Most AI consulting stops at a strategy deck. Azati’s consultants are the engineers who build, integrate, and operate what they recommend – and stay accountable for accuracy, cost, and uptime after go-live.
Book an AI consultationAI consulting is advisory and engineering work that helps an organization identify high-value AI use cases, assess its data and infrastructure readiness, and build a roadmap to move from experimentation to production. Azati pairs that advisory work with the engineering team that delivers it - for a complete AI transformation. Instead of handing over a strategy deck, Azati builds, integrates, and operates the AI systems it recommends – and stays accountable for accuracy, drift, cost, and uptime after launch.
The bottleneck in enterprise AI is rarely the model. It’s everything around it – data quality, secure deployment, integration with the systems you already run, and keeping accuracy from drifting once real traffic arrives. Pure-advisory consulting hands you a roadmap and leaves at that boundary. Azati is built for the other side of it.
The Enterprise AI Readiness Diagnostic scores your organization across six pillars – with the 2026 data on why most enterprises aren’t agent-ready yet, plus a self-assessment scorecard you can fill in.
That inversion is why pilots demo well and never ship. Azati is built for the two layers where projects quietly die: the 20% – data engineering, integration, and legacy modernization that make AI viable – and the systems-and-operations slice of the 70% – workflow redesign inside your platforms, plus the MLOps, monitoring, and drift control that keep accuracy from decaying after go-live. The leadership, adoption, and culture side of the 70% stays inside your organization; our job is to make sure there’s a production-grade system worth adopting.
Framework: BCG, “10-20-70 rule” for AI transformation, 2025–2026.
The conventional view that AI delivers short-term gains but a long-term drag on revenue misses the larger opportunity. What AI really unlocks is work that never had a business case: application modernization and the revamping of legacy software products, because they finally change the economics of touching complex, undocumented systems.
We consult and deliver for tech leaders who are curious enough to challenge their own assumptions about what AI actually does. Leveraging our domain expertise across Banking & Finance, Insurance, Life Sciences & HealthTech, Energy & Oil & Gas, Manufacturing, Construction, Retail & Logistics, we speak the language of engineering to managers pursuing various purposes:
You need AI shipped on production-grade architecture – evaluated, observable, maintainable, not a science project that never reaches users.
You need AI that respects data residency and security: on-premise or EU-sovereign deployment, with no proprietary data leaving your perimeter.
You need senior engineering capacity for data readiness, modeling, RAG, evaluation, and drift control without building the whole team in-house.
You need AI that cuts real cost and cycle time on regulated workflows with a partner who owns the result after go-live.
Azati’s AI consulting follows the path your AI actually has to travel – assess what’s ready, build the right systems, integrate them with what you run today, and operate them in production. Each stage maps to a dedicated Azati service.
Most AI failure traces back to data, not models. We evaluate data quality, infrastructure, and use-case feasibility, then build the data foundation – the AI-engineering layer – that makes AI viable in the first place.
AI-ready Data EngineeringIdentify and prioritize high-impact use cases, score readiness across the organization, and produce an implementation roadmap with ROI expectations, architecture, and a defensible path from pilot to production.
Start with AI readiness assessmentCustom ML models, predictive analytics, computer vision, NLP, RAG, and fine-tuned LLMs – built for your data and your decisions, not benchmark datasets.
Advanced AI & Machine LearningFine-tuned, domain-specific LLMs (DSLMs), RAG over your knowledge, and on-premise or EU-sovereign deployment for regulated data. We adapt proven models – we don’t pretrain foundation models from scratch.
LLM Development ServicesAutonomous, tool-using agents and multi-agent workflows built for traceability, human-in-the-loop control, and integration with your systems.
Agentic AI EngineeringEngineering-drawing digitization, intelligent document processing, digital twins, and visual inspection embedded into existing operations – not isolated AI models.
Computer Vision & OCRProduction-grade AI MVPs for venture-backed founders – idea to first users in 6–8 weeks, built to scale rather than rewrite. Python and Ruby engineering for AI-first products.
AI MVP Development & Rapid PrototypingEmbed AI into ERP, CRM, data warehouse, and custom platforms as a native capability – complete business workflows, not a bolt-on demo.
AI Integration ServicesMake legacy systems AI-ready: untangle the data, modernize the architecture, and connect AI to the platforms you can’t replace.
Legacy Modernization for AIRescue, harden, and take ownership of AI-generated codebases so they can be safely scaled, secured, and maintained in production.
Vibe-Coding EngineeringBuild-and-operate: MLOps, monitoring, automated retraining, and drift control – run as a managed service for teams with no in-house MLOps. We stay on the hook for the numbers.
Managed AIThree engagements, three industries, one common thread — they all went to production and stayed there.
Five AI agents across the full software development lifecycle, running the AI-augmented delivery pipeline for a 12,000-employee insurer.
Digitizing a 35,000-file PEFS archive and converting it to the DEXPI industry standard for process engineering.
Centralized ingestion, normalization, and governance of athlete and event data for a global sports organization.
NLP over physician responses surfacing actionable market insights and sharper campaign targeting for pharmaceutical marketing.
Edge-AI vision on NVIDIA Jetson replacing manual pallet counts on the warehouse floor.
LLM- and vector-search-driven ingestion, enrichment, and semantic discovery across patent documents and biological sequences.
Azati’s engineering delivery approach includes top-notch engineering culture, responsiveness, proactivity, AI speed, and human-governed quality.
Most importantly, it covers production ownership. We don’t end the engagement at a recommendation or a sprint demo – we stay responsible for the outcome in production: accuracy, drift, cost-per-query, and uptime, including for clients with no internal MLOps team. That is the line between consulting that produces slideware and consulting that produces a working system.
Tell us where you are – a stalled pilot, a strategy deck no one can execute, or a clean start – and we’ll map the path to production.
Book an AI consultationAI consulting helps an organization identify high-value AI use cases, assess data and infrastructure readiness, and build a roadmap from experimentation to production. Azati pairs the advisory work with the engineering team that delivers and operates the result.
AI consulting is the advisory layer – strategy, readiness, and roadmap. AI transformation is the full journey that follows: building, integrating, and operating AI inside core operations. Azati covers both, end to end.
Most consultancies stop at a recommendation. Azati’s consultants are engineers who build, integrate, and operate what they advise, and stay accountable for accuracy, drift, cost, and uptime in production including for clients with no in-house MLOps team.
Both. Engagements range from a scoped proof of concept and a dedicated AI team to a full build-and-operate model where Azati runs the AI in production and stays responsible for the numbers.
Yes. With an EU engineering center in Warsaw, Azati deploys open-source models on your own GPUs or on EU-sovereign infrastructure, so sensitive data never leaves your perimeter – keeping you aligned with EU data-residency and EU AI Act expectations.
A readiness assessment and roadmap is typically a fast, fixed-scope engagement. A scoped proof of concept runs in weeks; an AI MVP ships in 6–8 weeks; larger transformation programs run as dedicated-team or build-and-operate engagements.
Continuous evaluation against golden datasets, drift monitoring, automated retraining, cost-per-query benchmarking, and full execution traces for audit – the operating layer that keeps accuracy from quietly decaying after go-live.
Banking & Finance, Insurance, Life Sciences & HealthTech, Energy & Oil & Gas, Manufacturing, Logistics, and high-tech product companies – with deep experience in regulated, document-heavy, and legacy-bound environments.
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