Sold by: Perimattic ManageStacks
Perimattic delivers production-ready generative AI solutions - from LLM fine-tuning to RAG and AI agents - with domain-specific models deployed in 2-4 weeks on AWS.
Overview
Perimattic provides end-to-end LLM Fine-Tuning and Generative AI consulting services for organizations building custom AI applications on AWS. We help enterprises, SaaS companies, and startups customize foundation models, improve model accuracy, and deploy secure AI systems aligned with business goals - delivering domain-specific models in as few as 2-4 weeks.
Why Perimattic
Our engineers specialize in Amazon Bedrock, Amazon SageMaker, AWS Lambda, Amazon ECS, and modern open-source AI frameworks. We fine-tune models including GPT-4o, Llama 3, and other foundation models to your proprietary data, turning raw data into actionable insights with solutions designed for production environments.
Our Services LLM Fine-Tuning - Adapt foundation models to your domain with iterative training and human evaluation loops Retrieval-Augmented Generation (RAG) - Build pipelines that reduce hallucinations and enhance retrieval quality AI Agent Development - Create autonomous agents that integrate with your existing workflows Prompt Engineering and Optimization - Maximize model performance without retraining Vector Database and Knowledge Base Integration - Connect models to enterprise data sources AI Observability and MLOps - Monitor, evaluate, and continuously improve deployed models Enterprise AI Integration - Embed generative AI into existing applications and infrastructure AWS AI Architecture Consulting - Design scalable, secure architectures on AWS
Example Use Case
A SaaS company needing an internal knowledge assistant can engage Perimattic to build a RAG pipeline connecting Amazon Bedrock with their proprietary documentation stored in a vector database. The solution integrates with existing applications via AWS Lambda and Amazon ECS, delivering accurate, context-aware responses while keeping all data within the customer's VPC.
Engagement Process
Phase 1: Discovery (Week 1) - We assess your data readiness, define success metrics, and design the target architecture. Deliverable: Architecture design document and project roadmap.
Phase 2: Implementation (Weeks 2-3) - Our team builds, fine-tunes, and iterates on your AI solution with continuous evaluation against defined benchmarks. Deliverable: Trained model or RAG pipeline with evaluation report.
Phase 3: Deployment and Optimization (Week 4) - We deploy to production with monitoring, observability, and optimization. Deliverable: Production-deployed solution with runbooks and IaC templates.
Phase 4: Ongoing Support - Optional managed services for continuous model improvement, monitoring, and scaling.
Prerequisites Active AWS account with appropriate service access Training data or knowledge base content prepared for ingestion Designated product owner or technical point of contact Minimum dataset requirements vary by engagement type
Security and Data Handling
All solutions are built with security-first principles on AWS, leveraging VPC isolation, IAM least-privilege access, and encryption for data at rest and in transit. Customer training data remains within your AWS environment and is never shared or retained after engagement completion.
Get Started
Contact us at sales@perimattic.com to schedule a discovery call and receive a custom architecture assessment for your generative AI project.
Highlights
- Domain-specific LLM models delivered in 2-4 weeks: We fine-tune foundation models including GPT-4o and Llama 3 to your proprietary data using Amazon Bedrock and Amazon SageMaker. Our iterative approach with human evaluation loops ensures models meet accuracy benchmarks before production deployment, reducing hallucinations and improving retrieval quality for your specific use case.
- Production-first AI architecture on AWS: Every solution is built for production from day one with VPC isolation, IAM least-privilege access, encrypted data handling, and full observability. We deliver complete artifacts including IaC templates, runbooks, and monitoring dashboards so your team can operate independently after handoff.
- Structured engagement from discovery to managed support: Our phased process starts with a one-week architecture assessment, moves through implementation and deployment in 2-3 weeks, and includes optional ongoing managed services. You receive defined deliverables at each milestone - architecture documents, evaluation reports, and deployed solutions - with a dedicated engineering team throughout.
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Engagement Process
Perimattic provides consulting, implementation, deployment, and managed support for Generative AI and LLM projects.
Phase 1 - Discovery and Scoping (Week 1): We assess your data readiness, define success metrics, and design the target architecture. You provide AWS account access, training data or knowledge base content, and a designated point of contact. Deliverable: Architecture design document and project roadmap.
Phase 2 - Implementation (Weeks 2-3): Our engineering team builds and iterates on your solution with continuous evaluation. Deliverable: Trained model or RAG pipeline with benchmarking report.
Phase 3 - Deployment (Week 4): Production deployment with monitoring, observability, and optimization. Deliverable: Deployed solution, IaC templates, and operational runbooks.
Phase 4 - Ongoing Support (Optional): Managed services for continuous improvement and scaling.
Buyer Responsibilities Active AWS account with service access Training data or documentation for ingestion Designated product owner for feedback loops
Support Channels Email: sales@perimattic.com Website: https://perimattic.com/ Hours: Monday-Friday (Business Hours), with optional 24x7 enterprise support
Contact us to schedule a discovery call and receive a custom architecture assessment for your project.