AI-ready application engineering
Your applications work, but they are not built to support AI-driven workflows or data-intensive use cases. Re-architect systems so they can integrate AI, handle data reliably, and evolve without friction.
What we enable
AI-ready application engineering makes systems easier to integrate with AI, handle data at scale, and support continuous change.
What we enable
AI-ready application engineering makes systems easier to integrate with AI, handle data at scale, and support continuous change.
01
Integrate AI without rework
Enable applications to connect with AI models and services without redesigning systems each time.
02
Handle data reliably across systems
Ensure consistent data flow between applications, pipelines, and AI layers.
03
Support evolving use cases
Make it easier to extend applications as new requirements and workflows emerge.
04
Reduce dependency on rigid architectures
Move away from tightly coupled systems that limit flexibility and speed.
Built across financial and regulated environments
Alternative asset management
Specialty lending
Wealth management
PE-backed platforms
Experience with clients backed by
Built across financial and regulated environments
Experience with clients backed by
What we build
We restructure applications so they can support AI integration, data flow, and continuous evolution.
What we build
We restructure applications so they can support AI integration, data flow, and continuous evolution.
Decouple application layers
Separate data, logic, and interfaces for flexibility and scalability.
Standardize APIs and integrations
Enable consistent communication across systems and AI services.
Refactor critical components
Update parts of the system that block performance, scalability, or AI integration.
How the system works
We improve how applications are structured so they can support AI-driven workflows and data-intensive operations.
Assess current architecture
Identify limitations in how systems handle data, integrations, and change.
Restructure core layers
Update how data, services, and interfaces are organized.
Enable scalable data flow
Ensure applications can handle high-volume and real-time data reliably.
Prepare for AI integration
Make systems ready to support AI models and evolving use cases.
Assess current architecture
Identify limitations in how systems handle data, integrations, and change.
Restructure core layers
Update how data, services, and interfaces are organized.
Enable scalable data flow
Ensure applications can handle high-volume and real-time data reliably.
Prepare for AI integration
Make systems ready to support AI models and evolving use cases.
Assess current architecture
Identify limitations in how systems handle data, integrations, and change.
Restructure core layers
Update how data, services, and interfaces are organized.
Enable scalable data flow
Ensure applications can handle high-volume and real-time data reliably.
Prepare for AI integration
Make systems ready to support AI models and evolving use cases.
Assess current architecture
Identify limitations in how systems handle data, integrations, and change.
Restructure core layers
Update how data, services, and interfaces are organized.
Enable scalable data flow
Ensure applications can handle high-volume and real-time data reliably.
Prepare for AI integration
Make systems ready to support AI models and evolving use cases.
Building Data-First AI in Production for regulated and data-intensive industries?
Assess your AI readinessHow we engage
We start with a focused discussion around one application or system that is limiting your ability to adopt AI or scale.
How we engage
We start with a focused discussion around one application or system that is limiting your ability to adopt AI or scale.
Identify where this fits
We assess which parts need restructuring to support AI and data workflows.
Define a clear scope
We focus on specific layers or components without disrupting everything.
Work as an extension of your team
We collaborate closely to ensure changes work in practice.
Start with a focused conversation
We review your application architecture and identify constraints.
Identify where this fits
We assess which parts need restructuring to support AI and data workflows.
Define a clear scope
We focus on specific layers or components without disrupting everything.
Work as an extension of your team
We collaborate closely to ensure changes work in practice.
Start with a focused conversation
We review your application architecture and identify constraints.
Identify where this fits
We assess which parts need restructuring to support AI and data workflows.
Define a clear scope
We focus on specific layers or components without disrupting everything.
PARTNER + CERTIFICATE
Recognized by Platform Leaders. Trusted in Production.
PARTNER + CERTIFICATE
Recognized by Platform Leaders. Trusted in Production.
Frequently asked questions
AI-ready application engineering builds software architectures designed to seamlessly integrate, deploy, and scale AI capabilities including machine learning models, large language models, generative AI, and intelligent automation. Services include API-first design, microservices architecture, vector databases, model deployment pipelines, prompt engineering, and infrastructure enabling AI-powered features.
AI-ready architecture enables rapid AI feature deployment, supports model versioning and experimentation, handles compute-intensive workloads, manages vector embeddings efficiently, provides real-time inference capabilities, ensures scalability, maintains performance under AI loads, and future-proofs applications for evolving AI technologies and business requirements.
Components include API-first architecture, microservices for modularity, vector databases (Pinecone, Weaviate), model serving infrastructure, MLOps pipelines, prompt management systems, caching layers, async processing, scalable compute resources, monitoring and observability, security frameworks, and integration patterns supporting diverse AI models and providers.
Services include architecture assessment and redesign, API development for AI integration, vector database implementation, model deployment pipelines, prompt engineering frameworks, RAG system development, fine-tuning infrastructure, performance optimization, security implementation, monitoring setup, and ongoing support ensuring robust AI-enabled application capabilities.
Timelines depend on application complexity. Greenfield AI-native applications take four to seven months. Existing application modernization for AI-readiness requires five to nine months. Enterprise platform transformation with multiple applications, shared AI infrastructure, governance, and security frameworks spans nine to eighteen months.
Costs vary by application scope. Simple AI integration for single applications starts at $80,000 to $200,000. Mid-complexity application modernization ranges from $250,000 to $600,000. Enterprise AI-ready platform engineering with multiple applications, shared infrastructure, MLOps, and governance frameworks exceeds $1 million for comprehensive implementation.
Turn bottlenecks into running systems
Pick a process where work is slowing down. We’ll help you turn it into a system that runs with minimal manual effort.
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