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Artificial Intelligence & Machine Learning Services

Bring intelligence to your software with AI/ML.

OVERVIEW

The rapid rollout of AI adoption
- Businesses can not afford to ignore

AI is transforming businesses of all sizes, from start-ups to global enterprises. At Adamo, we view AI not as an add-on, but as the foundation of the next wave of enterprise transformation. As organizations accelerate AI adoption at scale, we help businesses to design AI/ML strategies and create tailored AI and ML solutions that embed intelligence into existing systems, solve real-world challenges, and deliver measurable impact.

SERVICES

Our AI and ML services turn your AI demands
into breakthrough results

01/06

Custom AI software

We turn your ideas into intelligent, high-performance solutions with our custom AI development services. From rapid prototyping and feasibility checks to full deployment, we create tailored AI products designed to integrate smoothly and drive meaningful impact across your business through automation, predictive insights, personalization and advanced analytics.

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custom AI software development

02/06

Generative AI solution

We empower your business with Generative AI solutions that use advanced language models to create text, visuals, and code instantly. We leverage AI algorithms to create human-like interactions, generate dynamic content, and automate the adoption of cutting-edge technologies.

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generative AI solutions development

03/06

Machine Learning development

Our machine learning development services are backed by our strong foundation in ML, natural language processing (NLP), cloud infrastructure, and data. We infuse machine learning algorithms into your business workflow through effective AI implementation to automate complex processes, discover patterns and predict outcomes.

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machine learning development service

04/06

LLM development

We develop enterprise-grade large language model (LLM) solutions fine-tuned with your domain-specific data, both structured and unstructured, to deliver outputs that align with your brand and business goals. Adopting advanced models like GPT, Llama, Claude, RAG…, we enhance AI performance, build resilient systems, and integrate them into your workflows for measurable value and efficiency.

LLM development solution

05/06

Agentic AI development

Our agentic AI solutions empower intelligent agents to think, plan, and act independently, driving smarter decisions and greater operational autonomy. Our agentic AI solutions span from collaborative copilots that assist users to fully autonomous systems capable of independent decision-making with minimal human oversight.

agentic AI development solution

06/06

AI Agent development

We create advanced AI agents that go beyond traditional automation to optimize complex workflows and enhance customer interactions while unifying data-driven insights and reinforcing enterprise security. Our solutions are purpose-built and seamlessly integrated to deliver intelligent, ethical, and high-performing outcomes that drive growth across industries.

AI agent development solution

Manual processes slowing you down?

From planning to deployment, we handle every aspect of your AI and ML journey to automate your process and cut down working hours. Team up with Adamo to build your AI & ML solution.

Let’s discuss your idea
Adamo Software team

PROCESS

Our approach helps you at every stage of your AI journey

Problem
Identification

Our AI experts work with you to understand your goals - automation, cost reduction, or better customer experience and define the core problem.

Data Collection &
Preparation

We collect, clean, and structure relevant data, removing errors and inconsistencies to ensure high-quality input that directly impacts model accuracy and insights.

AI-Based Model
Development

Our developers select the most suitable algorithms and build the AI model. We iteratively refine and optimize it to ensure accurate predictions and strong performance.

Model
Evaluation

The model is tested using fresh data to validate performance. If necessary, we fine-tune its architecture and parameters until it consistently delivers the expected results.

Integration with Existing
Systems

Once validated, the AI model is securely integrated into your existing platform or workflows, ensuring smooth operation, scalability, and user accessibility.

Monitoring &
Maintenance

After deployment, our team continuously monitors performance, updates the model with new data, and improves it over time to maintain accuracy and reliability.

INDUSTRIES

We adopt AI across different industries

Travel & Hospitality

Healthcare

Food & Beverage

Logistics

E-commerce & Retail

Finance

AI is redefining how travelers plan, experience, and share their journeys. From intelligent itinerary scheduling and personalized recommendations to destination management and enhanced customer support, our AI solutions empower travel companies to deliver more connected, convenient, and memorable experiences.

  • Travel personalization
  • AI recommendation engines
  • Flight ticket price forecast and dynamic pricing
  • Virtual travel assistants and AI chatbots

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AI in travel and hospitality

We develop compliant AI solutions that enhance patient experiences and operational efficiency across the entire healthcare ecosystem, from hospitals to research labs. Our solutions enable early disease detection, personalized treatment, and a more connected flow of health data.

  • Virtual medical assistants
  • Real-time alerts for high-risk cases
  • AI diagnostic and imaging solutions
  • Real-time patient data dashboards and analytics

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AI in healthcare

We design advanced AI solutions that drive transformation across the F&B value chain, from production to point of sale. Our technologies help businesses forecast demand accurately, optimize operations, control food quality and deliver exceptional customer experiences.

  • Food personalization and recommendation systems
  • Smart AI customer assistants
  • AI-powered food quality assurance and safety monitoring
  • AI-enabled inventory and supply chain optimization solutions

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AI in food and beverage

AI is transforming the logistics industry, making operations smarter, faster, and more connected. From predictive planning and intelligent routing to real-time tracking and automated warehouse management, our AI solutions help logistics providers improve efficiency, accuracy, and coordination across the supply chain.

  • Real-time monitoring of delivery routes and fleet performance
  • Predictive maintenance for warehouse equipment, vehicles, and other assets
  • Automated product inspection using computer vision
  • Automated supplier communications, including payment reminders and invoice sharing

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AI in logistics

We integrate AI into retail systems to enhance customer experiences and improve operations across online and offline channels. Our solutions deliver demand forecasting, personalized recommendations, real-time inventory management, and dynamic pricing for a smarter, more responsive shopping experience.

  • Personalized product recommendation systems
  • AI shopping assistants
  • Demand forecasting for inventory management
  • Dynamic pricing optimization for products

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AI in ecommerce and retail

We build AI solutions that transform finance and banking by enhancing decision-making, protecting assets, and delivering personalized experiences across digital platforms. Our technologies detect risks in real time, optimize investments, and turn financial insights into measurable business value.

  • Predictive analytics software
  • Real-time risk analytics
  • Automated financial reporting
  • Personalized investment & wealth recommendations

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AI in finance

OUTCOME

We know your data, your workflow and how to make AI work for you

Your problems might be

iconWhat Adamo offers

iconUnsure how to start with AI or what’s realistic?

Strategic AI consulting – We design realistic AI strategies, choose the right models, and build validation frameworks to reduce risk and ensure ROI.

iconManual processes slowing your operations?

Custom AI development – We build tailor-made AI automation to streamline operations and deliver measurable efficiency.

iconData everywhere but insights nowhere?

AI & MLOps – We create clean pipelines, automated training, and model monitoring to turn your data into actionable insights.

iconConcerned about privacy, ethics, or compliance?

Ethical & compliant AI – We embed transparency, governance, and explainability into every solution to keep your system safe and trustworthy.

CASE STUDIES

All cases

Real stories of AI in action

TECH STACK

Our AI development tools

Data engineering
Spark
Airflow
ClickHouse
Kafka
PostgreSQL
Deep learning frameworks
TensorFlow
Keras
Caffe
NLP
Gemma
Falcon
LLaMA 2
Cloud providers & Computer vision
AWS
Azure
Stable Diffusion XL

RELATED TECHNOLOGIES

AI-related technologies

machine learning technology

Machine Learning

We deliver machine learning solutions to analyze data, forecast trends, and improve decision-making.

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big data technology

Big Data

Adamo turns big data into actionable insights to forecast demand, uncover revenue opportunities, and act in real time.

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FAQ

Frequently Asked Questions

Should I build a custom AI model or use existing APIs like OpenAI or Claude?

This is one of the first decisions in any AI project. The answer depends on three factors: data sensitivity, control requirements, and cost at scale.

 

Use external APIs (OpenAI, Claude, Vertex AI) when: speed to market matters more than long-term cost; your use case is general (writing assistance, summarization, classification); volume is moderate (under millions of calls per month); and you’re comfortable sending data to third parties.

Build or fine-tune custom models when: data cannot leave your infrastructure (healthcare, finance, defense); you need consistent behavior at scale where API costs become prohibitive; your domain is specialized (medical imaging, legal documents, technical documentation); or you need very low latency or offline capability. For these cases, we typically use TensorFlow or Scikit-learn for training, then deploy on SageMaker, Vertex AI, or Azure ML.

 

A hybrid approach is often optimal: prototype quickly with API providers, then transition critical workloads to custom models once usage patterns are clear. We help clients evaluate this trade-off during discovery, including cost projections at projected scale.

Data privacy in AI projects is more complex than traditional software because data is consumed during training, not just stored. Our standard practices:

 

During development

  • Training data stays in your infrastructure or in isolated environments we provision. We don’t move sensitive data to developer laptops.
  • Signed NDAs with every team member and with Adamo as a company.
  • Role-based access — only assigned engineers see project data.
  • Code reviews and access logs on every commit and data access.

 

During deployment

  • Models can be deployed on your cloud account (AWS SageMaker, Azure ML, Google Vertex AI) or on-premises.
  • Encryption at rest and in transit as standard.
  • Audit trails for all inference requests.

 

For regulated industries (healthcare HIPAA, finance PCI DSS/SOC 2, EU GDPR), we layer in additional controls: BAA agreements, data residency commitments, differential privacy, and federated learning where applicable. We discuss the specific compliance regime during discovery to set the right architecture from day one.

Our AI projects follow a phased approach that differs from traditional software because results are uncertain until you see them:

 

Phase 1 — Discovery (1-2 weeks): Define the business problem, success metrics, and constraints. Audit your existing data — quality, volume, labels. Estimate feasibility.

Phase 2 — Data preparation (2-6 weeks): Data cleaning, labeling (if needed), feature engineering, baseline establishment. This is often the longest phase and frequently underestimated.

Phase 3 — Model development (4-12 weeks): Build candidate models using TensorFlow, Scikit-learn, or LLM-based architectures with LangChain/LlamaIndex for RAG. Evaluate against metrics defined in Phase 1, iterate. We deliver an evaluation report comparing approaches.

Phase 4 — Integration and testing (2-4 weeks): Connect the model to your application, build API endpoints, load testing, edge case handling.

Phase 5 — Deployment and monitoring (1-2 weeks initial, then ongoing): Production deployment on SageMaker, Vertex AI, or Azure ML, with monitoring for model drift, performance degradation, and unusual inputs.

 

Total timeline: 3 to 7 months for production AI systems. We share findings at each phase so you can pivot or stop if the technical risk is higher than expected — this is genuinely possible in AI work, unlike traditional development.

Honest answer: we don’t guarantee specific accuracy numbers upfront, and you should be skeptical of any AI vendor that does. AI model performance depends heavily on training data quality, the inherent difficulty of the problem, and ongoing data distribution stability.

 

What we commit to:

  • Establishing baseline metrics during discovery (target precision, recall, F1, latency, business KPIs).
  • Reporting actual measured performance at the end of model development.
  • Providing an honest assessment of whether targets are achievable with current data.
  • Recommending data collection or labeling improvements if needed.

 

Common metrics we report depending on the use case: classification accuracy/precision/recall/F1, regression RMSE/MAE, ranking NDCG/MRR, LLM evaluation rubrics (helpfulness, accuracy, safety), inference latency at p50/p95/p99.

 

If your data is insufficient for your target performance, we’ll tell you before starting, not after. This sometimes means recommending a smaller pilot first, or postponing the project until data is improved.

AI systems require more active post-launch attention than traditional software because they degrade silently. Without action, model performance drops as your data distribution shifts.

 

Continuous monitoring

  • Inference latency and throughput tracked through SageMaker Model Monitor, Vertex AI Model Monitoring, or Azure ML monitoring depending on your platform.
  • Input data distribution shift detection.
  • Output anomaly detection.
  • Business metric tracking — whatever the AI is supposed to improve.

 

Retraining (typically quarterly or trigger-based)

  • Refresh the model with recent data using TensorFlow or Scikit-learn pipelines.
  • Re-evaluate against current production traffic.
  • A/B test new model versions before full rollout.

 

Foundation model updates (for LLM-powered systems)

  • New model releases from providers (GPT, Claude, Gemini updates).
  • Prompt evaluation and regression testing — LangChain has built-in evaluation tooling we use.
  • Cost optimization as model pricing changes.

 

AI maintenance contracts typically run 12 to 36 working days per year depending on system complexity. We provide quarterly health reports and recommend retraining schedules based on observed drift. For high-stakes systems (medical, financial), we recommend more aggressive monitoring with paged on-call coverage.

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