Code scaffolding and refactors
Drafting service skeletons, spotting patterns for modularisation, and suggesting safer refactors before code review.
AI at Elisium
AI changes how we build software, not why we build it
Assistive, accountable, production-grade.
We use AI as a disciplined co-worker. It speeds up exploration and delivery, while our engineers stay responsible for architecture, reliability, and long-term decisions.
Principles
- AI amplifies our engineering discipline — it never substitutes ownership.
- Every AI feature is observable, reversible, and documented like any other critical service.
- Data safety leads the design; models receive only the context they truly need.
Trusted AI stack
Elisium Tech Platform
OA
OpenAI API
SG
Secure API Gateway
Workflow
How we use AI inside our engineering workflow
AI is embedded into our day-to-day delivery to remove toil, uncover options faster, and keep teams focused on the hard decisions.
Code copilots
Infra-as-code
Testing
Docs & logs
Test generation and infra-as-code support
Producing first-pass test cases, IaC snippets, and validation scripts that engineers tighten and harden.
Documentation and incident context
Summarising logs, incidents, and architectural decisions so teams can react faster and keep trail-ready documentation.
Research copilots
Helping teams compare APIs, libraries, or standards while humans decide what enters production.
AI behaves like a senior assistant: it accelerates thinking, but humans design, review, and sign off every change.
Client systems
How we integrate AI into client systems
Most engagements rely on OpenAI (and similar) through secure API tokens. We embed AI inside existing tools rather than creating novelty apps.
Embedded copilots
Context-aware assistants that live inside internal CRMs, ops consoles, or support desks to guide teams through complex workflows.
Knowledge search and summarisation
Layering AI on top of documents, tickets, or knowledge bases so staff can query, compare, and summarise with traceable references.
Workflow automation with guardrails
Drafting responses, reports, or structured content that flows through approvals, permissions, and audit logs before action.
Stack snapshots
Elisium UX Shells OpenAI Guardrails API Audit & Observability
Every integration ships with architecture diagrams, permissioning models, monitoring, and fallbacks so AI features stay observable and reversible.
Data safety, privacy, security
AI inside a secure engineering discipline
Client data is never treated as demo material. We design data flows so only the necessary context is shared with external models.
Data minimisation and anonymisation
Prompts carry just the fields needed for a response, stripping PII or masking identifiers wherever possible.
Encryption and controlled access
Traffic to AI endpoints is encrypted, gated through service accounts, and audited with per-environment secrets.
Governance and role-based permissions
We enforce RBAC, logging, and approval flows so only authorised roles can trigger AI actions or view generated outputs.
Deployment options
Private endpoints, regional routing, or stricter policies are used when compliance or sovereignty demands it.
Encryption
RBAC
Audit logs
Private endpoints
Data safety comes first. AI is layered on top of a secure platform strategy, never bolted on as an afterthought.
Human expertise
What AI cannot replace
AI offers suggestions but it cannot assume accountability for complex systems or businesses. Elisium engineers stay in charge of the work that truly matters.
Systems architecture & strategy
Multi-year roadmaps, budgeting, and trade-offs across cloud, data, and compliance require human judgement and ownership.
Product context & stakeholder alignment
Understanding why features exist, how teams operate, and how risk is shared is a people-first exercise.
Reliability & long-term stewardship
Someone must carry the pager, refactor subsystems, and keep infrastructure resilient. AI does not carry that responsibility.
Limits & risk
Limits and risks we design around
Large models can misinterpret prompts, hallucinate, or fail when context shifts. We treat AI as probabilistic tooling, not ground truth.
Hallucinations or wrong answers
Outputs are validated through schema checks, business rules, and human review before they affect production systems.
Missing or stale context
We maintain retrieval pipelines, context windows, and freshness indicators so models reason on curated, recent data.
External dependencies
APIs can change, throttle, or go down. Our integrations include retries, graceful degradation, and deterministic fallbacks.
We monitor prompts and cost, run sandbox tests, review outputs with SMEs, and keep human-controlled escape hatches for critical workflows.
Cost transparency
Understanding AI-related costs
AI usage involves API tokens plus the infrastructure that orchestrates prompts, context storage, and observability.
Usage-based API spend
Token consumption for prompts, context, and outputs. Usually modest but visible in monthly statements.
Support tooling
Vector stores, prompt routers, and monitoring services that keep AI features predictable.
Operational oversight
Time invested by engineers to tune prompts, validate outputs, and keep the experience sharp.
These costs are transparent and typically offset by faster delivery, fewer manual loops, and better insights for the business.
Business value
What this means for your business
Responsible AI lets us deliver systems faster while reducing operational drag for your teams.
Faster time to market
AI accelerates research and implementation so you see working software sooner.
More automation, less toil
Internal teams get copilots, better search, and guided workflows that free them for higher-value work.
Better decision support
Leaders gain richer summaries and telemetry, helping decisions stay rooted in current data.
Next step
Explore safe, concrete AI integrations
Tell us where your systems struggle and we will show how AI can responsibly augment them — no buzzwords, just measurable improvements.