Design, build, and deploy production-grade AI systems — from intelligent automation and custom LLM integrations to AI agents and predictive analytics — delivered 3× faster than traditional IT outsourcing.
Healthcare Client — Week 8
Most enterprises fail at AI — not because it doesn't work, but because generic tools and poorly scoped implementations fail to deliver measurable ROI.
"We spent 18 months on an AI pilot and still aren't in production."
"Our data science team is brilliant, but we have no AI engineering bench."
"We bought an AI platform but can't integrate it with our legacy systems."
"Our AI vendor doesn't understand our industry's compliance requirements."
"We have no visibility into what our AI is doing or how it performs."
At Aeologic, we close the gap between AI ambition and AI execution. We bring certified AI engineers, proven deployment pipelines, and enterprise-grade security practices to every engagement — so your AI systems go live, and stay live.
Our average time-to-production is 10 weeks — for real, working AI systems that move your KPIs, not slide decks or demos.
Start with a Free Assessment →Six core service lines, each designed for enterprise-scale deployment, security, and speed-to-production.
Build custom AI solutions from scratch including RAG systems, AI agents, LLM integrations and fine-tuned models — using the right model for your specific use case and constraints.
Deploy pre-vetted, senior AI engineers directly into your organization — on-site or remote — who work as a true extension of your internal team from Day 1.
Get AI systems into production reliably and at scale. We establish the infrastructure, pipelines, and monitoring to keep AI performing in the real world — not just in demos.
AI introduces new vectors for data exposure and regulatory risk. We build security into every layer of your AI stack — before a single line of code goes to production.
Before you invest in AI development, get clarity on where AI will genuinely move the needle — and where it will distract from higher-priority work. Hard decisions, not slide decks.
The fastest path to AI ROI is automating the repetitive, high-volume processes that consume your team's time. Average efficiency gain: 40–80% reduction in manual processing time within 90 days.
Measured outcomes across 50+ enterprise deployments in healthcare, finance, manufacturing, and more.
AI Projects Successfully Delivered
Average Time-to-Production (Weeks)
Average Operational Cost Savings
Uptime SLA on Deployed Systems
A Fortune 500 health system cut medical documentation time by 80% with an AI-powered clinical notes system — without disrupting existing EHR workflows.
A global fintech firm achieved 99.2% fraud detection accuracy across 10M+ monthly transactions — recovering $4.2M in the first quarter alone.
Predictive maintenance AI across 50+ factories reduced unplanned downtime by 45%, integrated with existing IoT infrastructure and zero disruption to operations.
A top-10 law firm reduced contract review time from 4 hours to 18 minutes using RAG-powered document analysis, handling 300+ contracts per week automatically.
Our four-phase delivery model eliminates the two biggest failure modes in enterprise AI: scope creep and production paralysis.
Stakeholder interviews, data infrastructure review, technical constraint mapping, and KPI definition. Output: a signed scope document with clear deliverables.
System architecture, model selection, integration mapping, and a detailed technical specification reviewed by your team before any code is written.
Development in 2-week sprints with working demos after each. You see progress constantly. Feedback loops are short. Scope changes are managed with impact assessments.
Production deployment, monitoring dashboards, performance validation, and 90-day post-launch support. Full knowledge transfer to your internal team included.
Faster Than Traditional IT Outsourcing
Clients Receive Working Software, Not Presentations
Post-Deployment Support Included at no extra cost
What enterprise leaders say after working with our AI engineering teams.
" Aeologic deployed a team of AI engineers who built a medical documentation system that reduced paperwork by 80%. Delivered in just 8 weeks. The integration with our existing EHR was seamless.
CTO, Fortune 500 Healthcare Company
" Their AI-powered fraud detection system processed 10M+ transactions with 99.2% accuracy. Incredible ROI within the first quarter. The team truly understands financial services compliance.
VP Engineering, Global Financial Services
" We implemented predictive maintenance AI across 50+ factories, reducing downtime by 45%. The team integrated seamlessly with ours — it felt like they worked for us, not a vendor.
Director of Operations, Industrial Manufacturer
Everything enterprise decision-makers ask before working with us — answered directly and thoroughly.
Enterprise AI solutions are custom-built artificial intelligence systems designed to solve specific business problems at organizational scale. Unlike consumer AI tools, they integrate with existing business systems (ERP, CRM, data warehouses), meet enterprise security and compliance requirements, and handle the data volumes and uptime standards of large organizations. Examples include AI-powered document processing, predictive analytics platforms, and intelligent workflow automation.
Focused AI automation projects (e.g., invoice processing automation) typically take 6–10 weeks. Custom AI systems with deep integrations take 10–16 weeks. Enterprise platforms with multiple modules take 4–9 months. Our average time-to-production is 10 weeks — approximately 3× faster than traditional IT project timelines — because we start with fixed scopes and iterate in 2-week sprints.
A focused automation or LLM integration project typically ranges from ₹15–40 lakh ($18,000–$50,000 USD). A custom AI system with dedicated engineering resources typically costs ₹40 lakh–₹1.5 crore ($50,000–$180,000 USD) depending on scope and team size. Dedicated AI engineering teams run ₹3–8 lakh/month per engineer. We provide detailed fixed-scope quotes after a free discovery call — no surprises.
We are model-agnostic and select the best AI model for each use case. We work with Anthropic Claude (excellent for reasoning, document analysis, and compliance-sensitive tasks), OpenAI GPT-4o (strong general-purpose and multimodal capabilities), Google Gemini (ideal for long-context and Google Workspace integrations), and open-source models including LLaMA 3 and Mistral for on-premise deployment or full data sovereignty requirements.
We sign Data Processing Agreements before accessing any client data. By default we implement PII anonymization, role-based access controls, end-to-end encryption, and zero-retention API configurations. Our infrastructure is SOC 2 Type II audited, GDPR compliant, and ISO 27001 certified. For healthcare clients we implement HIPAA-compliant architectures. All code and model weights remain your exclusive intellectual property.
Yes, 100%. All code, models, fine-tuning datasets, prompts, and system architectures developed during your engagement are your exclusive intellectual property. We do not retain any rights to reuse your systems, and we do not train our own models on your data. This is written explicitly into every contract we sign from day one.
RAG (Retrieval-Augmented Generation) connects a large language model to your private data so it can answer questions using your specific documents, policies, databases, and records — not just generic training data. For enterprises, this means employees can query internal knowledge bases in plain English, customer support AI can answer product-specific questions accurately, and compliance systems can reference the exact policy document when flagging an issue.
Yes — integration with existing enterprise systems is one of our core competencies. We have built integrations with SAP, Oracle, Salesforce, Microsoft Dynamics, ServiceNow, and Workday, as well as dozens of proprietary legacy systems. We use RESTful APIs, webhook architectures, and where necessary, custom middleware layers to connect AI to systems that weren't originally designed for AI integration.
Every deployment includes a 90-day post-launch support period at no additional cost. After that, we offer flexible ongoing support: a retainer model (fixed monthly hours), a monitoring-and-incident SLA (we alert and respond to system issues), or a full managed AI service (we run and optimize your AI systems on an ongoing basis). We also provide thorough documentation and knowledge transfer so your team can manage independently.
Yes — most of our clients start from zero AI infrastructure. Our AI readiness assessment identifies what you have (data assets, team skills, existing tools) and what you need, then we build the infrastructure alongside the AI system. You don't need an ML team, a data lake, or prior AI experience. We bring everything needed and transfer knowledge to your team as we go.
Still have questions?
Email our AI team →Enterprises that start today have a 12–18 month head start on competitors still evaluating options. Here's exactly what happens when you reach out:
Speak directly with a senior AI architect — not a salesperson. No commitment required.
Within 48 hours we'll send your top 3 AI opportunities with rough ROI estimates — in writing.
If you proceed, engineers can be active on your project within 2 weeks of contract signing.
We respond within 4 business hours.
Your information is protected and never shared.