AWS AI
Assessment
A fixed-scope engagement that turns AI ambition into a plan you can build. In 2 to 3 weeks, former AWS engineers hand you a prioritized roadmap, a reference architecture, an ROI model, and a security review, before a single line of production code.
Scope Your Assessment2–3 wk
Fixed-scope engagement
4
Concrete deliverables
HIPAA
Regulated-ready by default
– The Deliverables
What You Walk Away With
Every assessment ends with four concrete artifacts, not a slide deck. The plan is yours to keep and build with any team.
Prioritized Use-Case Roadmap
A ranked shortlist of AI use cases scored on business value and feasibility, so you invest in what moves the needle first.
Reference Architecture
A high-level architecture on AWS (Amazon Bedrock, data pipelines, and integration points) with the services chosen and justified.
ROI & Cost Model
A grounded estimate of build cost, ongoing AWS spend, and expected return, with the same rigor behind our public OCR cost calculator.
Security & Compliance Review
A review of privacy, regulatory, and data-handling requirements, HIPAA and PHI constraints included, baked into the plan from day one.
– Why Most Start Wrong
Six Mistakes That Derail AI Initiatives
Deploying AI comes with unique technical and strategic traps. These are the six we see most often, and the ones the assessment is built to prevent.
No Clear Objectives
Without defined business goals, AI projects ship outputs that impress in a demo but never deliver real value.
Underestimating Complexity
Integration, infrastructure, and maintenance realities surface late and quietly delay or derail the whole initiative.
Weak Data Foundation
Poor, incomplete, or unstructured data produces inaccurate models and results no one can trust.
Skipping Use-Case Validation
Chasing every idea at once, instead of the high-impact and feasible few, burns budget on wasted effort.
Neglecting Security & Compliance
Treating privacy, regulatory, and ethical requirements as an afterthought invites legal and reputational fallout.
Overengineering the Solution
Overly complex models and pipelines slow deployment, hurt usability, and pile on maintenance burden for years.
– The Horus Advantage
Six Ways We Answer Every One
Each mistake above has a direct antidote in how we run the assessment. Built by former AWS engineers who have taken regulated enterprises to production.
Clear Business Alignment
We define specific, measurable AI objectives tied to your organizational goals, so every initiative maps to real business value from the start.
Technical Feasibility Upfront
Former AWS engineers assess integration, infrastructure, and technical requirements early, delivering architectures that prevent the delays complexity usually causes.
Data-Driven Foundation
We evaluate your data quality, completeness, and readiness, flagging gaps so models are trained on reliable, structured data that produces accurate insights.
Prioritized Use Cases
We identify and validate high-impact use cases, weigh their opportunities against their challenges, and focus you on the initiatives with the greatest return.
Security & Compliance by Default
Privacy, security, and regulatory requirements are embedded in every phase. We have shipped AI under HIPAA constraints such as keeping PHI out of logs.
Practical, Scalable Solutions
We never overengineer. Our recommendations are practical, user-friendly, and built to scale, from a first proof of concept to hundreds of thousands of transactions a month.
– Proof, Not Promises
Proven on Regulated Healthcare AI
We assess and then build. Two healthcare enterprises we have taken to production AI on AWS.
LabCorp
Enterprise-scale, HIPAA-regulated
As the AWS and Terraform lead, we architected and deployed AI workloads on Amazon Bedrock: an agentic intelligent-document-processing research lane with specialist field agents, plus the MyLabCorp Trustworthy AI evaluation framework. We rewrote infrastructure against LabCorp's golden Terraform modules and wired it into their Bitbucket, Jenkins, and blue/green ECS delivery pipeline.
VRC
Non-AI-native to production AI
We took a healthcare company with no AI footprint all the way to a production document-processing platform. We built it end to end: a React and TypeScript frontend, an API Gateway and Lambda backend, ECS-hosted vLLM inference on a Qwen model, and an S3 and SQS pipeline backed by Aurora Serverless, extracting and scoring documents automatically.
– How It Works
Three Steps to a Buildable Plan
Discovery Call
A short call to understand your goals, data, and constraints. We scope the assessment to a fixed fee so you know the full cost before committing.
2–3 Week Assessment
We evaluate use cases, data readiness, architecture, and compliance, working with your stakeholders and validating feasibility against real AWS constraints.
Roadmap Handoff
You receive the roadmap, architecture, ROI model, and security review. Build it with your team or continue with us to implement it.
Frequently Asked Questions
What is an AWS AI Assessment?
It is a fixed-scope discovery engagement run before any build work. Over roughly 2 to 3 weeks, our former AWS engineers evaluate your goals, data, and infrastructure and hand you a prioritized use-case roadmap, a reference architecture on AWS, an ROI and cost model, and a security and compliance review.
How long does the assessment take?
Most assessments run 2 to 3 weeks depending on the number of use cases and the state of your data. You leave the engagement with a concrete plan, not a vague strategy deck.
How much does an AWS AI Assessment cost?
The assessment is a fixed fee scoped on a short discovery call, so you know the full cost before you commit. Pricing depends on the number of use cases and the complexity of your data and compliance requirements.
Do we have to build with Horus after the assessment?
No. The deliverables are yours to keep and implement with any team. That said, many clients continue with us to build what the assessment recommends, because we have already mapped the architecture and the risks.
Is the assessment appropriate for regulated industries like healthcare?
Yes. We have delivered production AI for regulated healthcare enterprises including LabCorp and VRC, with HIPAA constraints such as keeping PHI out of logs. Security and compliance are embedded in every phase of the assessment, not bolted on at the end.
What do we need to prepare before the assessment?
Very little. We work from your existing business goals, sample data, and access to the right stakeholders. Part of the assessment is telling you exactly where your data and infrastructure are ready and where the gaps are.
– Get started
Ready to turn AI ambition into a plan?
See how Horus Technology can reduce your document processing time by up to 85% with full compliance for your industry, on your AWS infrastructure.
What you'll get
No obligation, 45-minute technical demo
Custom ROI model for your use case
AWS-native deployment from week one
HIPAA & SOC 2 compliance included
Phone
+1 (858) 412-0778Location
San Diego, CA, USA