The stack for enterprise intelligence
Three layers, bottom-up - nothing above works without the foundation below.
Autonomous Execution
Agentic AI that acts rather than reports - generative and agentic workloads running real processes end to end.
Compute & Model Layer
Data science and AI/ML, LLMs and RAG - models trained, served, and observed with MLOps.
Data Foundation
Data engineering, quality, and integration - the governed foundation every layer above depends on.
A model for every stage of the journey.
Everything AI delivery needs - operationalized for you.
Frequently asked questions.
What does Inspira's AI & Data Analytics practice deliver?
Three pillars across the AI lifecycle: Advisory (maturity, governance, and AI-readiness roadmaps), Transformation (data engineering, data science, generative and agentic AI), and Operations (MLOps, model observability, and AI security) - from data foundation to autonomous execution.
Why do so few AI projects reach production?
88% of enterprises use AI somewhere, but only 7% have scaled it. Five barriers block production: pilot purgatory (88% of PoCs never reach production), an AI-to-strategy translation gap (50%+ can't bridge it), no ROI patience as cloud/GPU costs cut funding early, a governance gap where agentic AI lacks safety frameworks, and data debt where silos and quality drive 95% of failures. We engineer around each of those barriers, production-first (source: McKinsey).
How do you engage - do we have to commit big up front?
Three models fit every stage: a POC dip-stick (8-12 weeks) with defined success criteria, an immersive project (32-52 weeks) deploying 3-4 high-priority use cases, and a collaborative Center of Excellence (3+ years) for continuous adoption.
How do you keep AI governed and safe?
A Data & AI governance wrapper runs across everything - cataloging, MDM, data masking, and audit trail - plus AI security & compliance monitoring, PII-leakage testing, and model observability with drift and hallucination tracking.
Can you show real outcomes?
Yes - an 18-month AI roadmap for a Middle-East petrochemicals & solvents producer mapped 75+ AI opportunities across 14 processes in just 8 weeks through 50+ stakeholder interviews; and Gen-AI engagements span government citizen-grievance bots (NLP sentiment, multilingual voice), telecom SDLC automation (Gen-AI JIRA breakdown), and AI customer engagement for 42M+ banking customers.