Services · Applied AI & green technology
AI and green-technology projects, scoped one workflow at a time.
We design and build AI systems around one workflow at a time, using your data and your systems, and run green-technology projects the same way. Success criteria are agreed before anything is built.
None of it is a packaged product. Scope is set in discovery, and sometimes the honest answer is a conventional search or workflow tool instead.
01Where each piece stands
What exists today, and what is scoped work.
Products, services and research on this site carry a status label that says what is available, what is built per engagement and what is still research.
| Label | Meaning | Where it applies |
|---|---|---|
| Product | Ramerion's engineering software. Workflows are shown with labelled illustrations; what is available for a given project is confirmed in a demo. | |
| Scoped service | Work designed and built for one organisation's workflow after discovery. Not a packaged product, and only taken on where we can substantiate the approach. | The services below |
| Concept | A proposed offering whose architecture is defined for each deployment. | AI Box |
| Exploratory research | Questions under study. No product, result or timeline is claimed. | The research areas |
| Future possibility | A technically plausible longer-term direction inside a research area — not work under way. | Marked within research pages |
02Services
Eight services. Start from the problem you have.
Each has its own page covering use cases, the technical approach, limits, security and how success is measured. Many projects combine two or three.
Applied AI
- AI Agents & Workflow AutomationMulti-step tasks carried out through approved tools, with approval points and a full trace.Start here ifa process has several steps that vary from case to case.
- RAG & Enterprise Knowledge SystemsQuestion answering over your own material, filtered by permission and backed by citations.Start here ifpeople spend time hunting for the current, authoritative answer.
- Model Adaptation & Fine-tuningA measured route from prompting to — only if the evidence supports it — further training.Start here ifa model is nearly right but inconsistent in format, labels or style.
- Private AI & On-premises DeploymentManaged, dedicated, self-hosted and on-premises options compared on boundary, effort and cost.Start here ifdata sensitivity or regulation limits where processing may happen.
- AI Integration & Business AutomationAI steps wired into CRM, ERP, ticketing and databases with scoped access and safe write-back.Start here ifwork is re-keyed or copied between systems.
- Document IntelligenceClassification and extraction into validated records, with uncertain cases sent to people.Start here ifstaff type information from documents into other systems.
- AI Evaluation, Reliability & GovernanceTest sets, release gates, monitoring and controls sized to the risk.Start here ifyou need evidence that an AI system — new or existing — is good enough.
Green technology & climate
03Where to start
Use cases worth assessing, by organisation size.
Examples to assess, not customer deployments. The right answer is sometimes a conventional search, rules or workflow tool — and we will say so.
Small teams
- Internal knowledge search
- Draft customer responses, reviewed before sending
- Document intake
- Repetitive reporting
- Low-risk workflow assistance
Growing companies
- Support and operations triage
- Knowledge systems across several repositories
- Workflows integrated with CRM or ERP
- Document extraction with review
- Narrowly scoped agents
Large or regulated organisations
- Permission-aware retrieval across many sources
- Private deployment options
- Identity, access control and audit trails
- Custom integrations
- Model and provider selection
- Evaluation, governance and operating handover
04How an engagement runs
Small, measured steps — with a way out at each one.
Discover the workflow
Who does the work today, in which systems, and what “better” would mean in measurable terms.
Check data and risk
Whether the data exists, may be used and is good enough — and what could go wrong, and how badly.
Prototype against test cases
A small build tested on representative cases and compared with the baseline.
Pilot with guardrails
Real users, limited scope, human review, monitoring and an off switch.
Harden and hand over
Security review, regression tests, monitoring and an operating handover — then iterate as models and data change.
05References
Public guidance we design against.
- OWASP GenAI Security ProjectTop 10 for LLM Applications 2025 (external site)Threat checklist: prompt injection, sensitive-information disclosure, excessive agency and more.
- NISTAI Risk Management Framework: Generative AI Profile (NIST AI 600-1) (external site)Risk identification, governance and documentation.
- Microsoft LearnDesign and develop a RAG solution (external site)Document preparation, chunking, retrieval and end-to-end evaluation.
- Microsoft LearnRAG evaluators (external site)Definitions of retrieval, groundedness, relevance and completeness measures.
- OpenAIModel optimization (external site)Evaluations, prompting and fine-tuning as one iterative loop.
Standards-body and vendor guidance, used as engineering references. Listing it does not imply certification, compliance, endorsement or any partnership. Vendor guidance and service availability change often, so they are re-checked before they inform a recommendation.
Start with one workflow
Bring a workflow, not a wish list.
Describe the process, the systems it touches and what would count as better. The first conversation is about whether AI is the right tool — and if it is, what a small, measurable first step looks like.