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Capilano AIby Quanteroun Solutions

AI Modernization on AWS

Move AWS AI experiments into secure, observable services that complete real work

Modernize AI and automation on AWS with Amazon Bedrock agents and knowledge bases, Textract, serverless workflows, secure data foundations, and production observability.

Best suited for
AWS estates turning prototypes, scripts, and manual workflows into secure, supportable AI services
Engagement shape
Use-case assessment, target architecture, pilot, migration, and production rollout
Amazon Web Services cloud platform

Cloud-specific delivery

Architecture, implementation, evaluation, and operations designed for the platform you already run.

Amazon BedrockBedrock Knowledge BasesAmazon Textract

The service in practice

Start with the work that needs to improve

AWS AI modernization is not a model-shopping exercise. It is the work of turning a useful experiment into a secure service with dependable data, controlled access, measurable quality, and an operating owner.

Capilano AI helps teams use Amazon Bedrock, Bedrock Knowledge Bases, Textract, Lambda, Step Functions, and AWS data services to modernize a bounded workflow first, then establish the architecture and delivery practices needed to extend it responsibly across the business.

Why this work matters

Replace operational friction with a service your team can trust

What is getting in the way

Promising pilots with no production path

Prototypes may demonstrate model capability without resolving identity, integration, evaluation, deployment, cost, or support.

A fragmented AWS data, serverless, integration, and security estate

Data, documents, automations, and business logic sit in separate services with inconsistent ownership and controls.

Platform choices made before the workflow

Teams accumulate overlapping services because the operating outcome, evidence standard, and human role were never made explicit.

What the engagement should change

A defensible target architecture

A practical design for Bedrock agents and knowledge bases, event-driven workflows, document processing, evaluation, and CloudWatch operations, grounded in your existing cloud, identity, data, and integration boundaries.

One use case moved into production

A focused workflow delivered with evaluation, approval, telemetry, release controls, and a clear handoff to operations.

A modernization sequence leaders can fund

Dependencies, risks, platform decisions, and follow-on opportunities organized into a phased roadmap rather than an unbounded transformation program.

Platform capabilities

Use the cloud as a delivery system, not a model catalogue

We select managed services around the workflow, identity model, data boundaries, quality target, and operating responsibility.

01Amazon Bedrock

Build agents with model choice, action groups, guardrails, evaluations, and governed tool use.

02Bedrock Knowledge Bases

Ground applications in managed or customer-managed retrieval with citations and permission controls.

03Amazon Textract

Extract typed and handwritten text, forms, tables, invoices, receipts, IDs, and lending records.

04Lambda and Step Functions

Orchestrate event-driven AI workflows, approvals, retries, and business-system integration.

05S3, Glue, and Lake Formation

Prepare governed operational and analytical data for document, retrieval, and agent workloads.

06IAM, CloudWatch, and CloudTrail

Control access and establish logs, metrics, traces, alerts, audit, and operating ownership.

Where to apply it

Start with a bounded operating outcome

Each use case is scoped with data access, integration, evaluation, human approval, monitoring, and ownership from the beginning.

Build a Bedrock operations agent

Combine private knowledge, action groups, APIs, and human approvals for a bounded business workflow.

Automate document intake

Use Textract with serverless orchestration to validate documents, route exceptions, and update systems of record.

Create a governed company knowledge base

Ingest S3 and connected content, tune retrieval, preserve citations, and evaluate answer quality.

Move prototypes into accountable operations

Replace notebooks and fragile scripts with versioned infrastructure, evaluation gates, monitoring, and runbooks.

How we deliver

Evidence before scale. Ownership before launch.

The method is intentionally practical: reduce uncertainty early, build the full operating path, and leave the service with people who can run it.

See our delivery approach
  1. 01

    Define the operating outcome

    Map the work, baseline, users, decisions, exceptions, risks, and evidence required to call the engagement successful.

  2. 02

    Prove the difficult assumptions

    Test the data, integrations, model or platform behavior, quality target, and human workflow before scaling the build.

  3. 03

    Build the complete service

    Implement identity, data, workflow, evaluation, telemetry, deployment, documentation, and recovery—not only the visible AI feature.

  4. 04

    Release with an owner

    Roll out in controlled stages, train the operating team, review production evidence, and convert confirmed failures into improvements.

Typical engagement

What your team receives

The exact scope follows the operating outcome and current environment. These are the core work products typically required to make the result useful and supportable.

AI use-case, workload, and data-readiness assessment

Amazon Bedrock agent, model, tool, and guardrail architecture

Knowledge Base ingestion, retrieval, citations, and access design

Lambda, Step Functions, EventBridge, and API integration

Evaluation, trace, latency, safety, and cost baselines

Infrastructure as code, rollout plan, monitoring, and runbooks

Questions to resolve early

Frequently asked questions

Do we need to move everything to AWS?

No. We start with the systems and constraints you already have. The target design can keep selected data or applications outside AWS when that is the safer or more economical choice.

Can you modernize an existing pilot or low-code automation?

Yes. We review its workflow, prompts, tools, data access, failure modes, deployment method, and ownership, then decide what can be retained and what should be redesigned.

How do you choose the first use case?

We look for repeatable work with a measurable baseline, accessible data, a willing process owner, and a safe way for people to review or take over when needed.

What makes the result production-ready?

Production readiness includes identity, data protection, tested integrations, representative evaluations, telemetry, cost controls, release gates, incident handling, documentation, and an accountable owner.

Field notes

Read the implementation detail

All insights

Related services

AI Modernization on Microsoft Azure

Modernize AI, automation, and knowledge workflows on Microsoft Azure with Foundry agents, Azure AI Search, Document Intelligence, Fabric, Copilot Studio, and production controls.

AI Modernization on Google Cloud

Modernize AI and automation on Google Cloud with Vertex AI, Agent Development Kit, Agent Engine, Gemini, Document AI, BigQuery, and production-ready serverless delivery.

Document AI on AWS

Build production document workflows on AWS with Amazon Textract, Bedrock, S3, Lambda, Step Functions, validation rules, human review, and monitoring.

Bring us the workflow—not a finished AI specification

We will help clarify the operating outcome, difficult assumptions, delivery path, and evidence required for a responsible investment decision.