AI-first GCC
Use-case identification, prompt and RAG architecture, fine-tuning, evaluation, governance, and production deployment of generative AI with enterprise controls built in.
30-50%
productivity in target workflows
6 wks
to first production use case
<1%
hallucination target with RAG
60%+
cost reduction via routing
Enterprises have run hundreds of GenAI pilots since 2023. The majority never reach production. The common failure modes are predictable - no evaluation framework so quality cannot be defended; no retrieval architecture so the model hallucinates on enterprise data; no governance so legal and risk block launch; no cost controls so unit economics break at scale; and no integration into actual workflows so adoption never compounds.
Production GenAI is an engineering and operating problem, not a model problem. The right RAG architecture, evaluation pipelines, prompt versioning, model routing, guardrails, observability, and governance turn experiments into reliable enterprise systems.
NeoIntelli designs and ships production GenAI - copilots, knowledge assistants, document workflows, agentic systems - with the engineering rigour and governance posture that lets the enterprise scale them without surprises.
Deliverables
01
Identify and prioritise GenAI use cases by business impact, feasibility, data availability, and risk profile - converted into a sequenced delivery plan.
02
Design retrieval-augmented generation pipelines, prompt templates, context strategies, and guardrails for consistent, grounded outputs.
03
Decide where to use base models, fine-tuning, distillation, or model routing - and implement the training and serving stack.
04
Establish offline and online evaluation pipelines covering quality, factuality, safety, bias, latency, and cost - automated into CI/CD.
05
Deploy with observability, cost tracking, prompt versioning, A/B testing, guardrails, and integration into the enterprise workflow.
06
Use-case classification, data handling controls, model documentation, audit trail, and human-in-the-loop design tied to your risk framework.
01
Identify high-value GenAI use cases, assess data and workflow readiness, define success metrics, and design the user experience.
02
Design the RAG, prompt, model routing, evaluation, and integration architecture - and validate it against safety and cost constraints.
03
Implement the system end-to-end with automated evaluation pipelines and a human review loop before any production exposure.
04
Roll out with observability, governance controls, cost discipline, and a continuous improvement loop based on real user signal.
Without automated evaluation, you cannot defend quality, detect regression, or scale safely.
Retrieval architectures without curated knowledge sources hallucinate worse than naked models.
One foundation model for every workload creates cost and capability mismatch. Model routing is now table stakes.
Sending sensitive data to third-party APIs without controls creates regulatory exposure.
Unversioned, unowned prompts across business units make quality and governance impossible.
GenAI unit economics break quickly without caching, model routing, and observability on every call.
Quality and factuality scores tracked on every production model
Sub-second p95 latency on user-facing workflows
Cost per query trending down as the portfolio matures
Hallucination rate below the acceptance threshold for the use case
Adoption growing month on month in target workflows
Zero unmitigated incidents involving sensitive data exposure
It depends on the use case. RAG is better when grounding on enterprise knowledge that changes often. Fine-tuning is better for style, format, or domain reasoning where examples are abundant. Many production systems combine both.
Through grounded retrieval (RAG), structured prompts, schema-enforced outputs, evaluation pipelines, citation requirements, and human-in-the-loop review for high-risk workflows.
We are model-agnostic - OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, and open-source models (Llama, Mistral, Qwen). Most production systems use model routing to balance quality, cost, and latency.
Through model routing, semantic caching, prompt optimisation, response streaming, batch processing where possible, and per-workflow cost dashboards that flag anomalies.
Yes, with the right governance. We build use-case classification, data-handling controls, audit trails, human-in-the-loop workflows, and evaluation pipelines aligned to sector regulation and the EU AI Act.
We design agentic systems with explicit tool definitions, planner-executor patterns, evaluation at each step, and human escalation paths. Agentic AI is powerful but requires more rigorous evaluation and observability than single-call patterns.
A focused use case typically reaches production in 6-12 weeks - faster with a mature platform, slower when data foundations need work.
GenAI is one capability within a broader AI portfolio that also includes classical ML, automation, and analytics. The strategy, operating model, and governance designed in AI Advisory cover all of them.
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