GenAI Rapid Prototyping

Timeframe

2 days

Deliverables

Generative AI report

This report enables a go-or-no-go decision with recommendations for Generative AI process flow, use cases, tech analysis, team requirements, time and cost estimates, and risk assessment

Proof of Concept

A small PoC for a GenAI application illustrates its functionality

Metrics to improve

Development costs, time-to-market, release frequency, team performance, cycle time

Designed for

Tech teams too busy with priorities
to explore how to use AI productively

Companies needing step-by-step AI
integration for smarter decision-making

GenAI Sprint outcomes

01
Identified GenAI use cases with recommendations and team requirements
02
Personalized Proof of Concept with functionalities to co-build with AI
03
Report summarising our recommendations for Generative AI use cases to enable go/no-go decisions
04
Solutions tailored to your business context. See an example of an
Enterprise Generative AI tools comparison for a media publisher

How does it work?

01 Business workshops 1 day

Timeframe

1 day

Participants

Software architect, product researcher, business stakeholders

Needed

No specific preparations required; approach the workshop with open minds

Steps

Industry AI implementations applying to your company
Assessment of business benefits, cost implications, and risks
Discussion on the most beneficial generative AI strategy
AI use cases for your organization with an in-depth analysis of business values

02 Technology workshops 1 day

Timeframe

1 day

Participants

Software architect, data expert, business representatives

Needed

A completed technical information provided to TSH's team at least two days before the workshop

Steps

Technical analysis and exploration of available data from chosen use cases
Solution design
Roadblocks and potential 'quick-wins'

03 Report presentation 2 h

Timeframe

2 h

Participants

Software architect, data expert, your company's stakeholders and technical staff

Steps

A collaborative review of the findings
A discussion about the most promising solutions to introduce
Generative AI implementation framework
Q&A session

Deliverables

Sent within days of the meeting

Generative AI report

This report enables a go-or-no-go decision with recommendations for Generative AI process flow, use cases, tech analysis, team requirements, time and cost estimates, and risk assessment

Proof of Concept

A small PoC for a GenAI application illustrates its functionality

Example post-workshop report

Sprint's Creator

Darek Kocięcki

Generative AI Consultant

With his guidance and friendly demeanor, you'll address every AI-related problem with clear, actionable answers, making every sprint effortless and giddy excitement for future results.

2 weeks can have a big impact

A media publisher cut translation costs by 99% by recognizing AI possibilities

Our Lambda-based solution reduced costs from $200 (the dedicated translation service) to just $1.95.

OpusMT cut infrastructure costs, improved performance and accuracy.

Learn more

Consider other Sprints

Frequently Asked Questions

01 What's your AI philosophy and point of view on how to implement this technology?

If you want to do it, you need to do it well. Like with every IT solution, Artificial Intelligence needs proper treatment:

It is possible to reduce hallucination by using advanced prompt engineering,
Using the wrong prompts greatly impacts your bills and overall AI response quality.
AI is about improving the backend and the solutions' UX—something our Product Design team knows well.
AI development requires different skills and practices than building web apps.
You CAN build a prototype in a sprint.
You don't require a structured database to make good use of AI.
Build secure AI applications with full confidence that the AI model's creators will not access or use your data.
AI, machine learning, big data, and the cloud bring new business possibilities for a modernized app.

02 What kind of Artificial Intelligence projects have you completed?

Some of our recent cost optimization projects include:

You will find more information about our successful partnerships in the portfolio section. Wanna know what our partners think of us? Take a look at our Clutch reviews.

03 What if I need AI for my cloud? Do you do AWS Generative AI Workshop?

Absolutely, yes, the cloud is our house specialty!As an official AWS Advanced Partner, our teams include over 120 AWS-certified specialists (and that number is growing every month). We release around 30 AWS projects per year. Discover the full scope of what we solve with AWS.To give you an example, we have already worked on Large Language Models (LLM), both in the cloud (AWS Bedrock, OpenAI, Google Gemini AI, Google Bard) and self-hosted (Meta LLaMA).Various AI/ML services provided by AWS, including Amazon SageMaker, AWS DeepComposer, and AWS DeepRacer, are essential tools in our kit for building and deploying generative models.You’ll be in safe hands.