-star performance.
Engineering spans every stage of the Work Redesign System
Engineering is not a phase. It is embedded across every stage from the start.
When to bring in engineering
- The workflow is chosen and a Transformation Blueprint is in place or underway
- A prototype exists and has to reach real volume inside your systems
- Something is in production and unstable
When to start with Jumpstart instead
- You are still identifying where AI applies
- You have multiple competing ideas
- There is no executive alignment on priorities
- You are exploring tools, not workflows
In these cases, start with AI Jumpstart.
What buyers ask before engaging on production AI
It means the workflow runs against real operational load, integrates with your identity and access controls, meets your security posture, exposes audit traceability, and is monitored against a business KPI, not just model behaviour. It also means human-in-the-loop controls are engineered into the workflow, not bolted on.
Platform-agnostic by design: from governed Azure deployments to full Claude-native applications, the stack follows the workflow, not a vendor preference. Platform choice is driven by your existing data residency, identity, and security posture, not by a preferred vendor relationship.
Every workflow defines which decisions AI executes, which decisions require human judgment, and which decisions require human approval before execution. Those checkpoints are wired into the workflow itself, tied to role-based access, and logged for audit. This is a design decision made during redesign, not a runtime configuration.
You do. Architech builds in your cloud tenancy, in your repository, using your identity provider. No lock-in to a proprietary platform. When the engagement ends, your team can operate, extend, and modify the workflow with or without us.
For teams ready to move to production.
Bring us the system you need to ship.
A focused technical discussion to design, validate, or speed up a production AI system. It is aimed at systems headed for real use, not early prototypes.
Measured live in your operation: 35% faster resolution, documents and approvals completed 60% faster, 3x faster information retrieval. The first workflow typically reaches production in 5 to 7 weeks.
Not sure where to start?
Request an AI Jumpstart