Generative AI Experiences
LLM interfaces that work over your data — with guardrails.
Assistants, search, and generation built over your own knowledge — retrieval-grounded so answers cite real sources, with evals and guardrails so the model helps without hallucinating. We build the UX that shows the model’s work, so users can trust and verify what it produces.For teams sitting on knowledge or content who want to search, draft, or answer over it reliably.
What it is
What is Generative AI Experiences, and when do you need it?
Generative AI here means using large language models — the technology behind tools like ChatGPT — to build assistants, search, and drafting tools that work over your own information. Instead of a generic chatbot, it answers from your documents and cites its sources, so you can trust and verify what it says.
The value is in the system around the model — retrieval, guardrails, and evaluation — that keeps it grounded in your data and stops it from making things up.
On cost, there are two honest paths. Using a third-party model API (Anthropic, OpenAI, Google) means no hardware and pay-per-use — most internal assistants cost far less to run than the staff hours they save, and you always have access to the strongest models. Self-hosting an open model flips that: a fixed GPU server cost and more engineering, in exchange for data that never leaves your environment and a flat bill at high volume. We start most clients on an API because it is cheap to prove value, and we design so the model can be swapped — moving to self-hosted later is a deployment decision, not a rebuild.
When it’s the right choice
- Your team wastes time finding answers buried in documents
- You repeatedly draft similar replies, summaries, or content
- You want an assistant grounded in your real data, not generic
- You need answers users can verify against sources
When it’s probably not
- There’s no body of knowledge for it to work over
- Answers must be 100% certain with zero tolerance for error
- A simple search or FAQ page would already solve it
The problems we solve
What usually pushes a team to call us.
Knowledge is hard to find
Answers exist somewhere in docs, tickets, or wikis, but finding them wastes hours.
Generic chatbots that hallucinate
An off-the-shelf assistant makes things up because it is not grounded in your actual data.
Repetitive drafting
Staff rewrite the same kinds of replies, summaries, and documents from scratch.
No way to verify answers
When the model responds, there is no source to check, so it cannot be trusted for anything important.
Unclear guardrails
There is no control over what the assistant can say or do, which is a risk.
What we may build
Concrete things, not “digital solutions”.
Systems we build
- Knowledge search & Q&A
- Internal AI assistants
- Summarisation & drafting tools
- Document generation
- Retrieval over your data (RAG)
- Support tooling
- Guided AI workflows
- Content ranking & search
Modules & capabilities
Building blocks we assemble.
The project flow
How Generative AI Experiences projects run.
The stages specific to this kind of build, in order. The method underneath them is the same on every engagement.
Use case definition
The specific task the AI should help with.
Knowledge review
What data it answers from, and its quality.
Retrieval design
Grounding answers in real sources.
Prompt & UX
An interface that shows the model’s work.
Guardrails
Boundaries on what it can say and do.
Evaluation
Measured quality against real questions.
Integration
Wired into your tools and workflows.
Monitoring
Tracking answers, gaps, and misuse.
Improvement
Refining retrieval and prompts over time.
We understand the business before we write codeThe same ten-step method runs through every Noctverse engagement. It starts with your business, not our tech.
Purpose & outcome
Before anything is designed, we agree on why the system exists — the business problem, who uses it, and what successful adoption actually looks like. If we cannot name the outcome, we do not start building.
Map the real workflow
We map how the work is done today — the people, departments, data sources, approvals, and the steps that quietly cause delays — before proposing a new one. We also flag the parts that should stay manual.
Constraints & risks
We surface the practical limits early: budget, timeline, existing systems, data quality, third-party APIs, compliance, connectivity, and how ready the team is to adopt something new. Constraints shape the design; they should not be discovered mid-build.
Scale & direction
We plan for where this is going — expected growth, future modules, new locations, larger data volumes, and integrations that will likely be needed later — so early decisions stay cheap to revisit instead of forcing a rewrite.
Solution architecture
We choose the architecture from the actual requirements, not from trends. For most business platforms a well-structured modular monolith with event-based processing ships faster, costs less, and is simpler to run. Microservices are for genuine needs — independent scaling, isolation, large teams, separate deploy cycles.
Experience & interface
We turn the approved workflow into user journeys, information architecture, and interface concepts — including the states that get skipped: empty, loading, error, and permission-limited views. The design should simplify the workflow, not decorate it.
Development & integration
Frontend, backend, database, APIs, authentication, role-based access, third-party integrations, notifications, files, reporting, and automation — built in reviewable milestones so you see working software, not status slides.
Testing & QA
Functional, responsive, cross-browser, device, and permission testing, plus error handling, performance checks, a security review, and user acceptance testing before anything reaches production.
Deployment & handover
Environment setup, production deployment, domain/SSL, database migration, monitoring, analytics, documentation, training, and source-code handover — plus store submission where relevant.
Maintenance & improvement
Software keeps evolving after launch: security and dependency updates, monitoring, performance work, bug fixes, small enhancements, new modules, and reviewing analytics to improve the numbers that matter.
How we make technical decisions
The honest trade-offs — and how we choose.
We decide on cost, complexity, performance, security, maintainability, growth, and your team’s capacity — not on what is trendy. The most complex option is rarely the right one.
RAG vs. fine-tuning
For answering over your own knowledge, retrieval (RAG) is usually the right start — cheaper, easier to update, and grounded in sources. Fine-tuning is for style or narrow tasks, not for injecting facts.
Hosted API vs. self-hosted model
Hosted models are fastest and strongest today. We consider self-hosting when data sensitivity or cost at scale justifies it.
Open-ended chat vs. guided flows
A blank chat box invites misuse. For most business tools, guided flows with clear actions are more useful and safer.
Grounded vs. ungrounded generation
Anything factual is grounded in retrieved sources with citations. Ungrounded generation is reserved for genuinely creative drafting.
A realistic scenario
An assistant your team can actually trust
An illustrative example of a typical engagement, not a specific client.
Before
A support team answers the same product and policy questions repeatedly, digging through scattered docs. A generic chatbot was tried but made up answers, so it was switched off.
What we’d build
The outcome
Staff and customers get grounded answers with sources they can verify, the assistant stays within its remit, and anything it cannot confidently answer is handed to a person.
Security & performance
Considered from day one. Fast now, and as you grow.
The same standard applies to every build, whatever the service.
Security & reliability
- Secure authentication & session handling
- Hashed passwords, never plain text
- Role-based permissions enforced server-side
- Input validation & sanitisation on every request
- Safe, type-checked file uploads
- Encryption in transit (HTTPS) and for sensitive data at rest
- Rate limiting on public endpoints
- Audit logs for sensitive actions
- Separated dev / staging / production environments
- Secrets kept out of the codebase
- Dependency updates & error monitoring
- A documented backup & recovery plan
Performance & scalability
- Image optimisation & CDN delivery
- Lazy loading & code splitting
- Database indexing for the queries that matter
- Caching where data allows it
- Background jobs & queues for heavy work
- Pagination instead of loading everything
- Monitoring so regressions are caught early
- A scaling path chosen before it is urgent
Working together
Clear milestones, no surprises.
Discovery workshop
We learn your process, goals, and constraints together.
Proposal & scope
A concrete plan, scope, and milestones — before code starts.
Design review
You approve journeys and screens before development.
Build demos
Working software at the end of each milestone, not slides.
UAT & launch
You test against real scenarios; we prepare production.
Handover & support
Docs, training, source code, and a maintenance path.
What you receive
Depends on scope — agreed up front.
- Discovery findings & workflow documentation
- Feature & module breakdown
- UI/UX design & an interactive prototype
- Frontend application
- Backend system & database
- Admin dashboard & role management
- API integrations
- Source code & deployment
- Documentation & training
- Initial post-launch support
Further reading
What we’ve written about this
Frequently asked
Straight answers.
Is generative AI expensive to run?
Usually less than people expect. API-based models are billed per use — for a typical internal assistant that is a modest monthly cost, tiny next to the hours it saves. Costs grow with heavy volume, which is exactly when self-hosting an open model on your own GPU server starts to pay off. We model both against your expected usage before recommending one.
Should we use our own model or a third-party one?
Third-party APIs win on capability, zero maintenance, and speed to start; your own (self-hosted open) model wins when data must never leave your environment or volume makes a fixed GPU cost cheaper than per-use billing. Most teams should start on an API and switch only when privacy or scale demands it — we build so that switch is possible.
Will it make things up?
We ground factual answers in your real data with citations and add guardrails and evals to minimise hallucination. Where it is unsure, it says so or hands off to a human.
Does it use our own data?
Yes — that is the point. It retrieves from your knowledge base so answers are specific and verifiable, not generic.
Is our data secure?
Access is controlled and, where sensitivity requires it, we design for private or self-hosted deployment. We are explicit about where data goes.
Which model do you use?
We choose the model per use case and can switch as the field moves. The system around it — retrieval, guardrails, evals — matters more than the specific model.
Can it connect to our tools?
Yes — it can retrieve from and act within your existing systems, with the right permissions and boundaries.
How do you measure quality?
We build evals from real questions and track answer quality over time, rather than judging by a few demos.
Choose how to get started
Build with us
A system engineered around how you work.
Scoped in a proposal, designed as a prototype, delivered in milestones you can review.
Start a projectSee the work
Systems already running in production.
Marketplaces, operations portals and mobile apps, with the case studies behind them.
View our work