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AI & ML on AWS

AI/ML solutions on Amazon Bedrock and SageMaker

Five reference solutions developed and delivered by Rabbit Cloud, each adapted to the client's data, security requirements and operating model. Every solution is delivered with an evaluation framework, guardrails and a cost-per-transaction model.

Document Intelligence Pipeline

Conversion of unstructured documents into structured, validated and auditable records.

Amazon BedrockAmazon TextractHuman-in-the-loop review

Solution detail

Documents arrive as scans, faxes, PDFs and email attachments. The pipeline classifies each document, extracts fields with layout awareness, validates them against business rules, and routes any result below a confidence threshold to a reviewer with the source page highlighted. Every decision is recorded with the model, prompt version and confidence score.

Built for

Patient access and prior authorisation processing
Claims, invoice and remittance processing
KYC and customer onboarding document review
Contract and policy data extraction

Related case studies

Document intelligence on AWS, integrated with an existing Azure estate · Healthcare Services

Straight-throughtarget rate agreed per document class

Per-page costmodelled prior to build

Full auditmodel, version and confidence recorded

Enterprise Knowledge Assistant

A grounded, citation-backed assistant over enterprise documentation, tickets and procedures.

Bedrock Knowledge BasesAmazon OpenSearchSource citations

Solution detail

A retrieval-augmented assistant that answers from the organisation's own corpus and cites the source passage. Chunking strategy is tuned to the client's document types, retrieval is hybrid with reranking, refusal behaviour is defined for questions outside the corpus, and an evaluation set is executed on every change to the corpus or prompts.

Built for

Implementation support for platform and open-source products
Internal engineering and policy knowledge bases
Customer support deflection with escalation handoff
Sales and pre-sales enablement across solution libraries

Related case studies

Adoption assistant for an open-source payments standard · Payments Infrastructure

Cited answersevery response traced to source

Evaluated releasesregression suite gates every change

Corpus scaledesigned for six-figure chunk counts

Clinical & Contact-Centre Voice Intelligence

Speech to structured record, with clinical and domain vocabulary and PHI controls.

Amazon TranscribeComprehend MedicalPHI redaction

Solution detail

Conversations are transcribed, summarised into a structured schema and committed to the record with the entities downstream systems expect. PHI detection and redaction precede any generative step, and tenant isolation and encryption are designed into the architecture rather than added after review.

Built for

Clinical encounter documentation and note generation
Contact-centre quality assurance, summarisation and disposition coding
Care coordination and follow-up task extraction
Compliance review of recorded interactions

Related case studies

HIPAA-aligned AI documentation layer for a multi-tenant EHR platform · Healthcare SaaS

PHI controlsredaction ahead of any generation step

Domain vocabularycustom lexicons per specialty

Review gateno write-back without review

Vision Verification & Fraud Detection

Computer vision for signature, document and identity verification at operational scale.

Amazon SageMakerAmazon RekognitionRegional data residency

Solution detail

Custom vision models for verification tasks that otherwise consume manual effort: signature matching against reference specimens, tamper and forgery detection, document authenticity and identity matching. Deployed in the AWS Region required by the regulator, with a reviewer workflow for scores within the defined uncertainty band.

Built for

Signature verification for banking and lending operations
Cheque and instrument tamper detection
Identity document authenticity checks
Damage, defect and condition assessment

Related case studies

Signature verification at branch scale for a core banking ISV · Banking Software

Data residencyin-region deployment, including ap-south-1

Review thresholdscalibrated with the client's risk function

Model versioningevery decision traceable to a model version

AI-Assisted Cloud Operations

An agent that triages alerts, assembles context and proposes remediation, with human approval on every change.

Bedrock AgentsAWS Step FunctionsApproval workflow

Solution detail

When an alert is raised, the agent retrieves relevant metrics, logs, recent deployments and the applicable runbook, produces a triage summary and proposes a remediation. Read-only investigation executes autonomously. Any action that modifies infrastructure requires explicit human approval and is logged in full.

Built for

First-line alert triage and enrichment
Change and deployment correlation during incidents
Runbook execution with approval gates
Cost and configuration drift investigation

Reference architecture

Alert intake from Amazon CloudWatch, PagerDuty or an existing queue

Context assembly across metrics, logs, traces and deployments

Bedrock Agent reasoning against the runbook library

Proposed action with a human approval gate

Execution via AWS Systems Manager and Step Functions, fully logged

Triage timecontext assembled before an engineer is engaged

Approval gateno change action without authorisation

Full auditevery step recorded in AWS CloudTrail

Delivery approach

Our approach to AI delivery

Evaluation, governance, unit economics and the operating model determine whether AI systems reach production. Rabbit Cloud addresses each from the outset of an engagement.

Evaluation before scale

An evaluation set built from the client's own cases, executed on every prompt, model or corpus change. Accuracy is measured against a baseline, not asserted.

Guardrails and data handling

PII and PHI detection, prompt injection defence, refusal behaviour, tenant isolation and full audit logging of model decisions.

Unit economics defined in advance

Model selection is an economic decision as well as a quality decision. Cost per document, per call and per query is modelled before build.

Human oversight by design

Confidence thresholds route uncertain cases to reviewers. Actions that modify systems require approval. Automation assembles context; people make decisions.

Get in touch

Discuss an AI/ML use case with our team

A structured assessment determines feasibility, data readiness and the appropriate delivery path before any commitment to build.

Contact our team AI/ML service line