TA: decision makers and decision making influencers in businesses
Checklist to define the need
Your company doesn’t use GenAI
Your company uses different GenAI tools with low to zero visibility and control
Your employees are concerned by GenAI adoption and each AI case creates friction points
You don’t measure GenAI developer velocity because you have both high-skilled and low-skilled developers in terms of AI usage
You’re concerned by data security and privacy of your GenAI workloads
Your business operations require consistent and visible manual human effort
Pain points that triggers our TA
Market & Competitors
You don’t use GenAI and afraid of loosing market to your competitors
GenAI Tool Choosing
You don’t know which GenAI tool is worth paying for in your particular situation
Return on investment
You’re concerned by ROI for your GenAI models and tools investments
Your needs
Free GenAI courses and trainings don’t answer how to apply it for your needs
Employee training
You don’t have knowledge and resources to educate your employees at scale
Quality of the code
Your developers use GenAI to generate code, but you’re frustrated by the quality of this code
Solutions description
AI Literacy training to any audience
Job role, skills, responsibilities, location, education and age are not important
Training for software engineers
How to generate, test and deploy high-quality and secure code
Training for quality assurance engineers
How to test GenAI applications, define and measure GenAI software quality metrics
Training for DevOps/MLOps
About building secured, scalable and cost effective AI infrastructure on AWS
Training for HR processes
How to leverage GenAI for HR processes in a particular company
Training for marketers
How to create marketing campaigns and generate text, photo and video using GenAI tools
Discovery for your GenAI
Project ideas, providing recommendations on how to achieve your business goals or solve business problems using GenAI
Architectural review of existing Gen AI
Architectural review of your existing GenAI workloads and providing detailed recommendations how to improve your architecture
Case 2: content creation for corporate social media
Before:
A team of 4 marketers responsible for the communication strategy for social media (LinkedIn, Instagram, Facebook, company blog), bi-weekly content plan for all platforms (2-3 publications per each platform per week), writing texts and scripts, taking pictures/video and posting content.
What we did:
GenAI automation for texts and scripts, experiments with generated pictures/video to enhance the original content and reach new audiences.
After:
Decreasing the original team to 2 marketers, increasing the amount of content to 3-5 publications per each platform per week, increasing views for the company content by about 15% at the cost of 44.99 Euro/month (23 Euro for ChatGPT plus and 21.99 for Google AI Pro subscriptions). 2 marketers from the original team switched to marketing research for new products.
Case 1: employee performance review process
Before:
An engineering manager and an HR of each reporting pool had to sit together, review all employee feedback and assign a performance grade to each employee. The HR had to create a personal development plan for each employee as a follow-up, review it with the manager and assign it to the employee. The process took 1-2 weeks for each reporting pool (up to 10 employees).
What we did:
Agentic feedback review and summarization and creation of the development plan draft using GenAI, the manager and HR review and feedback in the end.
After:
The new performance review process takes 3-5 days for each reporting pool now (up to 10 employees) at the cost of 23 Euro/month (ChatGPT Plus payment plan required by the automation workflow).
Case 3: GenAI for engineers
Before:
The service company who builds healthcare projects and introduces AI components in some of them was using several AI tools in parallel for development. The amount of AI-generated code was ~15% but the quality was “frustrating”. Development of AI components required multiple iterations because non-functional requirements were not communicated at the RFP stage because of imbalanced team skills.
What we did:
Tailored 2-month training for developers and DevOps on AI usage and implementation in customer-facing projects. Internal AI hackathon to practice new skills and validate some ideas.
After:
Unification of GenAI tools used for development and dropping the not required ones that helped to save ~35 Euro/month for each developer (3500 Euro/month for all developers). Increasing the amount of AI-generated code to ~25% with strict measurements of success metrics using GenAI-specific test coverage. For the customer-facing projects, the average build stage decreased by 2-4 weeks.
Our Partners
About subject matter expert
Professional Approach
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CEO and founder Rost CAMP Consultancy
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Solutions Architect at Booking.com (Amsterdam, The Netherlands)
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Guest Professor at SET University (Solution Architecture and Generative AI courses)
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Human-centric technical leader, manager, consultant and mentor with about 14 years of experience in different engineering roles
Main focus
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Solution architecture, Generative AI, AWS, Kubernetes, SDLC, team leadership, mentoring, growing a team
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AWS Community Builder (AI Engineering category)
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Full AWS Certified (14 active AWS certifications), holder of AWS Golden Jacket
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Certified in AI and Ml domain on AWS (AI practitioner, Machine Learning Engineer Associate, Machine Learning Specialty
