RapidCanvas

RapidCanvas + Amazon Web Services

  • AWS Marketplace listed
  • APN Partner
  • EDP eligible

AWS Marketplace listed. APN Partner.

Your AWS account is already where the data sits and where the controls are enforced, and Bedrock already gives you the models. The gap is between having those pieces and having something in production that uses them. RapidCanvas closes it from inside the same account, so it inherits the postures you have already set, calls Bedrock for inference, and is bought on Marketplace against committed spend. Model choice stays yours: swap Claude for Titan, Mistral or Llama without rewriting your agents.

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12

AWS integration use cases documented

7

use cases already in production

11

distinct AWS services in active use

How it fits

  • Procurement through AWS Marketplace, with private offers supported
  • Bedrock model backend: Claude (Sonnet, Opus), Amazon Titan, Mistral, Meta Llama
  • Standard connectors to S3, Glue Data Catalog, RDS, Aurora and Redshift, plus Kinesis for streaming

Why Amazon Web Services customers choose this

  • One AWS account and one audit boundary, so RapidCanvas inherits the postures the account already holds
  • Bedrock consumed as part of a production AI platform, with evaluation and lineage included
  • Model swaps are configuration-driven; no agent rewrites

AWS holds the data, the controls and the models. RapidCanvas turns them into production systems. Together they get an AWS customer from account to outcome inside one audit boundary.

ProcurementAWS Marketplace listing liveAWS Partner Network memberEDP eligibility subject to your AWS contract
Reference Architecture

How RapidCanvas Builds on AWS

The same pattern recurs across all twelve engagements: credentials from IAM, files through Amazon S3, analytics through Athena and Redshift, inference through Bedrock, RapidCanvas doing the AI and workflow layer in between.

The same three-lane shape recurs across all twelve use cases: Business, RapidCanvas, and AWS.

Built to run inside the account you already govern.

Read data in place

RapidCanvas connects to Amazon S3, Amazon Athena, Amazon Redshift, Amazon Connect and Amazon Bedrock with scoped IAM credentials, and never asks a customer to copy data into a separate platform-owned store first.

Deploy inside the account

RapidCanvas runs as SaaS or as a private deployment on Amazon EKS provisioned by Terraform inside the customer's own AWS account, inheriting that account's IAM, KMS and network posture.

Keep the model configurable

Amazon Bedrock stands alongside the direct Anthropic API as a model backend selected by configuration, so inference can move inside the AWS boundary without a rebuild of the application.

Use Cases

12 Use Cases on AWS

Customer names are withheld. Every story below is a real RapidCanvas deployment, verified against the delivered code.

Contact center operations, Latin America

From Thousands of Recordings to Nightly Scorecards

  • Amazon Connect
  • Amazon S3
  • Amazon Athena

A contact center in Latin America needed continuous quality review of its agents without supervisors listening to thousands of recordings. RapidCanvas built a nightly pipeline that reads Amazon Connect contact records and recordings from Amazon S3, pulls a second automated channel's records through Amazon Athena, samples ten percent of eligible calls every night, transcribes and scores them against five criteria and six call types, and serves the results through a Data App with quality, agent and sentiment dashboards.

Hundreds of thousands of calls analysed, with ten percent sampled nightly across millions of records.
B2B marketing technology, global

Turning a Claims Backlog Into a Review Queue

  • Amazon S3
  • Amazon RDS

A B2B marketing technology provider needed to validate dealer reimbursement claims against roughly five hundred policy rules without adjudicators sorting untyped, often scanned documents by hand. RapidCanvas built a pipeline that stores every claim document in Amazon S3, routes each file to the right extraction method by type and size, classifies and extracts structured data with one prompt, and evaluates rules in batches to cut model calls by about ninety percent, with adjudicators finishing review in a Data App.

Batched rule checks cut model calls about ninety percent across daily claim volume.
Energy distribution, Americas

Seven Dashboards Consolidated Into One Live View

  • Amazon S3
  • Amazon Athena

An energy distributor's plant and route managers had one view of daily service orders spread across seven microservices, with month-to-date reads touching hundreds of thousands of rows against a thirty-second budget. RapidCanvas replaced the seven services with one reader that prunes Amazon S3 Parquet partitions by month and route, serves street sales from the same data platform, and caches repeat queries, serving a single Data App where managers drill from date to plant, route and customer with cascading filters.

Seven microservices became one Data App serving large month-to-date queries within thirty seconds.
Affiliate marketing, global

Media Kits and Clicks Become One Recommendation Engine

  • Amazon S3

An affiliate-marketing network's planners spent thirty to forty-five minutes memory-matching each placement proposal against hundreds of millions of click rows already sitting in Amazon S3, while publisher media kits piled up as unread PDFs. RapidCanvas scans daily Amazon S3 handoffs, extracts each media kit with Claude through its model gateway, loads the Delta and Parquet click-performance feed from Amazon S3 into a clicks table, and joins the two so a two-layer recommendation engine can produce ranked, exportable proposals.

Hundreds of millions of click rows joined to media kits for ranked placement proposals.
Financial services, global

Quarterly Fund Reports Turned Into Structured Data

  • Amazon Bedrock

An outsourced financial-services provider needed analysts to stop retyping quarterly private-credit fund PDFs into a per-instrument dataset, with numbers accurate enough for investor reporting and cheap enough to run at scale. RapidCanvas built a Data App that picks the best input format per page, extracts fields with Claude, chosen through Amazon Bedrock or the direct Anthropic API by configuration, computes derived metrics in Python rather than the model, and lets analysts review, override and export to Excel.

Already live, with Bedrock a configuration-only path to AWS-resident inference.
Real estate, Latin America

A Governed Lakehouse Replaces a Sixty-Table Legacy Schema

  • Amazon S3

A residential developer's market-intelligence system depended on a sixty-table legacy schema with no lineage, breaking every time a vendor's file format drifted. RapidCanvas built a governed lakehouse that lands vendor files in Amazon S3, compares each file's S3 ETag against a manifest so unchanged files are skipped, hashes and stores approved files as immutable bronze, and rebuilds gold dimensions and facts through shadow tables and atomic renames, so the application always reads a complete, documented layer.

More than a dozen ingest recipes and gold builders run daily against Amazon S3.

6 More Use Cases in the Full Case Study

  • Commission Invoices Reconciled Straight From S3 DatamartsAffiliate marketing, Europe · In production
  • One Reconciled Dashboard Replaces a Dozen SpreadsheetsConsumer skincare, North America · In delivery
  • Supplier Spreadsheets Map Themselves to the SchemaRetail, Europe · In delivery
  • A Voice Agent Routes Sales Calls in Real TimeTelecommunications, Latin America · Reference pattern
  • A Test-Data Platform Deployed Inside One AWS AccountAdvanced manufacturing, North America · Proof of concept
  • Interview Transcripts Become a Searchable, Grounded ChatManagement consulting, global · Proof of concept
Download complete case study PDF

Amazon Web Services questions

Yes. The listing is live and transactable through a private offer, with EDP eligibility subject to your own contract with AWS. A private offer is sales-led rather than self-serve, so it starts with a conversation and the terms are agreed before anything is provisioned.
Claude in Sonnet and Opus, Amazon Titan, Mistral and Meta Llama. Model choice is configuration-driven, so swapping one for another does not mean rewriting your agents, and the platform layer wraps every call with evaluation and lineage regardless of which you pick.
S3, the Glue Data Catalog, RDS, Aurora and Redshift through standard connectors, plus Kinesis for streaming. The platform reads them in place, inside the same AWS account, so it inherits the postures that account already holds.

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