Build a production generative AI feature.
A scoped consulting engagement that designs and delivers a real GenAI capability in your AWS accounts. RAG systems, autonomous agents or a custom model tuned to your domain. Fixed scope and clear handover.
Your engineers run it afterwards. We stay available for ongoing uplift if you want the AI Factory.
What generative AI means in practice
Generative AI uses foundation models to create, summarise, reason over or transform information. In production, the useful question is not which model comes first but what workflow should improve, what data can be used safely and how the result will be tested in your AWS accounts.
Foundation models
Foundation models provide the reasoning and generation layer. We choose the model and AWS service for the outcome, not the novelty.
Your data and workflow
RAG, agents, tools and fine tuning connect the model to your product, documents, systems and domain language.
Production controls
Guardrails, logging, evaluation, access controls and handover make the feature safe enough to run after launch.
What you get
Scoped GenAI build
One production feature, end to end. RAG, agents, custom model or a hybrid. Scoped to an outcome, not an hour count.
In your AWS accounts
Built under your account, your IAM, your data residency. Where Bedrock is the right fit, prompts and outputs stay out of public model training. We add the guardrails, masking, logging and access controls around it.
Domain tuned
The build reflects your domain language, your customers and your product, not a generic demo.
Responsible by default
Guardrails, audit logging and content controls baked into the build. Inherits the AI Factory patterns where relevant.
Handover planned from day one
Engineers work alongside us through discovery, design and build. By launch, your team can extend and operate it.
Clear next step
Run it yourself, add DevOps as a Service or move to the AI Factory for ongoing managed operations. Your call at handover.
Engagement model. Fixed scope. Knowledge transfer.
Discovery
- Use case workshop
- Data and governance review
- Success criteria agreed
- Benchmarks chosen
- Scope locked
Design
- Architecture in your AWS
- Model and tooling selection
- Prompt and retrieval design
- Evaluation harness
- Guardrail policy
Delivery
- Production feature built
- Evaluation suite running
- Documentation
- Training
- Signed off handover
How it works
A consulting engagement is not open ended. Each phase has a clear output. You own everything we produce.
Discover
Use case workshop, data audit, governance check, success criteria and scope agreement. Clear input to Design.
Design
Architecture, model selection, prompt strategy, retrieval design, evaluation harness and guardrail policy. Reviewed before build starts.
Build
Feature built in your AWS. Evaluation suite running throughout. Your engineers paired in from the start.
Handover
Production feature signed off. Documentation, training and a clear path forward, whether your team runs it or moves to AI Factory.
Audited and certified
AWS DevOps Competency
ISO 27001 Certified
AWS SaaS Competency
Pick the GenAI feature that matters. We will scope the build.
Walk us through your product and the capability you want to build. We will scope an engagement on the first call.
Frequently asked questions
What is a Generative AI consulting engagement?
A Generative AI consulting engagement is a scoped build that delivers one production AI capability in your AWS accounts, then hands it over for your team to own.
What kind of generative AI build is this for?
Production features include RAG systems, autonomous agents, custom tuned models and content generation workflows. Not POCs and not demos.
Which models and providers do you work with?
Bedrock, Anthropic, OpenAI and open weights models hosted in your AWS. Selection is based on your data, latency, cost and governance needs, not vendor preference.
How long does an engagement take?
Typical engagements run eight to sixteen weeks depending on feature complexity and data readiness.
Do you build bespoke agents or use off the shelf frameworks?
We favour frameworks that match your stack. LangChain, LlamaIndex, Bedrock Agents and AWS Strands. Bespoke only when the requirement genuinely warrants it.
Where do prompts, evaluations and code live?
In your Git repositories and your AWS account. We deliver into your infrastructure, not ours.
What happens if we want ongoing operations?
Add the AI Factory. Agentic Ops runs the agents, Guardrails enforces controls and MLOps handles any custom model lifecycle. The consulting engagement hands off cleanly into managed operations.