# Anovate.ai > Anovate.ai is an AI consulting and engineering company that designs, builds, integrates, and deploys production AI systems for businesses: product AI features, workflow automation, document intelligence, customer-facing assistants, computer vision, geospatial imagery analysis, finance operations AI, HR & internal enablement, and supply chain & logistics. The team works with clients across the US, Canada, and Spain, taking projects from a working prototype to a production system with monitoring, guardrails, and a runbook the client's own team can maintain. Anovate.ai is an AI engineering and delivery consultancy, not a SaaS product. Engagements are typically scoped, time-boxed builds (weeks, not quarters) that plug into a client's existing systems (cloud provider, CRM, ERP, data warehouse) rather than a new platform to adopt. Contact: info@anovate.ai. ## Company - [Homepage](https://anovate.ai/): overview of services, engagement models, and process. - [About](https://anovate.ai/about): company background. - [Careers](https://anovate.ai/careers): open roles. - [Contact](https://anovate.ai/contactUs): get in touch / book a discovery call. - [Privacy Policy](https://anovate.ai/privacy) ## Use cases Each page below is a standalone article describing one category of AI work Anovate.ai delivers, including what the work actually looks like and what the firm does at each step. - [Product AI capabilities](https://anovate.ai/use-cases/product-ai-capabilities): adding AI features into existing software products (e.g. "ask your data" chat, summarization, extraction) rather than building a new AI product. - [Workflow automation](https://anovate.ai/use-cases/workflow-automation): removing repetitive internal work with a clear input/output and low judgment requirement (routing, data entry, hand-offs between systems). - [Document intelligence](https://anovate.ai/use-cases/document-intelligence): extracting, classifying, and flagging structured data from invoices, contracts, claims, and scans, with confidence scoring and human review for exceptions. - [Customer-facing AI assistants](https://anovate.ai/use-cases/customer-ai-assistants): support and self-service assistants grounded in live product/account data, with confident handoff to a human agent. - [Computer vision systems](https://anovate.ai/use-cases/computer-vision): narrow-scope detection, inspection, and monitoring systems (defect detection, counting, safety-zone monitoring) deployed on edge devices, phones, or servers. - [Geospatial imagery analysis](https://anovate.ai/use-cases/geospatial-imagery): satellite, drone, and aerial imagery pipelines for monitoring at scale (crop monitoring, construction progress, roof surveys). - [Finance operations AI](https://anovate.ai/use-cases/finance-ops-ai): invoice matching, expense categorization, and reconciliation automation with confidence thresholds and audit trails. - [HR & internal enablement](https://anovate.ai/use-cases/hr-internal-enablement): CV screening and candidate ranking grounded in the role brief with bias controls, plus policy/onboarding Q&A grounded in current HR documents. - [Supply chain & logistics](https://anovate.ai/use-cases/supply-chain-logistics): demand/inventory forecasting and route optimization built on top of a business's existing planning process. ## Blog Long-form articles on applied AI engineering topics. Dates are publish dates. - [Preventing AI Hallucinations in Production LLM Systems](https://anovate.ai/blog/how-to-prevent-ai-hallucinations) (2026-05-12): trusted sources, validation, tool use, guardrails, and human review as the practical levers for reducing hallucinations in production LLM systems. - [Beyond SWE-Bench: How to Evaluate AI Coding Agents for Real Codebases](https://anovate.ai/blog/how-to-evaluate-ai-coding-agents-for-real-codebases) (2026-06-22): why the right question is not which model wins a benchmark, but how to evaluate a model + harness + workflow combination against a business's own codebase. - [AI Security Review Agents: From Static Scans to Context-Aware Risk Reports](https://anovate.ai/blog/ai-security-review-agents) (2026-07-05): a hybrid review pipeline pattern combining deterministic scanners, AI reasoning for context and remediation guidance, and human approval. - [Build vs. Buy: A Strategic Decision Guide for Enterprise AI](https://anovate.ai/blog/build-vs-buy-a-strategic-decision-guide-for-enterprise-ai) (2026-04-30): a 2026 framework for evaluating strategic differentiation, total cost of ownership, and an "Experiment, Extend, Evolve" implementation model. - [LLMs, RAG, Agents, and Agentic Workflows Explained](https://anovate.ai/blog/llms-rag-agents-and-agentic-workflows-explained) (2026-05-03): plain-English guide to the four pillars of modern AI orchestration and how they combine to deliver operational value. - [LLM Wikis: A Better Knowledge Base for AI Agents](https://anovate.ai/blog/llm-wikis-a-better-knowledge-base-for-ai-agents) (2026-04-29): why an LLM-maintained wiki can compound knowledge, preserve provenance, stay maintainable, and connect to tools over time. - [The Hidden Cost of Building Products with LLMs](https://anovate.ai/blog/the-hidden-cost-of-building-products-with-llms) (2026-04-28): the cost drivers that appear when moving an LLM feature from prototype to production scale, and how to plan for sustainable margins. - [Where AI Actually Works Today](https://anovate.ai/blog/where-ai-actually-works-today) (2026-04-29): where AI works reliably today, where it still needs human supervision, and how to pick safe, measurable pilot use cases. - [Why AI Benchmarks Don't Tell the Full Story](https://anovate.ai/blog/why-ai-benchmarks-dont-tell-the-full-story) (2026-04-21): why public AI benchmarks are useful for shortlisting models but don't prove production readiness against real workflows, data, cost, and latency. ## Optional - [Sitemap](https://anovate.ai/sitemap.xml): full machine-readable index of every page on the site, including individual use-case pages.