Generative AI Solutions & R&D Acceleration

Commit AI helps startups and enterprises design, build, and adopt Generative AI solutions on AWS, from use case discovery and proof of concept to production deployment.

Our services cover end-to-end Generative AI solutions, R&D productivity acceleration, and specialist team augmentation. Each engagement defines objectives, deliverables, and acceptance criteria based on customer requirements.

• Identify and prioritize use cases based on business value and technical feasibility.
• Assess data readiness, integration needs, security, and operational requirements.
• Define solution architecture, implementation roadmaps, and success metrics.
• Evaluate foundation models against quality, latency, cost, and workload requirements.
• Develop proofs of concept and MVPs with agreed evaluation criteria.

Build AI assistants that connect users with relevant enterprise information through Retrieval-Augmented Generation.

• Implement document ingestion, processing, and retrieval pipelines.
• Build knowledge assistants using Amazon Bedrock and appropriate retrieval services.
• Integrate enterprise documents, knowledge sources, and business applications.
• Apply access-aware retrieval and source attribution.
• Evaluate retrieval relevance, answer quality, and groundedness.

• Develop custom AI agents, assistants, and orchestrated workflows.
• Connect agents to business systems through tools and APIs.
• Implement human approval steps for sensitive actions.
• Deploy agents using Amazon Bedrock AgentCore where appropriate.
• Configure agent identity, access controls, tracing, and monitoring.
• Test tool execution, failure handling, and escalation paths.

• Build applications and APIs around Generative AI capabilities.
• Integrate Amazon Bedrock and, where required, Amazon SageMaker AI.
• Adapt or migrate existing AI applications and model integrations to AWS.
• Implement infrastructure and deployment automation.
• Test performance, scalability, resilience, and integration behavior.
• Prepare production releases, documentation, and operational handover.

Help engineering teams adopt AI across the software development lifecycle.

• Assess development workflows and identify opportunities for AI assistance.
• Evaluate, configure, and roll out developer assistants and coding tools.
• Introduce AI-assisted requirements analysis, coding, testing, documentation, and code review.
• Build engineering knowledge assistants and internal developer tools.
• Integrate AI capabilities into development and CI/CD workflows.
• Establish secure usage guidelines and human review practices.
• Deliver workshops, hands-on training, and adoption support.
• Track agreed indicators such as adoption, cycle time, and review effort.

Extend customer teams with specialists aligned to their technical and delivery needs.

• AI engineers, solution architects, data engineers, and LLMOps specialists.
• Individual specialists or integrated delivery teams.
• Onboarding into customer tools, engineering standards, and workflows.
• Technical guidance through Commit AI's Center of Excellence.
• Knowledge transfer, documentation, and team enablement.

• Implement identity controls, encryption, and appropriate data handling.
• Configure guardrails and content filtering for the use case.
• Test for prompt injection, unintended data exposure, and unsafe tool use.
• Evaluate solution quality using representative datasets.
• Define human oversight and escalation for sensitive outputs and actions.
• Document limitations and operational responsibilities.

• Manage versions of prompts, configurations, and evaluation datasets.
• Automate evaluation and regression testing.
• Monitor quality, errors, latency, token usage, and cost.
• Trace model calls, retrieval steps, and agent actions.
• Collect feedback and prioritize improvements.
• Establish release processes and operational runbooks.

Engagements may include assessments, POCs, MVPs, production projects, R&D enablement, or staff augmentation. Deliverables may include a roadmap, architecture, working application, integration code, evaluation report, deployment automation, and training materials.

Pre-purchase discussions establish requirements and scope. Delivery support, acceptance criteria, support hours, and any post-launch optimization are defined in the statement of work.

AWS infrastructure, model usage, and other AWS service charges are separate from professional services fees and are the customer's responsibility.