Generative AI solutions that ship to production. Not just prototypes.
Aeologic is a Generative AI solutions and AI consulting company We deliver AI strategy, development, and integration services helping enterprises innovate faster, reduce costs, and scale safely.
What are Generative AI solutions?
Generative AI solutions use models such as LLMs (large language models), multimodal AI and diffusion models to create new content — text, images, code, designs and insights — rather than simply analysing existing data. In an enterprise context, this means automating documentation, powering chatbots and copilots, accelerating product design and generating decision-ready insights from unstructured data.
Unlike traditional automation, which follows fixed, predefined rules, Generative AI learns patterns from data and produces original output — a proposal draft, a product description, a code snippet, a demand forecast — that adapts to context instead of repeating a template.
With Generative AI, enterprises can automate content and repetitive tasks, improve decision-making with AI-driven insights, reduce operating costs and cycle times, personalise customer experiences at scale, and accelerate product development.
Aeologic's difference is the data underneath. Because we also build the sensing, IoT, edge and integration layers, a GenAI application we deploy is grounded in your real operational data — not a static document dump — which is what keeps outputs accurate instead of hallucinated.
Terms buyers mix up
- Generative AI
- Models that create new text, images, code or designs in response to a prompt or trigger.
- LLM
- Large language model — GPT, Llama, Gemini and similar text/reasoning models.
- Multimodal AI
- Models that work across text, image, audio and structured data together.
- Agentic AI
- Uses generative models as part of autonomous systems that plan and act with limited human input.
- AAF
- Aeologic Automation Framework — the 8-layer blueprint from sensor to strategy.
| Feature | Traditional automation | Generative AI solutions |
|---|---|---|
| Workflow type | Rule-based | Creative + cognitive |
| Flexibility | Low | Very high |
| Output | Limited, repetitive | Unique, dynamic content |
| Innovation | Minimal | High |
| Scalability | Team-dependent | Model-dependent |
| Time to value | Moderate | Rapid — 6–8 week pilots |
Six problems Generative AI actually fixes.
These are the recurring reasons enterprises come to us, in their own words — and the pattern of solution that resolves each one.
Slow, manual content workflows
Problem: marketing, product and documentation teams are overloaded and can't keep pace with demand.
- AI content engines for blogs, product copy, emails
- Report and proposal drafting at scale
- Brand-voice fine-tuning on your assets
Repetitive operations & process errors
Problem: manual workflows increase errors and delays across CRM, ERP and ticketing systems.
- GenAI wired directly into workflow platforms
- Automated data entry & validation
- Exception routing to a human reviewer
Long design & engineering cycles
Problem: prototyping and simulation take weeks or months of engineering time.
- Generative design for ideation & simulation
- Automated optimisation passes
- Faster prototyping loops
Customer support backlogs
Problem: support teams struggle with volume and inconsistent answers.
- Domain-trained LLM chatbots
- Instant resolution for routine queries
- Full-context handoff to a human agent
Siloed data, slow insight
Problem: decision-making is slow because data is fragmented across systems.
- GenAI insight engines summarise reports
- Trend forecasting on demand
- Recommendation generation for operators
Fragmented ESG & sustainability data
Problem: sustainability data is scattered across facilities, suppliers and legacy systems.
- Automated ESG data aggregation
- Draft sustainability disclosures
- Emissions & resource-use forecasting
Six services. One delivery team, strategy to deployment.
You are not buying seats or licences — you are buying a working system with an owner, end to end.
AI Consulting & Strategy
A structured assessment to identify high-ROI use cases, evaluate data and infrastructure readiness, and build a scalable AI adoption roadmap — including governance, risk and compliance guidance from day one.
Custom Generative AI Development
Bespoke GenAI applications fine-tuned on your proprietary data — from intelligent copilots for internal teams to AI tools that improve developer productivity.
Chatbots & Virtual Assistants
Domain-trained conversational AI handles customer and employee support at scale, resolving routine queries instantly while escalating complex cases to human teams.
Workflow & Document AI
Intelligent document processing and workflow automation extract, summarise and route information across CRM, ERP, ticketing and legacy systems — cutting manual data entry and processing time.
Predictive Analytics
GenAI-powered forecasting and decision-intelligence tools turn historical and real-time data into demand forecasts, risk scores and operational recommendations.
LLM Integration
We integrate leading large language models — GPT, Llama, Gemini — directly into enterprise systems: ERPs (SAP, Oracle, Dynamics, Odoo), CRMs (Salesforce, Zoho, HubSpot) and support platforms (Freshdesk, Zendesk, ServiceNow).
Generative AI is Layer 7. It's only as accurate as Layers 1–6.
Ask any GenAI vendor where their grounding data comes from. If the answer is “whatever you upload once”, expect hallucination. The Aeologic Automation Framework exists so it doesn't have to be that way.
Sense
Layers 1–4. RFID, IoT sensors, edge gateways and cameras generate live ground truth — the operational facts a GenAI application can be grounded in, instead of a stale document export.
Decide
Layers 5–6. Spatial context and a unified data layer feed the model retrieval and fine-tuning pipeline, so outputs reflect what is actually happening in your ERP, CRM and floor systems.
Act
Layer 7–8. GenAI drafts, summarises and recommends; where it also plans and executes multi-step work with limited supervision, that's Layer 8 — Agentic AI, often built on top of the same GenAI foundation.
Pick your sector. See the application.
Six industries, the same underlying platform. These are the GenAI use cases enterprises ask for first.
Documentation load & slow queries
Clinical staff spend hours on documentation and answering routine patient queries instead of on patients.
Medical LLM + patient-query bot
A domain-trained LLM drafts clinical documentation and handles routine patient queries, integrated into EMR workflows.
60% faster clinical documentation
Deployable on-premise, HIPAA-compliant.
Downtime & slow design cycles
Maintenance is calendar-based and prototyping takes weeks; failures and design revisions both cost line-time.
Predictive maintenance + generative design
GenAI-based simulation models predict failure and generate design/optimisation options automatically.
35% reduction in downtime
Plus automated quality documentation and safety monitoring.
Content bottleneck at scale
Product descriptions, SEO content and personalisation can't be written fast enough for the catalogue.
AI content & personalisation engine
Generates product descriptions and SEO content automatically, and personalises recommendations per shopper.
5x faster content pipeline
Demand forecasting on the same data layer.
Manual compliance & slow risk modelling
Compliance reporting and portfolio risk analysis are assembled by hand across disconnected systems.
Fraud detection + compliance reporting
GenAI models flag fraud patterns, auto-draft compliance reports, and support risk and portfolio modelling.
20–40% less manual work
ISO 27001:2022 controls throughout.
Fragmented planning data
Routing and planning decisions are made on incomplete, siloed data with slow forecasting cycles.
Smart routing + forecasting engine
GenAI-driven planning automation generates routing options and demand/insight forecasts on live data.
20–40% less manual work
Pairs well with RFID/IoT track-and-trace.
Long design & documentation cycles
Engineering documentation and simulation work consume weeks of specialist time per design iteration.
Generative design + auto-documentation
Simulation and design-optimisation models generate options; GenAI drafts the accompanying technical documentation.
5–10x faster documentation
Feeds directly into manufacturing QA workflows.
Six phases, readiness to continuous optimisation.
Each phase ends with a decision gate. You can stop after any of them — and you own everything built up to that point.
AI Readiness Assessment
Evaluate data maturity, infrastructure and high-ROI use cases before any implementation begins.
Strategy & Roadmap
Map short-term pilots and a long-term AI adoption plan, typically spanning 12–24 months for full enterprise rollout.
Solution Design & Model Selection
Choose the right LLM or diffusion model — GPT, Llama, Gemini — and design the application architecture around your workflows.
Integration & Development
Build and connect the solution to your existing ERP, CRM, cloud or on-premise systems.
Testing & Governance
Validate outputs, implement security and bias checks, and confirm compliance before go-live.
Deployment & Optimisation
Roll out to production and continuously monitor, retrain and improve based on real usage.
Numbers, not narratives.
From delivered engagements. Named case studies available under NDA on request.
Downtime reduction
Manufacturing — predictive maintenance built on GenAI-based simulations.
Faster content pipeline
E-commerce — automated product descriptions and SEO content generation.
Faster documentation
Healthcare — custom medical LLM integrated into EMR workflows.
20–40%
Reduction in manual work
5–10x
Faster content & documentation cycles
15–30%
Cost savings on operations
12–18 mo
Typical full ROI realisation
Generic AI/dev agency. In-house team. Aeologic.
An honest comparison. There are situations where the other two are the right answer — they are listed too.
| Generic AI / dev agency | Build in-house | Aeologic | |
|---|---|---|---|
| Time to first working PoC | 2–4 months, model-first | 6–12 months incl. hiring | 6–8 weeks |
| Grounding data for the model | Whatever you upload once | Depends on existing estate | Wired to live ERP/CRM/ops data |
| Model & LLM selection | Usually one vendor, fixed | Team must evaluate options | GPT, Llama, Gemini — matched to use case |
| Legacy ERP / CRM integration | Case by case | Yes — you already know it | SAP, Oracle, Dynamics, Salesforce, Zoho |
| Compliance posture | Varies widely | Yours to build and audit | ISO 27001:2022 · CMMI 3 · HIPAA |
| Who runs it after go-live | You do | You do | Managed retainer, or trained handover |
| Best when… | You need one narrow model, fast, and own the data plumbing already | AI is your core product and you can hire an ML team | GenAI must be grounded in real operational data across systems |
Built for confidential and regulated data.
Security is a top priority, especially for enterprises handling confidential or regulated data.
On-premise / private cloud
Your data never leaves your environment — public cloud, private cloud, on-premise or air-gapped deployment options.
Role-based access controls
Only authorised users access sensitive tools, with granular permissions by team and system.
Data governance framework
Versioning, lineage, audit logs and full traceability across every model and pipeline.
Model safety & monitoring
Hallucination control, bias checks and drift detection built into the deployment, not bolted on after.
Compliance standards
ISO 27001:2022, CMMI Level 3, SOC and GDPR-aligned practices, and HIPAA compliance where applicable.
Encrypted architecture
End-to-end secure API and backend architecture for every integration point.
What it costs. What drives the number.
Your investment depends on scope, data readiness and integration needs. Rather than a fixed price list, engagements typically follow one of three models.
Engagement models
- Proof of Concept (6–8 weeks) — a scoped pilot on one high-ROI use case, priced to your specific requirements.
- Enterprise deployment — customised based on integration scope, data volume and compliance requirements.
- Ongoing support — time-and-materials or managed-service retainer options.
Expected outcomes from delivered engagements
- 20–40% reduction in manual work
- 5–10x faster content and documentation cycles
- 15–30% cost savings on operations
- Full ROI typically realised within 12–18 months
Use the per-layer ROI calculator for an indicative business case before you talk to us.
Nine questions buyers actually ask.
Short, direct answers — written so a search engine or an AI assistant can quote them without rewriting.
What exactly are Generative AI services and solutions?
Generative AI services and solutions use models such as large language models (GPT, Llama, Gemini), multimodal AI and diffusion models to generate new content, automate complex workflows and support business decision-making. Aeologic's Generative AI solutions include custom LLM development, AI-powered automation, intelligent chatbots and copilots, content generation systems, industry-specific GenAI applications, and workflow integrations into CRM, ERP and legacy platforms.
How can Aeologic help my enterprise implement Generative AI successfully?
Aeologic provides end-to-end implementation: an AI readiness assessment, strategy and roadmap creation, custom AI development, system integration with your existing ERP or CRM, a pilot-to-production rollout, and ongoing governance and compliance support — so adoption is fast, secure and measurable.
Which industries benefit the most from Generative AI solutions?
Generative AI delivers value across manufacturing (predictive maintenance, generative design), healthcare (clinical documentation, medical summarisation), retail and e-commerce (content generation, personalisation), banking and finance (fraud detection, compliance reporting), logistics (smart routing, forecasting), and automotive and engineering (design automation, simulation).
How secure and compliant are Aeologic's Generative AI solutions?
Aeologic secures every GenAI deployment with on-premise or private cloud options, role-based access controls, a full data governance framework with audit logs, model safety monitoring for hallucination and bias, and compliance with ISO, SOC, GDPR and HIPAA standards where applicable.
How fast can we see results or ROI from Generative AI?
Most enterprises see measurable improvements within 4–8 weeks of pilot implementation: initial prototypes in weeks 1–2, a working pilot by weeks 3–6, a 20–40% reduction in manual work within 2–3 months, and full ROI through cost savings and efficiency gains within 12–18 months.
Can Generative AI integrate with our existing CRM, ERP or legacy systems?
Yes. Aeologic integrates GenAI with CRMs (Salesforce, Zoho, HubSpot, Dynamics), ERPs (SAP, Oracle, Microsoft Dynamics, Odoo), support platforms (Freshdesk, Zendesk, ServiceNow), CMS/DMS tools, and legacy SQL or NoSQL databases, using secure APIs and workflow orchestration so GenAI feels like an extension of your current systems rather than a separate silo.
What is the typical investment required for Generative AI projects?
Pricing depends on use-case complexity, industry and compliance requirements, integration scope, and the extent of model training required. Most engagements start with a scoped 6–8 week proof of concept before moving to a customised enterprise deployment, with time-and-materials or managed-service retainer options for ongoing support.
What's the difference between Generative AI consulting and Generative AI development?
Generative AI consulting focuses on strategy — assessing readiness, identifying high-ROI use cases and building an adoption roadmap. Generative AI development is the hands-on build phase — creating and fine-tuning the actual models, chatbots or applications. Most Aeologic engagements include both: consulting defines what to build, development delivers it.
How is Generative AI different from Agentic AI?
Generative AI creates content — text, images, code or insights — in response to a prompt or trigger. Agentic AI goes a step further, using generative models as part of autonomous systems that can plan, take multi-step actions and make decisions with limited human input. Aeologic's Generative AI solutions often serve as the foundation for these more autonomous, agentic systems as enterprises mature their AI adoption.
Related layers, capabilities and proof.
Let's find your
first layer.
Book a 2-session AAF Workshop. Our solution architects walk your operations layer by layer, identify the fastest-ROI pilot, and hand back a sequenced roadmap with integration plan and validated numbers.
