AI & Machine Learning

LLM Integration Services From Prototype to Enterprise AI That Actually Ships

Numlytics builds production-ready LLM integrations for enterprises across the US, UK, Australia & UAE - RAG architectures grounded on your proprietary data, prompt engineering systems, AI-powered workflows, and secure deployments on Azure OpenAI and Anthropic Claude. We close the gap between the impressive demo and the enterprise system that your team trusts and uses every day.

Get Free Consultation ← Back to AI & Machine Learning
RAG architecture grounded on your enterprise knowledge base
Azure OpenAI - your data never leaves your infrastructure
Hallucination evaluation & guardrails before every deployment
Up to 50% lower cost vs US/UK AI consulting firms
Delivery Facts
2wk
Working RAG prototype
in 2 weeks
90%
Retrieval accuracy on
enterprise knowledge bases
100%
Azure-hosted deployments
- data stays in your tenant
50%
Lower cost vs US/UK
AI consulting firms
We build with
Azure OpenAI
OpenAI API
Anthropic Claude
LangChain
LlamaIndex
Pinecone
pgvector
Azure AI Search
What We Build

The Gap Between a Demo and an Enterprise LLM Integration

Getting a language model to produce impressive output in a demo is straightforward. Getting it to produce reliable, accurate, hallucination-free responses on your company's proprietary data - within your security perimeter, with logging, monitoring, and cost controls, is an entirely different engineering problem.

The gap between demo and enterprise is where most LLM integration projects stall. The model answers questions confidently but incorrectly. Responses reference documents the user never asked about. Sensitive data appears in answers it shouldn't. Nobody can explain why the model behaved differently on Tuesday than it did on Monday.

Our LLM integration services are designed for the enterprise gap - Retrieval-Augmented Generation (RAG) architectures that ground responses in your verified knowledge base, prompt engineering systems that constrain behaviour reliably, evaluation frameworks that measure output quality before deployment, and monitoring that catches regressions in production. Built on Azure OpenAI, OpenAI API, or Anthropic Claude - with your data staying inside your infrastructure.

Build Your LLM Integration →
Where LLM Projects Go Wrong
"Our LLM confidently answers questions with wrong information"
Hallucinations without RAG grounding. The model answers from its training data rather than your documents - producing confident, plausible, and often incorrect responses that erode trust the first time a user catches an error.
"We can't send our documents to OpenAI's servers"
Data privacy, regulatory requirements, and enterprise security policies prevent many organisations from sending proprietary data to public LLM endpoints. Azure OpenAI deployments within your own tenant resolve this, no data leaves your infrastructure.
"Our prototype worked but costs exploded in production"
Token usage without optimisation. Retrieval strategies that pull too many chunks. No caching layer for repeated queries. LLM inference costs can scale non-linearly from prototype to production - cost architecture must be designed in, not optimised retrospectively.
"The chatbot worked in testing but behaves inconsistently in production"
No evaluation framework, no regression testing, no prompt versioning. LLM behaviour changes across model versions, context length variations, and rephrased queries. Without evaluation infrastructure, production regressions go undetected until a user reports them.
What We Deliver

Six Components of a Production-Ready LLM Integration

From use case scoping and RAG architecture through to evaluation, deployment, and production monitoring - everything required to move from demo to enterprise system.

Use Case Scoping & LLM Selection

We define the exact use case - internal knowledge assistant, customer-facing Q&A, document summarisation, code generation, workflow automation, and select the right model and deployment approach. Not every use case needs GPT-4o. Not every deployment should be on OpenAI's public endpoints.

Use case & output requirements definition
Model selection (GPT-4o, Claude, Llama)
Deployment architecture (Azure, API, on-prem)
RAG Architecture & Knowledge Base

The Retrieval-Augmented Generation system that grounds the LLM in your proprietary documents - document ingestion pipeline, chunking strategy, embedding model selection, vector store (Pinecone, pgvector, Azure AI Search), retrieval algorithm, and re-ranking layer. The architecture that eliminates hallucination on your domain content.

Document ingestion & chunking pipeline
Vector store design & embedding selection
Hybrid search & re-ranking layer
Prompt Engineering & System Design

Structured prompt engineering - system prompt design, few-shot example selection, output format specifications, and guardrail prompts that constrain the model's behaviour reliably. Prompts versioned, tested, and documented, not hardcoded strings scattered across the codebase.

System prompt design & versioning
Output format & constraint engineering
Guardrail & refusal behaviour design
Evaluation Framework & Quality Testing

An automated LLM evaluation suite - retrieval accuracy, answer faithfulness, hallucination rate, response relevance, and latency benchmarks - run against a curated test set before every deployment. The quality gate that prevents regressions from reaching production users.

Retrieval accuracy & faithfulness scoring
Hallucination detection test suite
Automated regression testing pipeline
Secure Production Deployment

Production deployment on Azure OpenAI within your own Azure tenant, no proprietary data sent to public endpoints. API gateway, authentication, rate limiting, and cost controls configured. Integration with your existing applications, workflows, or internal tools via REST API or SDK.

Azure OpenAI private deployment
API gateway, auth & rate limiting
Cost controls & token usage budgets
Production Monitoring & Feedback Loop

Post-deployment monitoring - query logging, response quality sampling, latency tracking, cost per query, and user feedback collection. A feedback loop that surfaces low-quality responses for review and feeds corrections back into the evaluation suite, so the system improves with usage.

Query logging & response sampling
Latency, cost & quality monitoring
Feedback loop & evaluation dataset growth
How We Deliver It

From Use Case to Deployed Enterprise LLM in 4 Phases

Working RAG prototype in 2 weeks. Evaluation gates before every deployment - no untested changes reach production users.

Scoping & Architecture Design

Use case definition, model selection, deployment architecture (Azure OpenAI vs public API vs on-prem), data security assessment, RAG design blueprint, and evaluation metric definition. The architecture document your engineers build from - not a slide deck with boxes and arrows.

⏱ Week 1
RAG Build & Prompt Engineering

Document ingestion pipeline, vector store, embedding model, retrieval algorithm, and re-ranking layer built and tested. System prompt and prompt engineering completed. Working end-to-end RAG prototype with initial evaluation results reviewed with stakeholders before production build begins.

⏱ Weeks 1–3
Evaluation & Quality Gate

Automated evaluation suite built and run - retrieval accuracy, faithfulness, hallucination rate, latency, and cost benchmarks. Failure cases reviewed and addressed. No deployment approval until evaluation thresholds are met. The quality gate that differentiates a production system from a demo.

⏱ Weeks 3–4
Production Deploy & Monitor

Azure OpenAI deployment within your tenant, API gateway, cost controls, and application integration. Monitoring infrastructure live from day one - query logging, response sampling, latency and cost dashboards. Full handover documentation and team training on operating and extending the system.

⏱ Weeks 4–6
Why Numlytics

Why Choose Numlytics for LLM Integration Services

We've built production enterprise LLM integrations for legal, financial services, professional services, and SaaS organisations across the US, UK, and Australia.

Evaluation Before Every Deployment
We don't deploy an LLM integration without passing a pre-defined evaluation suite - retrieval accuracy, faithfulness, hallucination rate, and latency benchmarks measured against your test set. An untested LLM in production erodes user trust the first time it answers confidently and incorrectly.
Azure OpenAI — Your Data Stays in Your Tenant
Every enterprise LLM deployment we recommend uses Azure OpenAI within your own Azure tenant - your documents and query data never leave your infrastructure. We don't cut security corners to ship faster. Enterprise data governance requirements are non-negotiable design constraints, not afterthoughts.
RAG Architecture Specialists
Chunking strategy, embedding model selection, hybrid search design, re-ranking, metadata filtering, and retrieval accuracy optimisation, not just "connect documents to an LLM." The quality of a RAG system is almost entirely a function of retrieval design, not the base model.
Cost Architecture Built In
Token usage optimisation, semantic caching for repeated queries, retrieval chunk sizing calibrated to your use case, and cost monitoring dashboards included in every production deployment. LLM inference costs that scale unexpectedly from demo to production are a design failure we prevent upfront.
Model-Agnostic Recommendation
We work across Azure OpenAI, OpenAI, Anthropic Claude, and open-source Llama. Our model recommendation is based on your use case, security requirements, cost constraints, and performance needs, not a preferred vendor. The right model for a legal document assistant is different from the right model for a customer-facing chatbot.
Up to 50% Lower Cost
Senior LLM engineers and RAG specialists from India - same enterprise AI quality as US or UK AI consulting firms at up to 50% lower cost. Full timezone overlap, daily standups, and Slack access throughout the engagement.
★★★★★

"We had 14 years of legal case files, contract templates, and compliance documentation, 40,000 documents across SharePoint and local drives. Our lawyers were spending hours each week searching for precedents and policy answers they knew existed somewhere in the archive. We'd tried a basic chatbot that hallucinated law that didn't exist in our documents. Numlytics designed a RAG architecture on Azure OpenAI entirely within our Azure tenant — document ingestion pipeline, hybrid search with re-ranking, and a prompt engineering system that instructs the model to cite sources and refuse to answer when context is insufficient. Our evaluation suite shows 91% retrieval accuracy and under 2% hallucination rate on our test set. Lawyers now get accurate, cited answers in under 30 seconds. The first case where it surfaced a relevant precedent that changed our strategy paid for the entire engagement."

CL
Catherine L.
Head of Legal Technology · Law Firm, United Kingdom

LLM integration sits on top of clean, structured data and needs production infrastructure to run reliably.

MLOps Consulting
The CI/CD and monitoring infrastructure for AI systems
→
NLP Services
Classification, NER, and structured NLP before LLM integration
→
AI Product Development
Build an AI-powered product on top of your LLM integration
→
Data Quality Management
Clean source documents improve RAG retrieval accuracy
→
All AI & Machine Learning Services ↗
Full AI & machine learning service hub
→
FAQ

LLM Integration FAQs

Common questions before starting an LLM integration engagement with Numlytics.

Ask Us Anything →
RAG (Retrieval-Augmented Generation) grounds an LLM's responses in your specific knowledge base rather than its training data. When a user asks a question, a retrieval system finds the most relevant documents from your knowledge base, and the LLM generates its answer from those retrieved documents — not from general training data. This dramatically reduces hallucination, keeps responses accurate to your current documentation, and allows the LLM to answer questions about proprietary content it was never trained on.
Fine-tuning trains the model on your data - expensive, requires significant data volume, and bakes knowledge into the weights (meaning updates require retraining). RAG keeps the base model unchanged and retrieves relevant information at inference time from an external knowledge base that can be updated without touching the model. For most enterprise use cases, internal knowledge assistants, document Q&A, policy bots - RAG is faster, cheaper, and easier to update than fine-tuning.
We deploy on Azure OpenAI within your own Azure tenant - your documents and query data never leave your infrastructure and are never used to train Microsoft or OpenAI models. This satisfies most enterprise data governance, regulatory, and security requirements. For organisations that cannot use any cloud-hosted LLM, we can deploy open-source models (Llama) on your own on-premises or private cloud infrastructure.
Hallucination in enterprise LLM integrations is primarily a RAG architecture problem. A well-designed retrieval system returning accurate, relevant context - combined with prompts instructing the model to answer only from retrieved documents and to say it doesn't know when context is insufficient - eliminates most hallucination on domain-specific queries. We also implement an evaluation framework that measures hallucination rate before every deployment, providing evidence of quality levels before users interact with the system.
Numlytics delivers a working RAG prototype in 2 weeks - document ingestion, vector store, retrieval, and a functional Q&A interface. A full production LLM integration including evaluation framework, Azure OpenAI deployment, API gateway, cost controls, monitoring, and application integration typically runs 4–6 weeks. We do not deploy without passing evaluation thresholds - the quality gate adds time but prevents the trust-eroding experience of a hallucinating production system. See our MLOps consulting service for the ongoing infrastructure layer.
Ready to Start?

From LLM Prototype to Enterprise Production

Get a production-ready LLM integration - RAG architecture, Azure OpenAI private deployment, hallucination evaluation, and production monitoring. Working prototype in 2 weeks. Your data stays in your infrastructure. US, UK, Australia & UAE.

Get Free Consultation ← All AI & Machine Learning Services
Other AI & Machine Learning Services
Predictive Analytics NLP Services MLOps Consulting AI Product Development Data Engineering