AI Development

AI readiness depends on three factors: clear business problems with measurable ROI, sufficient data (or the ability to collect it), and organizational willingness to adopt AI-driven decisions. We begin every engagement with a feasibility assessment—evaluating whether AI is the right solution, reviewing your data quality, and estimating realistic timelines and costs. Not every problem needs AI, and we’ll always advise when traditional software is a better fit.

Data requirements depend on the use case. Supervised learning needs labeled data (from thousands to millions of examples). Unsupervised learning can work with unlabeled data. LLM applications can use pre-trained models with minimal fine-tuning data. During discovery, we assess your existing data, identify gaps, and suggest collection strategies, labeling support, or synthetic data generation. Many AI projects succeed with far less data than expected.

Timelines vary by complexity. Simple ML models typically take 2–3 months. Computer vision or NLP systems take 4–6 months. Enterprise-grade AI platforms may require 6–12 months. After discovery, we provide a detailed project plan with milestones and regular demos. Most organizations start seeing ROI within 6–18 months of deployment.

We follow strict quality assurance practices: validation on held-out datasets, A/B testing against baselines, detailed error analysis, model drift monitoring, automated retraining pipelines, and continuous optimization. For regulated industries, we include explainable AI, bias testing, and compliance documentation. No model is deployed until it meets jointly agreed performance standards.

Security is integrated into every stage: encrypted data storage and transfer, strict access controls, secure model deployment, privacy-preserving techniques like federated learning and differential privacy, and compliance with GDPR, CCPA, and regional regulations. We support on-premise, private cloud, or air-gapped deployments for sensitive environments. Your security requirements guide our architecture.

Costs depend on project scope. Proof-of-concepts range from $50–150K, production systems $150–500K, and enterprise platforms $500K–2M+. Key cost drivers include data preparation, model complexity, integrations, infrastructure, and ongoing maintenance. We provide transparent estimates during discovery and offer phased delivery options. Most organizations achieve 3–10× ROI within 18–24 months.