Case Studies | SaaS B2B

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Real-Time AI Content Generation Accelerator

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The feasibility study framed the customer problem around four focus areas.

Hallucination and factual errors

Standard large language models generate confident but wrong facts, so every claim needs checking before it can be published. Grounding each output

Content that ages immediately

Model knowledge cutoffs mean AI content is stale the moment it is produced. Fetching current web data at generation time keeps output aligned with what is happening today.

Slow and expensive production

Researching, writing and fact-checking a single article takes hours, and unconstrained model and search calls can lead to runaway API spend. The challenge is to compress that cycle to minutes while keeping cost predictable.

No AI tooling for Second Life

LSL scripting for Second Life is a specialised skill with zero AI support today, an entirely unaddressed pain point in a virtual economy estimated at over 500 million dollars in annual GDP.

Solution

Cloud Combinator and the client deliver the accelerator as a packaged, repeatable engagement of four to six weeks, structured in three phases from assessment to a hardened production handover.

4-6 wks

From kickoff to a live production pipeline

10x

Targeted uplift in research-grounded content output (projected)

$500M+

Second Life virtual economy addressable (market estimate)

By the numbers:

  • 4-6 wks - From kickoff to a live production pipeline
  • 10x - Targeted uplift in research-grounded content output (projected)
  • $500M+ - Second Life virtual economy addressable (market estimate)
Changes

Because this is a feasibility engagement, the outcome is the study's verdict, its target metrics, and the evidence from the live the client's reference implementation, rather than broad delivered results. The study concluded that the buyer pain is real and growing, that the joint offer has clear and defensible differentiation, and that Second Life LSL generation is an unique, unserved niche worth pursuing. The figures below are target and market figures, marked accordingly.

  • Proven reference implementationThe client's platform already runs the client and AWS serverless integration in production, generating research-grounded blog content and LSL code snippets, and serves as the design partner, demo environment and proof of repeatability.
  • Accuracy by designReal-time the client's grounding targets a near-zero hallucination rate on factual claims, with Bedrock Guardrails adding a further safety layer. This is a target, validated qualitatively through the reference implementation.
  • Defensible differentiationThe study found this to be the only AWS-native, VPC-deployable offer combining the client's grounding, Step Functions and Bedrock multi-agent orchestration, and specialist LSL code generation with no direct competition.
  • Cost controlled from day oneA fully serverless, pay-per-use architecture with per-workflow the client's credit and Bedrock token budgets is designed to prevent runaway API spend.
  • HandoverEach engagement is designed to leave the customer with a live pipeline, CloudWatch dashboards, runbooks, a KPI baseline against target, and a prioritised expansion roadmap.

With the feasibility study complete and the client live as the reference customer, Cloud Combinator and the client's plan to secure further design-partner pilots, publish a joint reference architecture, and scale through AWS co-sell, expanding from blog and image generation into e-commerce copy, technical documentation and the underserved Second Life creator economy.

AWS Stack

AWS Lambda

And AWS Step Functions for serverless multi-agent orchestration.

Amazon API Gateway

(WebSocket) for real-time streaming of generation progress.

Amazon Bedrock Guardrails

For safety filtering of all model outputs.

Amazon DynamoDB

And Amazon S3 for job state, context sharing and artefact storage.

AWS Secrets

Manager, Amazon CloudWatch and AWS CloudTrail for security, observability and audit.

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