AWS Generative AI Practice
Build production-ready generative AI on AWS.
From GenAI readiness and architecture to RAG, AI agents, model integration, security, observability and production deployment. We design, secure and operationalise generative AI workloads on AWS.
- AWS Advanced Tier Services Partner
- ISO 27001 certified
- Amazon Bedrock
- Amazon SageMaker
- AWS Lambda
- Amazon OpenSearch Service
- Amazon S3
- AWS IAM
The problem
Your AI POC worked. Now what?
Buyers recognise these before they care about architecture. If several sound familiar, the prototype is not the hard part.
Our AWS GenAI practice moves AI from experimentation to secure, measurable production systems.
- AI responses are inaccurate or hallucinate
- Models cannot securely access company data
- Token usage and inference costs are unpredictable
- AI applications have high latency
- Nobody knows which model to choose
- Sensitive or PII data may reach an LLM
- Security teams cannot audit prompts and responses
- Prototypes do not scale into production
- There is no monitoring or governance
- Teams are experimenting independently
- Self-hosted models are operationally expensive
- AI agents have excessive permissions
What the practice delivers
Six services that cover the path from first use case to a governed production platform.
GenAI Readiness Assessment
Business use-case discovery, data readiness, architecture assessment, model selection, security review, cost estimation and a roadmap.
Generative AI Application Development
Internal copilots, assistants, summarisation, search, content generation and domain-specific AI applications.
Enterprise RAG
Secure AI search over documents, databases, knowledge bases and enterprise systems, with citations and access control.
AI Agents and Automation
Agents that reason, call APIs, run workflows and integrate with enterprise systems, inside permission boundaries.
Model Evaluation and Optimisation
Model comparison, latency testing, token optimisation, prompt engineering and quality evaluation against your own data.
AI Security and Governance
Guardrails, IAM, PII protection, prompt security, model access controls, logging and governance.
Solutions we build
Specific systems, each with a data source, a model and a workflow around it.
Enterprise AI Assistant
Ask questions across internal documents, policies, manuals, tickets and company knowledge.
AI Customer Support
Use customer history and knowledge bases to generate contextual responses and automate support workflows.
Document Intelligence
Extract, classify, summarise and reason over contracts, invoices, reports and large document collections.
AI DevOps Assistant
Analyse logs, incidents, infrastructure and operational data to help engineers troubleshoot systems.
Security Copilot
Investigate security findings, explain vulnerabilities and recommend remediation.
AI Sales Assistant
Research accounts, summarise conversations, generate proposals and prepare teams for meetings.
AI Knowledge Search
Replace keyword search with natural-language enterprise search over everything you already store.
AI Agents
Understand a request, reason, query company data, call tools, request approval, execute and validate the outcome.
How an agent request runs
Reasoning is only useful when it can act. Every action passes an approval gate and is validated afterwards.
- 1Understand request
- 2Reason
- 3Query company data
- 4Call APIs and tools
- 5Request approval
- 6Execute action
- 7Validate outcome
The AWS GenAI stack
Amazon Bedrock gives access to foundation models without operating model infrastructure. Around it, a reference architecture we adapt per workload.
Foundation models on Bedrock
- Anthropic Claude
- Amazon Nova
- Meta Llama
- Mistral
- Cohere
- Other Bedrock-supported models
We recommend models from evaluation on your data, and Bedrock keeps the choice reversible as newer models arrive.
- Users
- Web
- Mobile
- Slack / Teams
- Internal tools
- Edge and API
- Amazon CloudFront
- Amazon API Gateway
- AWS WAF
- Application
- AWS Lambda
- Amazon ECS
- Amazon EKS
- Foundation models
- Amazon Bedrock
- Amazon SageMaker
- Knowledge, data and tools
- Knowledge Bases / RAG on OpenSearch
- Enterprise data on S3, RDS, DynamoDB
- Tools and APIs via Lambda
- Guardrails
- Bedrock Guardrails
- PII redaction
- Prompt security
- Observability and governance
- Amazon CloudWatch
- AWS CloudTrail
- Model invocation logging
Enterprise RAG
Turn enterprise knowledge into an AI-powered knowledge system.
Retrieval-augmented generation grounds answers in your documents and data, with the source attached to every answer.
- Citations and source attribution
- Access control and document permissions
- Metadata filtering
- Hybrid keyword and vector search
- Conversation history
- Document ingestion pipelines
- Evaluation datasets and scoring
Pipeline
- 1Documents, PDFs, SharePoint, Confluence, S3, databases, APIs
- 2Ingestion
- 3Chunking and embeddings
- 4Vector search
- 5Relevant context
- 6Foundation model
- 7Grounded answer with citations
AI agents
Move beyond chatbots with AI agents.
Agents connect reasoning with enterprise tools. The playbooks below are the shape of agents we have built for operations and security, with a human approving every consequential step.
IT Operations Agent
- 1Incident detected
- 2Analyse CloudWatch
- 3Identify likely issue
- 4Recommend fix
- 5Request approval
- 6Execute Lambda or runbook
- 7Validate
Security Agent
- 1Security finding
- 2Investigate resource
- 3Assess severity
- 4Find remediation
- 5Generate fix
- 6Approval
- 7Remediate and collect evidence
Finance Agent
- 1Invoice received
- 2Extract information
- 3Verify data
- 4Query ERP
- 5Flag discrepancy
- 6Route for approval
GenAI security
Enterprise AI without compromising enterprise security.
The same team that runs penetration testing and DevSecOps designs the controls, so they are in the architecture rather than bolted on.
- AWS IAM-based access controls
- Model access policies
- Private networking with VPC endpoints
- Encryption at rest and in transit
- Secrets management
- Prompt injection protection
- PII detection and redaction
- Data leakage prevention
- AI guardrails
- Audit logging
- Model interaction logging
- Human approval workflows
- Agent permission boundaries
- Responsible AI controls
AI Security Assessment
A fixed-scope review of an existing AI application, tested against the OWASP LLM Top 10.
- 1AI application
- 2Prompt security
- 3Data exposure
- 4Model access
- 5Agent permissions
- 6API security
- 7Logging and governance
How an engagement runs
Five phases. Most clients enter at Discover or Production, depending on where their prototype stands.
- 1
Discover
- Identify the business problem
- Prioritise GenAI use cases
- Analyse the data you have
- Define success metrics
- 2
Assess
- AI readiness
- AWS architecture
- Model evaluation
- Data security
- Cost estimation
- 3
Proof of concept
- Use case
- Data
- Model
- Prototype
- Evaluation
- 4
Production
- Scalable AWS architecture
- IAM
- CI/CD
- Monitoring
- Security
- Resilience
- 5
Optimise
- Model quality
- Latency
- Token consumption
- Cost
- User feedback
- Evaluation datasets
GenAI accelerators
Named, fixed-scope offerings so the first engagement has a clear start and finish.
2-week readiness assessment
GenAI Launchpad
A fixed-scope assessment that ends with a decision, not a deck.
- Use-case shortlist
- AWS architecture
- Model recommendation
- Security assessment
- Cost model
- Implementation roadmap
Production-ready RAG foundation
Enterprise RAG Accelerator
The retrieval stack most enterprise assistants need, built once and reused across teams.
- Ingestion pipelines
- Embeddings
- Retrieval with access control
- Amazon Bedrock integration
- Security and guardrails
- Citations
- Observability
An agent wired into your systems
AI Agent Accelerator
One agent, integrated with the tools your teams already use, with approval gates from day one.
- APIs
- Slack or Teams
- Jira
- Databases
- AWS services
- Internal tools
For self-hosted and third-party model estates
Bedrock Migration Assessment
For organisations running Ollama, self-hosted Llama, the OpenAI API, Azure OpenAI or custom GPU inference and weighing managed models.
- Current-state review
- Latency and cost comparison
- Model equivalence testing
- Target Bedrock architecture
- Migration plan
Architecture examples
Three reference designs we start from. Pick one to see the layers.
Enterprise RAG on AWS
Every layer is built as code and can be handed over or run by us as a managed service.
Walk through this with an architect- 1S3 / SharePoint / Confluence sources
- 2Ingestion on Lambda
- 3Embeddings via Bedrock
- 4OpenSearch vector index
- 5Retrieval with permission filters
- 6Bedrock generation with Guardrails
- 7Answer with citations
GenAI case studies
Published work on Amazon Bedrock, plus an engagement we can describe without naming the client.
- Bagmane Group
AI-powered smart building energy optimization
Problem
Energy telemetry across multi-tenant business parks was fragmented, anomalies surfaced only on utility bills and peak demand was hard to anticipate.
Approach
- Ingested smart-meter and sensor telemetry through AWS IoT Core into Timestream and S3
- Trained SageMaker models for consumption forecasting and per-building anomaly detection
- Used Amazon Bedrock to turn forecasts and anomalies into plain-language recommendations
Outcome
Near real-time visibility across the portfolio, earlier detection of abnormal usage and explainable recommendations facility teams can act on.
Read the full case study - Dedups.ai
AI-powered autonomous cloud security operations
Problem
High volumes of security findings needed manual correlation, prioritisation and remediation prep, leaving misconfigurations exposed longer than acceptable.
Approach
- Routed Security Hub, GuardDuty, Inspector and Config findings through EventBridge into Step Functions workflows
- Used Amazon Bedrock to explain root cause, assess impact and generate standardised remediation
- Added human approval gates and post-remediation verification, with evidence stored in S3
Outcome
Faster response to high-risk issues, consistent remediation and a complete audit trail, with humans in control of every consequential action.
Read the full case study - Client name withheld
Moving self-hosted GenAI workloads to Amazon Bedrock
Problem
The customer's self-hosted LLM environment on Ollama experienced latency and model response timeouts as usage grew.
Approach
- Evaluated GenAI readiness and reviewed the existing architecture
- Evaluated managed foundation models on Amazon Bedrock against the self-hosted models
- Designed an AWS architecture using Amazon Bedrock, with AWS Lambda for application workflows
- Implemented CloudWatch monitoring for latency, errors and token usage
Outcome
A production-oriented architecture and POC roadmap for migrating the workload to managed AWS GenAI services.
Industries
Where GenAI on AWS is paying off for the companies we work with.
SaaS
- In-product copilots
- Support automation
- Usage-aware cost controls
FinTech
- Document and KYC intelligence
- Compliance evidence
- Analyst assistants
Healthcare
- Report summarisation
- Patient-facing assistants with PII controls
- Clinical knowledge search
Retail
- Product discovery
- Recommendation assistants
- Catalogue enrichment
Cybersecurity
- Security investigation agents
- Vulnerability remediation
- Security copilots
IT Operations
- Incident triage
- Runbook automation
- Log and metric analysis
Professional Services
- Proposal drafting
- Knowledge management
- Engagement summaries
Economics
Control the economics of GenAI.
AI cost is no longer only infrastructure. Tokens are infrastructure. We measure them from the first prototype and work every lever.
- Model selection
- Input tokens
- Output tokens
- Context size
- Prompt caching
- RAG retrieval
- Model routing
- Inference architecture
- Serverless infrastructure
- Observability
Model evaluation
Choosing the right model.
We do not recommend one model by default. Each candidate is scored on your prompts and data across these dimensions.
| Dimension | Measurement |
|---|---|
| Accuracy | Response quality on your evaluation set |
| Grounding | Factual consistency with retrieved context |
| Latency | Time to first token and full response |
| Context | Required context window |
| Cost | Cost per request at expected volume |
| Security | Data residency and handling requirements |
| Tool use | Reliability of function and API calling |
| Structured output | JSON and schema adherence |
Cloud, AI and security under one team
- AWS Advanced Tier Services Partner
- ISO 27001 certified
AWS-native architecture
Serverless-first designs on Bedrock, Lambda, Step Functions and OpenSearch, built as code.
Production-focused engineering
IAM, CI/CD, monitoring and cost controls are part of the build, not a later phase.
Security-first GenAI
The same team runs VAPT and DevSecOps, so guardrails and permission boundaries are designed in.
Infrastructure plus AI
We already operate the AWS estates these workloads run on, 24x7.
Automation and agent engineering
Approval-gated agents for operations and security are systems we have shipped.
Cost-conscious by default
Token, retrieval and inference economics are measured from the first prototype.
Where to start
Four engagement models, matched to the sentence you would say on the first call.
GenAI Readiness Assessment
“We want AI but do not know where to start.”
Start hereGenAI Proof of Concept
“We know the use case and want to validate it.”
Start hereProduction Implementation
“Our POC works. Help us productionise it.”
Start hereGenAI Managed Engineering
“We need an AI engineering team without hiring one.”
Start here
Questions buyers ask
Do we have to use Amazon Bedrock?
No. Bedrock is our default because it gives access to several model families without running model infrastructure, keeps data inside your AWS account and integrates with IAM, KMS and CloudTrail. Where a workload needs a custom or fine-tuned model we use Amazon SageMaker, and we will say so when self-hosting is genuinely the better fit.
How do you stop the model from seeing data it should not?
Retrieval is filtered by the user's permissions before anything reaches the model, PII is detected and redacted where required, and models are reached over private networking. Every prompt and response is logged so security teams can audit what was sent. Agents run under least-privilege roles with approval gates for consequential actions.
What does a GenAI readiness assessment produce?
A shortlist of use cases ranked by value and feasibility, a target AWS architecture, a model recommendation backed by evaluation on your data, a security assessment, a cost model at expected volume and an implementation roadmap. It takes about two weeks.
How do you control GenAI cost?
We measure tokens per request from the first prototype, then work the levers: model selection and routing, prompt and context size, caching, retrieval precision and serverless inference architecture. Costs are tagged and visible in the same FinOps reporting we run for the rest of your AWS spend.
Can you migrate us from OpenAI, Azure OpenAI or a self-hosted model?
Yes. The Bedrock Migration Assessment compares your current models against Bedrock options on accuracy, latency and cost using your own prompts, then produces a target architecture and migration plan. We have done this for a self-hosted Ollama estate that was hitting latency and timeout limits.
Which industries and regions do you work in?
Most of our GenAI work is with SaaS, fintech, healthcare, retail and cybersecurity companies in India and abroad. Workloads can be kept in Indian AWS regions with residency controls, and we document any service that is only available elsewhere so you can decide.
Have a GenAI use case in mind?
Speak with an AWS GenAI architect and we will help determine the architecture, model, security requirements and estimated AWS cost. No sales deck. Bring your use case and architecture questions.