Custom AI
Solutions
Contact us to see if one of our Custom AI Solution packages is right for your larger business.
(CUSTOM SOLUTIONS)
Artificial Intelligence (AI)
The field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, creation, and image recognition. The goal with AI is to create self-learning system that derives meaning from data.
(What Needs solved?)
1) Business Goal & Problem Definition
Identifying what needs solving (e.g., prediction for ML or creation/personalization for GenAI) and the expected value (e.g., revenue lift, efficiency gains). Key common steps include:
- Defining measurable success criteria tied to objectives (e.g., KPIs like accuracy rates or productivity metrics) to enable evaluation and iteration.
- Aligning stakeholders for consensus on goals, scope, and outcomes.
- Assessing organizational readiness (e.g., data, infrastructure, skills) and a feasible path to production. This ensures initiatives are ROI-driven rather than tech-led, avoiding wasted effort on unsuitable problems.
1A.
Machine Learning (ML)
A branch of AI and computer science that focuses on use of data and algorithms to imitate the way humans learn. It gradually improves its accuracy to build computer systems that learn from data. Machine learning models are trained by using large datasets to identify patterns. and
make predictions.
2) Data Collection & Preparation
Gather, clean, label, and preprocess data (EDA, feature engineering).
3) Model Development
Select algorithms, train models, perform hyperparameter tuning.
4) Model Evaluation
Assess performance using metrics (accuracy, F1, AUC, etc.) and bias checks (SageMaker Clarify).
5) Model Deployment
Host the model for inference (real-time endpoints, batch transform, serverless).
6) Model Monitoring
Detect drift, degradation, and bias in production (SageMaker Model Monitor).
7) Iteration & Retraining
Feed monitoring insights back to retrain or update the model (SageMaker Pipelines for automation).
1B.
Generative AI
(GenAI)
Multipurpose technology that helps to generate new original content rather than finding or classifying existing content. Generative AI focuses on creating new content, such as text, images, audio, video, and even code. The models of generative AI learn patterns and representations from a large amount of training data. They then use that knowledge to generate outputs that resemble the training data.
2) Model Selection
Define the GenAI problem and select a pre-trained foundation model (Bedrock Model Evaluation).
3) Data Preparation
Curate, chunk, and prepare data for RAG, fine-tuning, or prompts (governance, PII checks).
4) Customization
Adapt the model using prompt engineering, RAG (Knowledge Bases), fine-tuning (SFT, PEFT/LoRA), continued pre-training, or agents.
5) Evaluation
Assess quality, safety, bias, and hallucinations (Bedrock Model Evaluation, human eval, LLM-as-a-judge, red-teaming).
6) Deployment
Serve the customized model (on-demand, provisioned throughput, batch, agents).
7) Monitoring & Governance
Track hallucinations, drift, guardrail violations, cost, and user feedback (Bedrock Guardrails, CloudWatch).
8) Iteration & Refresh
Update prompts, refresh RAG data, re-fine-tune, or switch to newer FM versions based on performance.
Machine Learning (ML) Use Cases
Predictive Inventory Management
A small retail store uses ML algorithms to forecast demand based on historical sales data, weather patterns, and local events, optimizing stock levels to reduce overstock and stockouts.
Customer Segmentation
A boutique clothing shop employs ML to group customers by purchasing behavior, demographics, and preferences, enabling targeted marketing campaigns.
Fraud Detection
An online artisan marketplace implements ML models to identify suspicious transactions in real-time, protecting against credit card fraud.
Recommendation Systems
A local bookstore uses ML to suggest books to customers based on past purchases and browsing history, increasing sales.
Price Optimization
A small e-commerce site for handmade goods applies ML to dynamically adjust prices based on competitor pricing, demand, and inventory.
Credit Risk Assessment
A micro-lending business uses ML to evaluate loan applications more accurately than traditional methods.
Supply Chain Optimization
A small manufacturer optimizes supplier selection and timing with ML models.
Energy Consumption Prediction
A small office building uses ML to forecast energy usage, helping to cut utility bills through better scheduling.
Voice Recognition for Customer Service
A small call center integrates ML for voice analysis to route calls efficiently.
Route Optimization
A local delivery service uses ML to optimize delivery routes, reducing fuel costs and time.
Generative AI
(GenAI) Use Cases
Content Generation for Social Media
A coffee shop uses GenAI to create engaging posts, captions, and hashtags tailored to their brand voice.
Chatbots for Customer Service
An online boutique deploys a GenAI-powered chatbot to handle inquiries, provide recommendations, and process simple orders 24/7.
Product Description Writing
A handmade jewelry store employs GenAI to generate compelling, SEO-optimized descriptions for new items.
Image Generation for Ads
A pet store generates custom images of products in various settings using GenAI for marketing campaigns.
Code Generation for Custom Tools
A small web design firm uses GenAI to quickly prototype code snippets for client websites.
Virtual Assistants for Scheduling
A freelance photographer employs GenAI to manage appointments and respond to client emails intelligently.
Video Script Creation
A small video production company generates scripts for promotional videos using GenAI.
Translation Services
A tourism business employs GenAI for real-time translation of marketing materials into multiple languages.
Report Generation
A consulting business uses GenAI to auto-generate client reports from data inputs.
FAQ Generation
An e-commerce site uses GenAI to create and update comprehensive FAQs based on customer queries.
