AI Development Company
That Builds AI Systems for
the Real World

Three things that most AI development companies can't genuinely claim together: CMMI Level 5 process maturity (independently audited, not self-assessed), 10+ years of production AI and ML engineering experience across 35+ industries, and a verifiable delivery record of 700+ projects with documented business outcomes. We're also one of the fewAI development companies with a formal no-black-box AI policy — every model we ship is explainable, auditable, and designed from the start to meet the requirements of regulated enterprise environments in the USA, UK, Australia, Canada, and UAE.

Costs vary significantly based on project type, data complexity, integration requirements, and team composition. Here are honest estimates based on real project data: AI Proof of Concept (POC): $15,000 – $40,000 USD | 4–8 weeks Focused AIfeature (chatbot, recommendation engine,fraud model): $40,000 – $100,000 USD | 8–16 weeks Production AI system (fraud detection, demand forecasting, clinical AI): $80,000 – $250,000 USD | 12–24 weeks Full AI SaaS platform or enterprise AI transformation: $250,000+ USD | 6–12 months Contact our team for a no-obligation tailored estimate. UK clients can request GBP pricing; Australian and Canadian clients AUD and CAD pricing respectively.

Timeline depends entirely on scope, data readiness, and integration complexity. A focused AI integration using an existing API can be live in 2–4 weeks. A custom ML model with full data preparation, training, and production deployment typically takes 8–16 weeks. A full AI SaaS platform or enterprise AI system is usually 5–10 months from discovery to production launch. We work in two-week agile sprints throughout — meaning stakeholders see working software every fortnight, not just at the end of the engagement.

Yes — and we have active delivery relationships across five countries. UK clients work with us under GDPR-compliant architecture and can request GBP pricing. Australian clients are served with Australian PrivacyAct compliance built in, with AUD pricing available. Canadian enterprises receive PIPEDA-aligned AI systems with Canadian data residency options. UAE clients working within the AI Strategy 2031 framework, including Arabic-language NLP requirements, are a significant part of our current practice.

Yes, and compliance is an architecture requirement, not an afterthought. We've delivered HIPAAcompliant AI systems for healthcare clients, GLBA-aligned AI for financial services, and CCPAcompliant data pipelines for California-based consumer businesses. All infrastructure for US clients deploys to AWS US-East/West, Azure US, or GCP US regions with full data residency guarantees. Every engagement begins with a mutual NDA and data handling agreement before any access to client data occurs.

Traditional software follows deterministic, explicit logic — if X, then Y. AI software development creates systems that learn from data and improve with experience — making probabilistic decisions based on learned patterns rather than prescribed rules. This introduces entirely different engineering challenges: data quality management, model training infrastructure, accuracy evaluation frameworks, bias testing protocols, drift monitoring, and continuous retraining pipelines. None of these challenges exist in traditional software development. It's why the team composition at a genuine AI development company — ML researchers, data scientists, MLOps engineers alongside software engineers — looks fundamentally different from a standard software agency.

In most cases, yes. Our data engineering team integrates AI systems with existing databases, data warehouses, ETL pipelines, CRMs, and ERPs — including legacy systems. A full data infrastructure rebuild is rarely necessary and we don't recommend it unless there's a fundamental architectural issue that would constrain AI performance. We begin every engagement with a data audit that gives you an honest assessment of exactly what needs to change and what can stay as-is.

Yes — we treat explainability as a non-negotiable requirement for enterprise AI, particularly for clients operating in regulated industries in the USA, UK, and Australia. Every system includes SHAP value reports explaining individual model decisions, confidence score outputs on every prediction, model performance dashboards, full audit logs ofAI decisions, and human-in-theloop override mechanisms. When a regulator, auditor, or board member asks "why did the system make that decision?" — your team has a clear, documented, and legally defensible answer.

We've delivered production AI systems across 35+ industries globally, including: healthcare and MedTech (clinical AI, EHR intelligence, diagnostics), fintech and banking (fraud detection, credit underwriting, KYC automation), e-commerce and retail (recommendation engines, demand forecasting, dynamic pricing), manufacturing (predictive maintenance, computer vision quality control), logistics (route optimization, supply chain AI), real estate (property valuation, lead scoring), insurance (claims automation, underwriting AI), EdTech (adaptive learning, student performance prediction), media (content recommendation, moderation AI), and SaaS and technology (AI features, LLM copilots, intelligent analytics dashboards).

Book a Free AI Consultation — a 45-minute session with a senior Shanti Infosoft AI architect. We review your use case, assess your data readiness, identify the right AI approach, and give you a realistic scope, timeline, and cost estimate. No sales pressure. No commitment required. If we're a good fit, we'll outline an engagement structure. If we're not the right fit for your project, we'll tell you honestly and point you in the right direction. Book at shantiinfosoft.com/contact-us