AWS
AWS consulting services for automation, data and AI
Our AWS consulting services help operations, data and engineering teams put the AWS account they already pay for to work on automation, data pipelines and generative AI. We build event-driven workflows with Lambda and Step Functions, data pipelines into S3 and Redshift, and Amazon Bedrock applications that answer questions from your own documents. Every project has a fixed price after a free review, and everything is deployed in your account as code your team owns.
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Quick answer
What do AWS consulting services include?
AWS consulting services for automation and AI include building event-driven workflows with Lambda and Step Functions, data pipelines with Glue, S3 and Redshift, and generative AI applications on Amazon Bedrock. The work is deployed in your own AWS account as infrastructure as code, so your team keeps full ownership and control.
- Focus is automation, data pipelines and AI, not hosting or data center migration.
- Amazon Bedrock offers multiple foundation models inside your own AWS environment.
- Everything is deployed as infrastructure as code in your account, reviewed through your change control.
- Fixed quote after a free review, with first workflows typically live in 2-4 weeks.
01
What our AWS consulting services cover
We are not a hosting or migration shop. We do not lift and shift data centers or run your VPCs day to day. Our work sits on top of the infrastructure: the workflows, data movement and AI features that turn AWS from a cost line into something the business uses every day.
Most engagements start with a manual process that touches several systems, such as a nightly export someone reconciles by hand, invoices keyed in from PDFs, or a report assembled from three databases. We rebuild it as an automated, monitored pipeline in your account.
- Serverless automation with AWS Lambda, Step Functions and EventBridge
- Data pipelines with AWS Glue, S3, Athena and Amazon Redshift
- Generative AI with Amazon Bedrock, Knowledge Bases and Bedrock Agents
- Document extraction with Amazon Textract feeding ERP, CRM or a database
- Dashboards in QuickSight, Power BI or Looker Studio on top of AWS data
02
AWS Lambda automation and Step Functions workflows
AWS Lambda automation is usually the fastest way to remove repetitive work. A function runs when a file lands in S3, a message hits SQS, a schedule fires in EventBridge, or a webhook arrives through API Gateway. You pay per invocation, so small jobs cost very little.
For processes with several steps, approvals or retries, we use Step Functions so each stage is visible, logged and restartable. That matters when finance or IT asks why a record failed at 2 a.m. and what happened next. Where a client already runs n8n or another orchestrator, we can connect it to AWS instead of replacing it.
WHAT WE BUILD
What we automate
Typical automations, each scoped and quoted at a fixed price after a free review.
Invoice and document extraction
PDFs dropped into an S3 bucket trigger Lambda and Amazon Textract, fields are validated, and clean records post to your ERP or a review queue.
Bedrock knowledge assistant
A Bedrock Knowledge Base indexes policies, SOPs and contracts so staff get cited answers in Slack, Teams or an internal web app.
Nightly ERP to data lake load
Glue jobs scheduled by EventBridge pull SAP or Oracle extracts into S3, apply data quality checks, and load Redshift for reporting.
Multi-step approval workflows
Step Functions routes purchase requests or vendor onboarding through checks, AI summaries and human approval, with every step logged.
Support ticket triage
New tickets from ServiceNow or Zendesk are classified and summarized by a Bedrock model, then routed to the right queue with a draft reply.
Data quality and failure alerts
CloudWatch alarms and SNS notify the owning team by email or Slack when a pipeline fails or row counts fall outside expected ranges.
Scheduled report delivery
Athena queries run on a schedule and deliver formatted summaries or refresh QuickSight and Power BI datasets for leadership.
03
AWS Bedrock AI: generative AI inside your own account
Amazon Bedrock gives access to foundation models from Anthropic, Meta, Mistral, Cohere, Amazon and others through one API, with your prompts and data staying inside your AWS environment. For many enterprise teams this is the easiest route through a vendor security review, because data handling, IAM and logging follow the controls you already have.
We build retrieval-augmented assistants with Bedrock Knowledge Bases over SharePoint, S3 or Confluence content, and Bedrock Agents that can call your internal APIs to look up orders, open tickets or draft responses. Guardrails, CloudWatch logging and human review steps are part of the design, not an afterthought.
04
Data pipelines and analytics on AWS
Good AI and good reporting both depend on clean, current data. We build pipelines that pull from ERP systems such as SAP S/4HANA or Oracle EBS, CRMs such as Salesforce, and SaaS APIs into an S3 data lake, catalog it with Glue, and serve it through Athena or Redshift.
Where a client already uses Snowflake or Databricks on AWS, we build around that rather than adding another warehouse. As one example from our case studies, a European procurement firm working on nuclear infrastructure saved about 30 hours a month with a supplier intelligence tool built on automated data collection and AI summaries.
05
Working with an AWS consultant in an enterprise setting
An AWS consultant working with a mid-size or large company has to fit existing IT. We work inside your change control process, use IAM roles with least privilege, store secrets in Secrets Manager, and deploy through CloudFormation, AWS CDK or Terraform so every change is reviewable.
We expect security questionnaires and answer them plainly. Access is through your SSO and IAM Identity Center where available, logs go to CloudWatch and CloudTrail, and nothing runs from our own accounts.
- Infrastructure as code in your repository, reviewed through pull requests
- Separate dev and production accounts or environments
- Audit trails through CloudTrail and structured application logs
- Runbooks and handover documentation for your internal team
06
AWS vs Azure vs Google Cloud for automation and AI
The best cloud is usually the one you already run, because identity, networking, billing and security reviews are already in place. AWS is strong for event-driven serverless work and offers a wide choice of models through Bedrock.
Azure tends to fit Microsoft 365 and Dynamics shops, with Azure OpenAI and Logic Apps. Google Cloud stands out for BigQuery analytics and Vertex AI with Gemini. We build on all three and will tell you if a different tool suits a specific job better.
07
What AWS consulting costs and how to get started
We quote a fixed price after a free review of the process, the systems involved and your security requirements. A single Lambda or Bedrock workflow typically ships in 2-4 weeks. Larger data platform or AI programs are delivered in stages over 4-8 weeks, with something useful live after the first stage.
AWS usage is billed to your account by Amazon. We estimate running costs during design and set up budgets and cost alerts so there are no surprises.
TOOLS
Tools we connect
FAQ
AWS Consulting questions
What do AWS consulting services include?
Our AWS consulting services focus on automation, data pipelines and AI built in your AWS account. That includes Lambda and Step Functions workflows, Glue and Redshift pipelines, Amazon Bedrock assistants and dashboards. We do not provide general hosting or data center migration.
How much does an AWS consultant cost?
We quote a fixed price after a free review rather than billing hourly. Cost depends on the number of systems, data volumes and security requirements. AWS usage is billed separately to your account by Amazon.
Is Amazon Bedrock safe for company data?
Bedrock runs inside your AWS environment, and AWS states that prompts and outputs are not used to train the underlying models. Access follows your IAM policies and can be logged. Your security team should still review the setup, and we provide documentation to support that review.
Can you work with our existing AWS environment and IT team?
Yes. We deploy into your accounts using your naming, tagging and network standards, through infrastructure as code your team can review. We work within your change control and access processes.
What is the difference between AWS Lambda and Step Functions?
Lambda runs a single piece of code in response to an event. Step Functions coordinates many steps, including Lambda functions, waits, retries and approvals, into one visible workflow. Most production automations use both.
How long does an AWS automation project take?
A single workflow typically ships in 2-4 weeks. Data platforms and AI programs are delivered in stages over 4-8 weeks, with the first stage live early so your team can use it and give feedback.
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GET A FREE PROPOSAL
Tell us what you want automated
Describe the process that eats your team's week. We reply within one business day with a first take on what can be automated, which tools fit and roughly what it would cost.
- Free 30-minute automation review
- Fixed quote before any work starts
- You own every workflow, account and line of code