End-to-end AI services cover AI strategy and consulting, generative AI development, AI agents development, AI integration services, machine learning engineering, AI chatbot development, LLMOps and AI infrastructure, AI evaluation and quality engineering, AI safety and guardrails, and continuous AI operations. The best engagements also include an AI readiness assessment before any build work begins because decisions made in week one shape AI economics for years.

A focused AI proof-of-concept: $25,000-$75,000. A mid-complexity AI build (AI chatbot, RAG system, ML model): $75,000-$300,000. An enterprise AI platform built with multiple use cases, custom infrastructure and full MLOps: $300,000-$1.5M+. Ongoing AI operations and infrastructure typically run $10,000-$80,000 per month depending on scale. The number that matters isn't the build cost, it's the outcome the AI is producing. We provide detailed estimates after a readiness assessment.

A focused AI proof-of-concept: 6-10 weeks. A production AI feature (chatbot, copilot, RAG system): 3-6 months. A full enterprise AI platform with multiple use cases, custom infrastructure and full MLOps: 9-18 months, executed in phases. We provide milestone-based timelines in writing before AI development begins.

AI consulting answers "what should we build, why and how should it pay back?" readiness assessments, roadmaps, use-case prioritization and executive advisory. AI development answers "build it" production engineering of models, retrieval systems, agents and AI applications. Most serious AI engagements need both: consulting establishes what's worth building, development ships it. We deliver both as connected disciplines, not separate practices.

We're model-agnostic. OpenAI's GPT models, Anthropic's Claude, Google Gemini, Meta Llama, Mistral, Cohere and open-source models for self-hosted use cases. Model selection is part of the strategy we choose based on workload requirements, cost economics, data privacy constraints and latency profile, not based on which model we happen to resell. Many production systems run multiple models routed by use case.

AI compliance is engineered into the architecture from day one. PII redaction in prompts and outputs, data residency controls, audit logging for AI decisions, model isolation patterns where required, on-premise or VPC-isolated deployments for sensitive use cases, and compliance evidence collection for SOC 2, HIPAA, PCI-DSS, GDPR and the EU AI Act as relevant. Privacy isn't bolted on before the audit. It's architecturally enforced.

AI FinOps is operated as a continuous practice, not a quarterly review. Token-level cost monitoring, model routing for cost optimization (using cheaper models where they suffice), prompt efficiency engineering, semantic caching, batch processing where it fits and continuous spend anomaly detection. Most enterprises waste 30-50% of their AI budget on inefficiencies that competent engineering eliminates.

We measure what actually matters outcome metrics (resolution rate for support AI, conversion rate for sales AI, accuracy for prediction models), operational metrics (latency, cost per request, evaluation scores), business metrics (pipeline contribution, cost savings, time reduction) and quality metrics (hallucination rate, output accuracy, user satisfaction). Vanity metrics like "number of AI features shipped" don't pay for themselves.

Yes. We start with a technical audit model quality, evaluation rigor, cost posture, safety guardrails, observability maturity and the accumulated technical debt no one's documented. We give you an honest picture of what you've inherited and a clear remediation roadmap. Sometimes the answer is rebuild. Sometimes it stabilizes and hardens. Sometimes the existing work is better than it looks.

Three models: Dedicated AI Team (for long-term, multi-quarter AI roadmaps), Time & Material (for evolving scope and iterative AI exploration) and Fixed Cost (for well-scoped AI projects with defined deliverables). When you hire our AI development company, we recommend honestly based on your situation not based on which model is most profitable for us.

AI safety is engineered into the system, not reviewed at the end. Output validation, retrieval grounding through RAG, evaluation harnesses that detect hallucinations in testing, prompt injection defense, content filtering, PII redaction and the kind of guardrail engineering that responsible AI deployment actually requires. We design the system to fail safely and to fail visibly so issues surface in dashboards, not in customer complaints.

Yes. LLMOps and AI operations is one of our core offerings: continuous evaluation, model and prompt versioning, cost optimization, drift detection, safety monitoring, incident response and quarterly AI architecture reviews. You can hand over the operational load entirely, or run it alongside your in-house team. SLAs are defined and reported on every month.

Yes. AI integration is one of our core services integrating AI into Salesforce, HubSpot, SAP, Microsoft 365, Google Workspace, ServiceNow, internal data warehouses, legacy CRMs and the custom platforms your business depends on. Done with the engineering discipline that protects data privacy, respects existing access controls and gives your team the AI capability without ripping out what already works.