Amazon Bedrock
AI automation that runs in your AWS account
Document processing, data extraction, knowledge retrieval and workflow automation — built on Bedrock, on your account. Chosen models, human review for low-confidence results, and data that is not used to train foundation models.
How it is built
- Platform
- Amazon Bedrock
- Models
- Claude, Llama, Mistral and others, chosen per task
- Where it runs
- Your own AWS account
- Your data
- Never used to train models. Encrypted, audited, UK residency
Organisations we have worked with
Delivery
How an AI project runs
We start small and prove the automation on your real documents before building it out.
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1
Identify the process
Which manual task, how much of it, what a good result looks like and what it costs today.
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2
Prototype
A working prototype on your real data in days, using patterns we have already shipped.
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3
Production build
Error handling, review steps for low-confidence results, monitoring and cost controls.
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4
Run and improve
Deployed in your account, monitored, and tuned as the volume and the documents change.
Scope
What we build
Document processing and extraction
Extract, classify and process information from invoices, contracts, applications and forms.
- Invoice and receipt processing
- Contract analysis and extraction
- Application form processing
- Compliance document review
AI agents and assistants
Purpose-built agents for specific business tasks.
- Customer support automation
- Internal knowledge assistants
- Sales and lead qualification
- Technical support triage
Intelligent workflows
Process automation that handles decisions, routing and actions that used to need a person.
- Email triage and routing
- Content generation pipelines
- Approval workflow automation
- Data enrichment and validation
RAG and knowledge systems
AI grounded in your documents, policies and knowledge base so answers are accurate and sourced.
- Document Q&A systems
- Policy and compliance lookup
- Product information retrieval
- Training and onboarding assistants
Data analysis and summarisation
Analyse large datasets, generate reports and surface what would take a team days to find.
- Automated report generation
- Meeting and call summarisation
- Competitor and market analysis
- Sentiment and feedback analysis
System integration
AI connected into the tools you already use.
- CRM and helpdesk integration
- Slack and Teams bots
- API-driven AI services
- Legacy system augmentation
Why us
Production systems, not demos
Built for production
Real data, real users and the edge cases a demo never meets.
Your account, your data
Everything runs in your AWS account with IAM controls, encryption and audit logging.
Cost-conscious
The right model for each task, and caching where it saves money.
Certified AWS Partner
Solutions Architect Professional and DevOps Professional certified. AWS is all we do.
"Cloudavian has been integral to our augmented team and product strategy. Mark and his team are trusted partners."
What we build with: Amazon Bedrock with Claude, Llama and Mistral, Bedrock Agents and Knowledge Bases, Textract and Comprehend, Step Functions, Lambda and EventBridge, OpenSearch, S3, DynamoDB and PostgreSQL with pgvector, CDK and CloudWatch.
Questions
Common questions
Your data stays in your AWS account
- All processing runs within your own AWS account
- We never use your data to train foundation models
- Full IAM access controls and audit logging
- Data encrypted in transit and at rest
- Compliant with GDPR and UK data residency requirements
- You retain full ownership of everything we build
Does our data leave our AWS account?
No. Processing runs inside your account through Bedrock. Your data is not used to train foundation models and stays under your IAM controls.
Which model will you use?
Whichever fits the task. Bedrock gives access to Claude, Llama, Mistral and others through one API, so the choice can change without rebuilding the system.
What happens when the AI gets something wrong?
We design for it. Low-confidence results are routed to a person for review, and every decision is logged so it can be checked.
How quickly can we see something working?
A prototype on your real documents typically takes days, not months. That is the point at which you decide whether to build it out.