How do you prove the assistant is accurate enough to launch?

We build a golden evaluation set from real user questions with expected answers, then score every release against it for factuality, grounding, and refusal behaviour. You get a published accuracy threshold that must be met before promotion.

Can this run without sending data outside our environment?

Yes. We deploy into your cloud tenancy using in-region model endpoints, or fully self-hosted open-weight models where regulation demands it.

How quickly can an engagement start?

Discovery usually begins within one to two weeks of scope agreement, and a delivery team is typically mobilised within three to four weeks depending on the skills involved.

Can you work alongside our in-house team?

Yes — most of our engagements are blended. We work in your repositories, ceremonies, and tooling, and we plan for knowledge transfer from the start so capability stays with you.

How are engagements priced?

Fixed price for well-bounded projects, monthly capacity for dedicated pods, and outcome-linked SLAs for managed services. We are explicit about assumptions and what would change the number.

NeonAITech services — AI and data engineering, product engineering, cloud and DevOps, managed operations, security and quality

Generative AI & LLMOps

Take assistants and agents from promising demo to dependable production.

Book Free Consultation All Services

60%+

Query deflection on support workloads

40%

Lower token spend after tuning

Weekly

Automated quality regression runs

Overview

What Generative AI & LLMOps means at NeonAITech

Most generative-AI pilots stall because nobody owns evaluation, cost, and reliability. We build the full production path — retrieval grounded in your knowledge, agent orchestration, evaluation harnesses, guardrails, and observability — so AI features stay accurate and affordable after launch.

Tools & Platforms

Claude
Amazon Bedrock
Azure OpenAI
LangGraph
pgvector
LangFuse

We are not tied to a single vendor — the stack follows the problem, your existing estate, and your team’s skills.

What We Deliver

RAG & Knowledge Grounding

Retrieval pipelines over your documents and systems, with chunking, re-ranking, citations, and permission-aware access built in.

Agentic Workflow Orchestration

Tool-using agents that complete multi-step business tasks, with checkpoints, fallbacks, and human approval where the stakes demand it.

LLMOps Platform

Evaluation suites, prompt versioning, regression gates, token-cost dashboards, and drift alerts that keep quality measurable release over release.

Capabilities

Inside the Engagement

Model selection, routing, and fallback strategies

Golden-set and LLM-as-judge evaluation pipelines

Guardrails, PII redaction, and content safety

Vector, hybrid, and graph-based retrieval

Token-cost, latency, and caching optimisation

How We Work

A delivery rhythm you can see into

Every Generative AI & LLMOps engagement runs the same four phases, with AI used wherever it removes effort rather than adds novelty.

01
Discover
We map the current state, agree the outcome, and size the work — so scope is a shared decision, not a surprise.
02
Design
Architecture, delivery plan, and success measures are set before build, with costed options where trade-offs exist.
03
Build
Short increments with working output you can review, steer, and stop — never a black box until go-live.
04
Operate
We measure against the agreed outcomes, hand over documentation, and stay on for support where you want it.

Engagement Models

Buy it the way that fits

Fixed-Scope Project

A defined outcome, timeline, and price. Best when requirements are clear and the deliverable is well bounded.

Dedicated Pod

A cross-functional team working to your backlog and priorities, scaling up or down with a month’s notice.

Managed Service

Ongoing ownership against agreed SLAs, with a share of capacity reserved for continuous improvement.