Sold by: LiminalArc
LiminalArc delivers end-to-end implementation of custom generative AI solutions on AWS -- Amazon Bedrock and SageMaker architected with domain-driven design thinking that aligns models with your business workflows.
Overview
From generative AI experiment to embedded capability
Most enterprises don't struggle to start with generative AI -- they struggle to get it out of the lab. Pilots impress, then stall, because models were never aligned to the domains, data, and workflows the business actually runs on. LiminalArc's AI Implementation service closes that gap: end-to-end deployment of custom AI solutions using AWS-native tools, structured through domain-driven design so every model serves a specific business domain and workflow.
What we deliver
Each engagement covers the full path to production: domain discovery that maps AI capabilities to your business domains; prompt engineering and evaluation; retrieval-augmented generation (RAG) pipelines that ground models in your own knowledge; fine-tuning of foundation models where retrieval alone isn't enough; and deployment of secure, scalable inference endpoints with guardrails, monitoring, and cost controls built in. We build on Amazon Bedrock for foundation models, agents, and knowledge bases as well as Amazon SageMaker and SageMaker JumpStart for training, fine-tuning, and hosting
Outcome-focused by design
Every technical component is tied to a measurable business outcome -- accelerated content creation, enhanced customer engagement, automated decision intelligence, reduced handling time -- defined at the start and measured at handover. This is how generative AI becomes a strategic asset embedded in core operations rather than a siloed experiment.
How the engagement runs
A short discovery identifies the highest-value use case and its domain boundaries. A working pilot proves the outcome on your data. Production build-out hardens security, scale, and observability. A structured handover enables your team to own and extend the platform. Scope, timeline, and deliverables are agreed per project and transacted through an AWS Marketplace private offer.
AWS services used: Amazon Bedrock, Amazon SageMaker (including SageMaker JumpStart), Amazon OpenSearch Service, AWS Lambda, Amazon API Gateway, AWS Step Functions, Amazon S3, Amazon DynamoDB, AWS IAM, AWS KMS, and Amazon CloudWatch.
Highlights
- Outcome-focused delivery: every technical component -- prompts, RAG pipelines, fine-tuned models, inference endpoints -- is tied to a measurable business result such as accelerated content creation, enhanced customer engagement, or automated decision intelligence.
- AWS-native and domain-driven: built on Amazon Bedrock and SageMaker JumpStart structured through domain-driven design so models align with your specific business domains and workflows rather than generic use cases.
- Production from day one: secure, scalable inference endpoints with guardrails, evaluation, and observability -- generative AI embedded in your core operations as a strategic asset, not a siloed experiment.
Details
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Delivery method
Deployed on AWS
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Pricing
Custom pricing options
Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.
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Support
Vendor support
LiminalArc provides direct support for this offering by email at stacy.gordon@liminalarc.co .
Pre-sales questions receive a response within one business day.
During an engagement, buyers receive a named engagement lead, weekly delivery reviews, and priority email support with same-business-day response. Post-implementation support and ongoing optimization options -- including model evaluation, prompt and pipeline tuning, and cost reviews -- are defined in each statement of work and private offer.
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