Gen AI · Step 01 Assess

GenAI/ML Readiness Assessment.

Understand business goals, current AI capability, data readiness, technology gaps and build an execution pathway.

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Built with

  • Amazon Bedrock
  • Amazon Bedrock AgentCore
  • Amazon SageMaker
AWS Partner, Advanced Tier ServicesAWS Partner, Generative AI Competency, via Ingram Micro

Why it matters

Why it matters.

Most GenAI programs fail on selection, not on engineering.

The wrong use case is expensive even when it is built perfectly.

An honest read on data readiness now saves months of rework later. The model is rarely the bottleneck; the data is.

You end up with a sequenced pathway leadership can fund, not a list of ideas.

What’s included · how it runs

Discover. Assess. Design. Present.

Typically 3–4 weeks

  1. Step 01

    Discover

    Interview stakeholders, review the estate and collect candidate use cases.

    What’s included

    Use case discovery

    Workshops across product and operations to surface candidates, then score them on value, feasibility and risk.

  2. Step 02

    Assess

    Score use cases, test data readiness against the top candidates and identify gaps.

    What’s included

    Data readiness review

    What data exists, where it lives, how clean it is and what has to happen before a model can use it.

    Capability and gap analysis

    Skills, platform, security posture and tooling measured against what your top use cases need.

  3. Step 03

    Design

    Define target architecture on AWS and the sequence to get there.

    What’s included

    Execution pathway

    A staged roadmap with target architecture, cost envelope and the first build clearly defined.

  4. Step 04

    Present

    Walk leadership through findings, costs and the recommended first build.

    What you walk away with

    • Scored and prioritised use case register
    • Data readiness findings
    • Target AWS architecture
    • Costed execution roadmap

Case studies

Customers we’ve delivered this for.

All case studies
Draft · pending HES approval
Case studyHuman eSources

Make the hiring call with AI.

How Human eSources turned nearly three decades of talent science into AI that explains every job match, live on Amazon Bedrock behind TruTalent, its new recruiting platform.

Industry
HR tech, talent assessment
We built
Talent Scout AI
Runs on
Amazon Bedrock
Read the case study
Keith Gaudreau, VP Product Strategy, Human eSources
Human eSources

Keith Gaudreau

VP Product Strategy

Case studyMould Detect AI

Computer vision for mould detection.

[How the client got the outcome: what Assembly built and where it runs. One sentence.]

Talk to an engineer
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Case study[Client]

[The promise] [in 2 lines].

[How the client got the outcome: what Assembly built and where it runs. One sentence.]

Talk to an engineer
[First Last][Role]

Funded by AWS · for eligible companies

Funded.

Your GenAI/ML Readiness Assessment, funded by AWS if you qualify.

Check if you qualify

Funding is subject to AWS approval. We check your fit with you before anything goes to AWS.

Let’s talk about GenAI/ML Readiness Assessment.

A 30-minute call is usually enough to tell you whether this is the right next step, and what it would cost.