GenAI integration, LLM workflows, and AI strategy that turns innovation into revenue.
AI is moving from experiment to expectation. Your competitors are already using generative AI to automate support, accelerate content creation, and personalize user experiences. But integrating LLMs is not as simple as calling an API — it requires thoughtful architecture, data pipelines, safety guardrails, and continuous monitoring.
We help you cut through the hype. Our AI consulting team designs pragmatic AI solutions that integrate into your existing product, respect your budget, and deliver measurable ROI. From proof of concept to production deployment, we bring the technical depth and product sense to make AI work for your business.
Integrate OpenAI GPT-4, Claude, Llama, and Amazon Bedrock into your product. We build retrieval-augmented generation (RAG) pipelines, fine-tune models on your data, and design safe, cost-effective inference architectures.
Not every problem needs a neural network. We help you identify high-ROI AI use cases, build a phased roadmap, and avoid the trap of AI for AI's sake. Strategy first, implementation second.
From medical imaging analysis to quality inspection on manufacturing lines, we build custom computer vision models and ML pipelines using PyTorch, TensorFlow, and AWS SageMaker.
Build intelligent assistants, copilots, and autonomous agents that understand context, take action, and integrate with your existing APIs and databases.
Clean, label, and pipeline your data for model training. We build ETL/ELT pipelines, vector databases, and embedding stores that feed your AI systems with high-quality, up-to-date data.
Deploy models to production with monitoring, A/B testing, and automated retraining. We use AWS SageMaker, ECS Fargate, and Lambda to serve models at scale with sub-second latency.
We evaluate models across four dimensions: capability (can it perform the task?), cost (token pricing and infrastructure), latency (response time requirements), and privacy (can we self-host?). We then prototype with 2–3 candidates and measure real-world performance on your data before committing.
Yes. Most of our AI work is integrating intelligence into existing applications, not building AI products from scratch. We design APIs, embeddings pipelines, and UI components that slot into your current architecture with minimal disruption.
We use a multi-layer approach: retrieval-augmented generation to ground outputs in verified data, prompt engineering with structured output constraints, human-in-the-loop review for critical decisions, and automated monitoring for drift and anomalies.
We start with a 2–4 week discovery phase to map use cases, audit data readiness, and build a proof of concept. From there, we scope a phased implementation that typically runs 3–6 months for production-ready AI features.
Yes, when fine-tuning or domain adaptation is necessary. We have experience fine-tuning LLMs, training custom computer vision models, and building domain-specific embeddings. However, we always start with off-the-shelf models — they are often good enough and dramatically cheaper.
Connect with Guy Shahine (CEO) and book your free strategy session now.
GenAI integration, LLM workflows, and AI strategy that turns innovation into revenue.
AI is moving from experiment to expectation. Your competitors are already using generative AI to automate support, accelerate content creation, and personalize user experiences. But integrating LLMs is not as simple as calling an API — it requires thoughtful architecture, data pipelines, safety guardrails, and continuous monitoring.
We help you cut through the hype. Our AI consulting team designs pragmatic AI solutions that integrate into your existing product, respect your budget, and deliver measurable ROI. From proof of concept to production deployment, we bring the technical depth and product sense to make AI work for your business.
Integrate OpenAI GPT-4, Claude, Llama, and Amazon Bedrock into your product. We build retrieval-augmented generation (RAG) pipelines, fine-tune models on your data, and design safe, cost-effective inference architectures.
Not every problem needs a neural network. We help you identify high-ROI AI use cases, build a phased roadmap, and avoid the trap of AI for AI's sake. Strategy first, implementation second.
From medical imaging analysis to quality inspection on manufacturing lines, we build custom computer vision models and ML pipelines using PyTorch, TensorFlow, and AWS SageMaker.
Build intelligent assistants, copilots, and autonomous agents that understand context, take action, and integrate with your existing APIs and databases.
Clean, label, and pipeline your data for model training. We build ETL/ELT pipelines, vector databases, and embedding stores that feed your AI systems with high-quality, up-to-date data.
Deploy models to production with monitoring, A/B testing, and automated retraining. We use AWS SageMaker, ECS Fargate, and Lambda to serve models at scale with sub-second latency.
We evaluate models across four dimensions: capability (can it perform the task?), cost (token pricing and infrastructure), latency (response time requirements), and privacy (can we self-host?). We then prototype with 2–3 candidates and measure real-world performance on your data before committing.
Yes. Most of our AI work is integrating intelligence into existing applications, not building AI products from scratch. We design APIs, embeddings pipelines, and UI components that slot into your current architecture with minimal disruption.
We use a multi-layer approach: retrieval-augmented generation to ground outputs in verified data, prompt engineering with structured output constraints, human-in-the-loop review for critical decisions, and automated monitoring for drift and anomalies.
We start with a 2–4 week discovery phase to map use cases, audit data readiness, and build a proof of concept. From there, we scope a phased implementation that typically runs 3–6 months for production-ready AI features.
Yes, when fine-tuning or domain adaptation is necessary. We have experience fine-tuning LLMs, training custom computer vision models, and building domain-specific embeddings. However, we always start with off-the-shelf models — they are often good enough and dramatically cheaper.
Connect with Guy Shahine (CEO) and book your free strategy session now.
