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GPT-4o Β· Claude 3.5 Β· Llama 3 Β· Fine-Tuning

🧠 LLM Integration
That Understands. That Delivers.

Connect your product to the world's best language models β€” or fine-tune your own. We handle prompt engineering, API integration, cost optimisation, and private on-premise deployment for GPT-4o, Claude 3.5, Llama 3, and Mistral.

Start Your Project β†’ Book Free Discovery Call
50+
AI Projects
β˜…β˜…β˜…β˜…β˜…
4.9 / 5.0
2–6wk
Time to POC
On-Prem
Option
services/ai/llm-pipeline.ts
import { OpenAI } from "openai"
import { pgvector } from "@/lib/db"
// Hybrid RAG + LLM pipeline
export async function queryKB(q: string) {
const context = await retrieveChunks(q)
const res = await openai.chat.completions.create({
model: "gpt-4o", messages: [{ role: "user", content: q }]
})
return { answer: res.choices[0].message.content }
}
What We Build

Every LLM use case,
built for production

From a single OpenAI API call to a fully fine-tuned private model β€” we cover every pattern of LLM integration required to ship AI features your users actually rely on.

Discuss Your Project β†’
  • β†’GPT-4o, GPT-4 Turbo, and GPT-4o-mini API integration with streaming
  • β†’Claude 3.5 Sonnet & Opus integration via Anthropic API
  • β†’Llama 3 70B and Mistral 7B self-hosted on your own infrastructure
  • β†’Fine-tuning with LoRA and QLoRA on your proprietary datasets
  • β†’Function calling, tool use, and structured JSON output
  • β†’Prompt engineering, versioning, and A/B evaluation
  • β†’Cost optimisation: model routing, caching, and batching
  • β†’On-premise deployment for GDPR and data sovereignty requirements
Services Breakdown

Full-spectrum
LLM engineering

Every layer of LLM integration β€” from a quick API call to a multi-model production system with guardrails, monitoring, and continuous improvement.

πŸ”—
API Integration
GPT-4 Β· Claude Β· Cohere Β· Mistral

We wire LLM APIs into your product with streaming, error handling, retry logic, and cost telemetry β€” not just a fetch() call.

  • OpenAI, Anthropic, Cohere, Mistral APIs
  • LangChain and LlamaIndex orchestration
  • Multi-model routing and fallback chains
  • Rate limiting, caching, cost dashboards
🎯
Fine-Tuning
LoRA Β· QLoRA Β· RLHF Β· SFT

When a base model lacks domain accuracy, we fine-tune on your data to match performance that generic models cannot achieve.

  • LoRA and QLoRA fine-tuning pipelines
  • Dataset preparation and quality filtering
  • RLHF alignment for tone and safety
  • Model evaluation on MMLU and HELM benchmarks
πŸ–₯
On-Premise Deployment
Llama 3 Β· Mistral Β· Falcon Β· Phi-3

For GDPR, HIPAA, or security-sensitive use cases, we deploy open-source LLMs entirely within your own infrastructure.

  • GPU infrastructure provisioning (A100, H100)
  • Triton and vLLM inference server setup
  • Data sovereignty compliance architecture
  • Latency optimisation for on-prem serving
⚑
Prompt Engineering
System prompts Β· Few-shot Β· CoT Β· Evals

We design, test, and version-control prompts like production code β€” the primary quality lever for any LLM feature.

  • System prompt design and optimisation
  • Chain-of-thought and few-shot patterns
  • Structured output parsing and validation
  • A/B testing prompt variants with evals
πŸ”§
Function Calling
Tool use Β· Text-to-SQL Β· API actions

LLMs can do more than generate text β€” we build tool-use systems that query databases, call APIs, and take real-world actions.

  • Custom tool definitions and schemas
  • Text-to-SQL for natural language DB queries
  • Multi-step tool orchestration
  • Integration with your existing APIs
πŸ“Š
Evaluation & Monitoring
RAGAS Β· HELM Β· Cost dashboards

We build evaluation frameworks and production monitoring so you always know how your LLM features perform.

  • LLM evaluation with RAGAS and HELM
  • Hallucination detection pipelines
  • Latency and cost monitoring dashboards
  • Regression alerts on model upgrades
Technology

The stack behind every LLM project

Best-in-class tools chosen for performance, reliability, and team expertise β€” not hype.

GPT-4oClaude 3.5 SonnetLlama 3 70BMistral 7BPhi-3 MediumLangChainLlamaIndexPineconepgvectorMLflowWeights & BiasesAWS BedrockvLLMTriton Inference
Our Process

Brief to deployed β€” how we work

A clear, collaborative process with no surprises and working demos at every milestone.

01
Discovery & Requirements
Week 1

Audit your use case, data quality, model options, and privacy requirements. Define measurable success metrics before any code is written.

02
Model Evaluation & POC
Week 1–3

Working POC with your real data, evaluating 2–3 candidate models against accuracy, latency, and cost targets.

03
Integration & Security
Week 2–5

Production API integration with auth, rate limiting, PII redaction, cost controls, and full audit logging.

04
Fine-Tuning (if needed)
Week 3–6

Dataset prep, LoRA/QLoRA training runs, evaluation, and deployment of your domain-specific model.

05
Monitoring & Evals
Week 5–7

RAGAS evaluation framework, cost dashboards, hallucination alerts, and latency monitoring in production.

06
Go-Live & Handover
Week 6–8

Load testing, runbook documentation, team training, and 30-day post-launch support included.

Why Nexcode

What sets our LLM work apart

πŸ—
Senior Engineers Only

No juniors, no mid-weight delegation. Every engineer on your project is 5+ years experience, senior by any measure.

⚑
Performance as Pass/Fail

We set Lighthouse 90+ as a non-negotiable acceptance criterion β€” not a target, a requirement. Deployments fail if CWV regress.

πŸ§ͺ
Test Coverage Standard

Unit, integration, and E2E tests as standard deliverable. We don't ship without coverage. No exceptions under deadline pressure.

πŸ“
Architecture Before Code

Full system design β€” schema, API contracts, auth, deployment β€” documented and approved before any code is written.

β™Ώ
Accessibility Built-in

WCAG 2.1 AA from component 1, not added at the end. Keyboard navigation, screen readers, colour contrast β€” non-negotiable.

πŸ”
Weekly Working Demos

End of every sprint, you get a live staging URL to click through. Not a Loom recording β€” a real deployed demo.

πŸ”’
Zero Lock-in Guarantee

100% IP & code transfer. Your repo, your infra, your AWS account. Full documentation so your team can own it the day we hand over.

πŸ“Š
Analytics-Ready Launch

GA4, Mixpanel or Amplitude wired in before go-live. You launch with data, not waiting weeks to set up tracking after.

How we compare
Criteria ✦ Nexcode Typical Agency Offshore Dev Shop Freelancer
Full IP & code ownershipβœ“βœ“βœ“βœ“
Client Reviews

What clients say about our LLM work

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4.9 / 5.0 Β· 50+ AI projects
"

Nexcode rebuilt our entire frontend in Next.js App Router in 12 weeks. Lighthouse score went from 41 to 97. The code quality, test coverage, and documentation are unlike anything I've ever received from an external team. We extended the engagement twice.

SM
Sarah Mitchell
CTO, Apex Financial Β· SaaS Platform Rebuild
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Upwork Verified
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"They architected and built our entire web platform from scratch β€” real-time collaboration, complex permissions, WebSockets. Every edge case handled, zero bugs at launch."

JK
James Kowalski
CEO, NovaBrain AI
AI Web Platform
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"Our new storefront loads in 0.8s and converts at 3.2x our old Magento site. Every detail considered β€” mobile-first, accessibility, structured data. The results speak."

RP
Rachel Patel
Director, LuxeCommerce
Headless eCommerce
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"From Figma to deployed in 8 weeks. Their React architecture thinking sets them apart from every agency I\"

TN
Thomas Nguyen
Founder, WanderGo
Travel Booking Platform
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"200K concurrent users on launch day β€” not a single outage. The infrastructure and caching strategy Nexcode built handled load I didn\"

LM
Laura MΓΌller
VP Product, EduPath
EdTech LMS
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"The real-time dashboard processes 1M+ events/day without a hiccup. Clean code, exceptional docs, and they explained every architectural decision. Extended the team afterward."

AK
Amir Khan
CTO, SwiftFreight
Logistics Dashboard
FAQ

LLM Integration questions
answered

Have a question not covered here? Book a free 30-min call β†’

Which LLM should we use for our use case?↕
There is no single right answer β€” it depends on latency requirements, privacy constraints, budget, and task complexity. We evaluate 3–5 candidate models against your real data before recommending any approach. In many cases a smaller fine-tuned open-source model outperforms GPT-4 at a fraction of the cost for domain-specific tasks.
Can we keep data private and never send it to OpenAI?↕
Absolutely. We deploy open-source models (Llama 3, Mistral, Phi-3) entirely on your own infrastructure β€” AWS, Azure, GCP, or your own data centre. Your data never leaves your environment. This is our recommended approach for healthcare, legal, financial, and GDPR-sensitive use cases.
How long does LLM integration take?↕
A basic API integration with prompt engineering typically takes 2–4 weeks. Fine-tuning adds 3–6 weeks depending on dataset size. We always build a working POC in the first 2 weeks so you can validate the approach before committing further.
What does LLM integration cost?↕
Simple API integration from Β£8,000. Full RAG pipeline with LLM from Β£15,000. Custom fine-tuned model with deployment from Β£25,000. All fixed-price with no surprises.
Related Services

Often paired with LLM Integration

πŸ”
Knowledge bases & retrieval
β†’
πŸ’¬
Customer & internal bots
β†’
πŸ€–
Task automation, agentic flows
β†’
βš™οΈ
Model deployment & monitoring
β†’
🧠

Ready to add LLM intelligence to your product?

Free technical scoping call. We review your use case, recommend the right model and architecture, and provide a fixed-price estimate within 48 hours.

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