Service
Generative AI & LLM Application Development
Shipping a generative AI product is an engineering discipline: model selection, context management, structured outputs, latency budgets, cost ceilings and evals. NexusNao builds LLM applications that hold up in front of paying users.
Problems we solve
Sound familiar?
Impressive demos that fail on real user inputs
Hallucinations eroding user trust in AI features
Latency and token costs that make unit economics impossible
No systematic way to compare models or prompt changes
Capabilities
What NexusNao delivers
AI assistants & copilots
In-product copilots grounded in your data with tool use and structured actions.
Content generation systems
Brand-safe generation pipelines with templates, review workflows and quality scoring.
Structured extraction
Reliable JSON outputs from messy inputs, validated with schemas and retries.
Model strategy & routing
Multi-model routing that matches each request to the cheapest model that meets quality.
Streaming UX engineering
Responsive streaming interfaces with optimistic states and graceful failure handling.
Typical use cases
Where this lands first
- Customer-facing product copilots
- Marketing and product content pipelines
- Conversational interfaces over business data
- Summarisation and briefing systems for knowledge workers
- Semantic search and Q&A features
Recommended technology
Tools we reach for
- Claude & GPT APIs
- Vercel AI SDK
- Next.js
- LangGraph
- pgvector & Pinecone
- Redis
Final technology choices are made per project, on evidence — never by default.
Delivery process
How the engagement runs
Scope
User journeys, quality bars, latency and cost budgets.
Model bake-off
Benchmark candidate models against your actual tasks and data.
Build
Application engineering with evals, guardrails and observability.
Launch & tune
Progressive rollout with live quality monitoring and cost tuning.
Benefits
What you get out of it
AI features users trust and return to
Grounded responses with your data as the source of truth
Unit economics engineered before scale, not after
A/B-testable prompts and models with objective metrics
FAQ
Generative AI & LLM Applications questions
Straight answers to the questions teams usually bring us.
How do you prevent hallucinations?
Grounding via retrieval, constrained structured outputs, citation requirements, confidence thresholds with fallbacks, and eval suites that measure faithfulness on your domain.
Which LLM should we use?
It depends on the task. We run a bake-off on your real workload measuring quality, latency and cost, and often route different request types to different models.
Can generative features run on our own infrastructure?
Yes — open-weight models can be self-hosted where privacy or residency requires it, with the same evaluation discipline applied.
Related services
Often combined with
Intelligence, made operational.
Ready to talk generative ai & llm applications?
Share where you are today. We'll respond with an honest read on feasibility, timeline and the fastest route to value.