AWS AI Services
Wire AWS managed AI into your applications — Bedrock, SageMaker, Rekognition, Textract, Forecast — with event-driven pipelines, human-review loops, and cost-per-inference tracking. ML capability without requiring a dedicated data-science team for every integration.
Overview
AWS offers a mature set of AI and machine learning services — from pre-built APIs for vision, language, and speech to fully managed platforms like SageMaker. Most organisations underuse these services because they either assume they need a data science team or they do not know how to integrate AI into existing workflows. We identify the right use cases, integrate managed AI services into your applications, and keep costs and outcomes measurable.
Clevertek scopes every engagement to your environment — capacity, sites, compliance and support model — so you get a tailored plan rather than a fixed SKU. Pricing is quote-only, and our solutions architects will work through your requirements before any proposal.
Our approach
We apply AWS AI services to real business problems — document processing, customer support triage, demand forecasting, quality inspection, and content moderation. Our team starts by identifying the jobs that are good candidates for AI: high-volume, repeatable decisions where a model can augment or automate a human step. We then select the appropriate AWS service — Amazon Textract for document extraction, Comprehend for language analysis, Rekognition for image and video, Forecast for demand prediction, or Bedrock for foundation model access. Each integration includes a human review loop where the business risk warrants it, cost-per-inference tracking, and a fallback path for low-confidence predictions. We do not oversell AI: if a rule-based solution is cheaper and more reliable, we say so.
Why work with us
Use-case-first, not technology-first
We start from the business problem and work backwards to the AI service that fits — not from a model looking for an application. Half our AI engagements involve saying no to AI when a simpler solution works.
Managed services over custom models
Pre-built AWS AI services handle 80% of use cases without a data science team. We reserve custom model training only for genuinely novel problems with a clear ROI case.
Cost and governance as design constraints
We track cost per inference from day one, set ceilings, and design fallback paths. AI cost surprises and model failures are designed out, not discovered in production.
Human-in-the-loop by default
Every AI integration includes a review step where the business risk is meaningful. AI assists decisions; your process and accountability stay with your people.
Bedrock-ready for foundation model access
We help you select and integrate foundation models through Amazon Bedrock with guardrails, cost controls, and monitoring — no standing up your own model infrastructure.
Integration into existing workflows
AI is only useful inside a workflow. We connect it to your existing applications, databases, and business processes — not a standalone demo.
Key benefits
What this solution delivers for your business.
Automated document processing
Extract structured data from invoices, forms, contracts, and reports — reducing manual data entry from hours to seconds with Amazon Textract and Comprehend.
Intelligent customer support routing
Classify and route support tickets by intent, sentiment, and urgency using language AI — getting each query to the right team without human triage.
Accurate demand forecasting
Improve inventory and workforce planning with AI-driven demand forecasts that learn from your historical data and external factors.
Reduced operational cost through automation
Automate high-volume, repeatable decision tasks — document classification, content moderation, data extraction — that previously required full-time human effort.
Faster insights from unstructured data
Turn unstructured text, images, and documents into structured data your analytics pipeline can consume — without manual tagging or data entry.
Controlled AI spend with measurable ROI
Cost-per-inference tracking and usage ceilings prevent budget surprises. Every use case has a defined success metric tied to business outcomes.
What's included
Part of this managed service.
Document and text intelligence
Extract, classify, and analyse text from documents, emails, and reports using Amazon Textract, Comprehend, and Bedrock foundation models.
- Invoice and form data extraction
- Document classification and routing
- Sentiment and entity analysis
- PDF and scanned-document processing
Vision and content moderation
Analyse images and video for object detection, content moderation, quality inspection, and text-in-image extraction using Amazon Rekognition.
- Image classification and tagging
- Content moderation and policy compliance
- Quality inspection on production lines
- Text extraction from images
Conversational AI and chatbots
Build conversational interfaces for customer support, internal helpdesks, and self-service workflows using Amazon Lex and Bedrock.
- Intent-based conversation design
- Integration with support systems
- Escalation to human agents
- Multi-language support
Forecasting and anomaly detection
Predict demand, detect anomalies, and forecast business metrics using Amazon Forecast and Lookout for Metrics.
- Time-series demand forecasting
- Anomaly detection in business metrics
- Inventory optimisation models
- Integration with planning systems
Where it helps
Real-world scenarios where this solution delivers measurable outcomes.
Support tickets that sort themselves
Classify and route incoming queries with language AI so the right team sees them first — reducing first-response time while eliminating manual triage.
Documents that process themselves
Extract structured fields from invoices, contracts, or forms and push them into your ERP or CRM system without manual data entry.
Product images that moderate themselves
Automatically detect policy-violating content in user-uploaded images before they reach your platform, with human review only on borderline cases.
Questions buyers actually ask
Do we need a data science team for this?
Not for the common cases. Managed AI services handle the model; we handle the integration and the guardrails. A data science team is justified only when the job is genuinely novel.
How do we control AI cost?
We measure cost per call from day one and set ceilings. Most teams overspend on AI by invoking it where a rule would do — we keep the simple path simple.
What if the model is wrong?
We design a human-in-the-loop and a fallback for exactly that. AI assists the decision; your process stays accountable.
Can you use custom models or fine-tuning?
Yes, where the use case warrants it via Amazon Bedrock or SageMaker. But we only go down that path when pre-built models genuinely cannot deliver the required accuracy.
How do we measure AI ROI?
Each use case gets a defined success metric before build — time saved, accuracy improved, tickets reduced. We track these post-deployment and report actual versus projected.
Ready to scope a solution?
Talk to a Clevertek solutions architect about your requirements — no obligation.