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AWS AI & Data

Your next AI workflow. Built on AWS.

We build AI workflows in your AWS account. Use Amazon Bedrock to put your enterprise knowledge to work, with access checks before retrieval and human approval before consequential actions.

Your AWS account. Your acceptance criteria.

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The problem

A working model is only the start.

The answer needs a reliable source

Your documents live in S3 or business systems. An AI answer must point back to the current source and respect who can read it.

An agent needs boundaries

A service account with broad permissions can turn a small mistake into a business problem. Define which tools an agent can call and which actions need approval.

Data access must survive the handoff

A permission on a source table does not automatically protect a copied document or vector index. Test access at every retrieval boundary.

Your team has to operate the result

Set a cost budget and quality threshold before launch. Give the team a runbook with a clear owner for failures.

From the work

90+ sales processes. A shared AWS data foundation.

For a global healthcare organization, we paired Power Apps workflows with an enterprise data layer on AWS. The same sales-process data could support other applications and analytics.

Read the anonymized case study

How it works

AWS services, tied to the workflow

Amazon Bedrock applications

Select a model against your own evaluation set. Build assistants with clear refusal behavior when the available evidence cannot support an answer.

Knowledge Bases & Amazon S3

Connect approved documents to Amazon Bedrock Knowledge Bases. Apply user-specific access checks before retrieval and return source citations with the answer.

AgentCore & workflow integration

Scope agent runtimes and tool access with Amazon Bedrock AgentCore. Use AWS Lambda or Step Functions where the process needs a defined execution path.

Governed data foundations

Design S3 data foundations with AWS Glue Data Catalog. Use Lake Formation permissions for supported Athena queries, with separate authorization for any downstream AI retrieval.

Identity & operating controls

Define least-privilege IAM roles and encryption requirements. Configure CloudWatch monitoring and CloudTrail records for supported AWS API activity, with application-level traces for the workflow.

Evaluation & handover

Test denied access as carefully as successful answers. Measure response quality and cost per completed task, then hand over the code with deployment instructions.

Our method

From one scoped workflow to an agent in daily use.

  1. Day one

    Scope and sign

    We define the workflow and the acceptance criteria it has to meet, and agree them in writing before the build starts.

  2. The build

    Build

    We build the agent in your environment, with Claude Code and automated evaluation against your own data, and test it against the criteria you signed.

  3. After launch

    Run

    We run, monitor and improve the agent after launch, measured against those criteria, with a named person on your side approving what it produces.

Compliance & security

  • Least-privilege access
  • Source citations
  • Human approval gates
  • Acceptance tests

How we engage

Four ways to work with us.

Data platform programs and wider portfolios are scoped separately. AWS usage stays on your own account.

Advisory

Strategy, roadmap and readiness for an AI program.

Build

One scoped workflow at a time, with acceptance criteria agreed in writing before the build starts.

Run

Managed operations after launch, measured against those criteria.

Outcome-linked terms

Commercial terms can be tied to the acceptance criteria you sign.

Every engagement starts with one scoped workflow and a written definition of done. Commercial terms are set in the statement of work.

Talk to our team How we engage

Frequently asked

Questions buyers ask.

Can we keep our existing AWS environment?

Yes. We scope the build around your AWS account and existing systems. We agree the deployment region, identity model, and data boundaries with your team before implementation.

Can an AWS workflow work with Microsoft 365?

Yes. We can connect approved Microsoft 365 sources or business applications through their APIs. We scope identity mapping and document permissions explicitly so a connector does not broaden access.

Does Lake Formation secure every AI answer automatically?

No. Lake Formation governs supported data access paths. Copies in a document store or retrieval index need their own access controls. We test that each user can retrieve only the information they are permitted to see.

What do we agree before the build starts?

One workflow, its source systems, and the acceptance tests. Those tests cover the useful result and failure cases such as denied access or missing evidence. The scope also names the AWS usage budget and handover requirements.

Research paper

The mid-market AI advantage: How $50M to $500M companies out-ship the Fortune 500

Why $50M to $500M companies get AI into production faster than the Fortune 500.

Read the paper
Get the designed PDF

Ready to ship AI that actually works?

Every engagement starts with one scoped workflow and a written definition of done. Commercial terms are set in the statement of work.

Talk to our team See our work
  • Built to SOC 2, HIPAA and GDPR standards
  • EU AI Act-aligned delivery
  • Microsoft Solutions Partner