AWS AI assessment.
Before you commit a budget to AI, it's worth knowing what's actually worth building. In two to three weeks we hand you a prioritized roadmap, a reference architecture, an ROI model, and a security review — a plan your team can build from, whether or not that team is us.
Scope Your Assessment →2–3 wk
Fixed-scope engagement
4
Concrete deliverables
HIPAA
Regulated-ready by default
The deliverables
What you walk away 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, integration points — with every service chosen and justified.
ROI & Cost Model
A grounded estimate of build cost, ongoing AWS spend, and expected return — 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.
Deploying AI comes with its own traps. These are the six we see most often, and the ones the assessment is built to catch before they cost you.
01
No clear objectives
Without defined business goals, AI projects ship outputs that impress in a demo but never deliver real value.
02
Underestimating complexity
Integration, infrastructure, and maintenance realities surface late and quietly delay the whole initiative.
03
Weak data foundation
Poor, incomplete, or unstructured data produces inaccurate models and results no one can trust.
04
Skipping use-case validation
Chasing every idea at once, instead of the high-impact and feasible few, burns budget on wasted effort.
05
Neglecting security & compliance
Treating privacy, regulatory, and ethical requirements as an afterthought invites legal and reputational fallout.
06
Overengineering the solution
Overly complex models and pipelines slow deployment, hurt usability, and pile on years of maintenance.
The Horus advantage
How we answer every one.
Each mistake has a direct antidote in how we run the assessment — from a team whose engineering is led by people who took regulated enterprises to production on AWS.
Clear business alignment
We define specific, measurable AI objectives tied to your goals, so every initiative maps to real business value from the start.
Technical feasibility upfront
Our engineers assess integration and infrastructure early, so complexity doesn't ambush the project halfway through.
Data-driven foundation
We check your data quality and readiness and flag the gaps, so models train on data that produces results you can trust.
Prioritized use cases
We validate high-impact use cases, weigh opportunity against effort, and point you at the initiatives with the best return.
Security & compliance by default
Privacy and regulatory requirements are built into every phase. We've shipped AI under HIPAA constraints like keeping PHI out of logs.
Practical, scalable solutions
We don't overengineer. Recommendations are practical 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, then we build. Two healthcare enterprises we've taken to production AI on AWS, backed by our hands-on AWS consulting in San Diego.
LabCorp
HIPAA-regulatedAs 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
Zero to productionWe took a healthcare company with no AI footprint all the way to a production document-processing platform, built 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 that extracts and scores documents automatically.
How it works
Three steps to a buildable plan.
No long discovery theatre. A call, a couple of focused weeks, and a plan you can act on.
01
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.
02
2–3 Week Assessment
We evaluate use cases, data readiness, architecture, and compliance with your stakeholders, validating feasibility against real AWS constraints.
03
Roadmap Handoff
You get the roadmap, architecture, ROI model, and security review. Build it with your team, or continue with us — explore all services.
Keep exploring
What comes next.
Generative AI Consulting
Once the roadmap points at GenAI, this is how we build it on Amazon Bedrock.
AI & ML Development
Agentic workflows, RAG, fine-tuning, and the production pipelines to run them.
Intelligent Document Processing
The flagship many assessments point toward — extract, classify, route, validate.