Goaltech Enterprise Generative AI Adoption and Implementation on AWS

Organizations want to adopt Generative AI but face challenges such as identifying high ROI use cases, selecting the right foundation models, handling sensitive data securely and deploying solutions at enterprise scale. Goaltech provides a structured AWS native approach that transforms ideas into governed, reliable and production grade GenAI systems.

We combine foundation models from Amazon Bedrock with AWS AI services to build multimodal and multi agent solutions that generate, interpret and enrich text, speech, images and documents. Agentic architectures allow multiple specialized agents such as routing, planning, research and action agents to collaborate on complex tasks. Retrieval Augmented Generation over enterprise knowledge bases ensures answers are grounded in your own data and remain auditable.

WHAT WE DELIVER

AI strategy and use case discovery

-Use case analysis aligned with KPIs such as efficiency, resolution time and cost reduction -Data and content readiness assessment, risk analysis and roadmap creation -Evaluation of Bedrock models and architectures for different domains and languages -Definition of guardrails, governance principles and success metrics

Knowledge and document intelligence with RAG

-Document ingestion pipelines using Textract for scanned and structured content -Vector store and knowledge base design for unstructured and semi structured documents -Retrieval Augmented Generation pipelines with citations, source -links and traceability -Assistants that can search, summarize and interpret contracts, manuals, policies and reports

Multi agent orchestration and knowledge base assistants

-Agent architectures that separate retrieval, reasoning, validation and action responsibilities -Knowledge aware agents that use RAG over structured and unstructured data sources -Policy enforcement and approval workflows integrated into agent behaviors -Detailed logging for agent steps to support debugging, compliance and audit

Enterprise AI assistants and copilots

-Internal assistants for HR, finance, IT, engineering and operations -Role aware guidance using identity integration and fine grained access control -Integration with internal systems for workflow automation and case handling -Configurable guardrails to restrict topics, actions and output formats

Customer and citizen facing conversational experiences

-Voice and chat assistants powered by Bedrock, Transcribe and Polly -RAG over product catalogs, policies and knowledge bases for accurate responses -Escalation paths to human agents with full context transfer -Support for multiple channels including web, mobile and contact centers

Multimodal workflow and decision automation

-Document understanding with Textract feeding RAG knowledge bases -Image and video analysis using Rekognition for safety, compliance or quality workflows -Speech to text pipelines with Transcribe for call or meeting analytics -Combined signals processed by Bedrock and multi agent orchestration for decisions

Generative AI for software and data teams

-Integration of Amazon Q Developer and tools like Kiro into developer workflows -Code suggestions, documentation generation and SQL assistance -GenAI enhanced data exploration and analytics workflows -Secure access to internal code and knowledge bases where appropriate

Responsible, secure and governed AI

-Guardrails, moderation and policy enforcement integrated into GenAI flows -Data isolation, encryption and access control aligning with governance requirements -Evaluation frameworks for quality, safety, bias and hallucination management -Audit trails, logging and review workflows for sensitive decisions

OUR ENGAGEMENT MODEL

1. Strategy and discovery We identify high value use cases, assess data readiness, model options, risks and governance needs.

2. Architecture and model evaluation We evaluate Bedrock models, retrieval strategies, vector store choices and multi agent patterns.

3. Proof of value We build a functional proof of value such as a document RAG assistant or voice agent and measure impact.

4. Production rollout We harden identity, security, monitoring, logging, multi agent orchestration and operational readiness.

5. Optimization and expansion We tune cost and quality, extend assistants to new domains and continuously improve guardrails and governance.