AI Development

Custom LLM Development and Fine-Tuning for Your Domain

We build, fine-tune, and deploy large language models tailored to your industry, data, and accuracy requirements — delivering capabilities no general-purpose API can match.

40%Cost Reduction
3xFaster Ops
Automation running — 247 tasks saved today
⭐⭐⭐⭐⭐ Trusted by Growing Businesses
✔ 40% Cost Reduction
✔ 3× Faster Operations
✔ 99% Accuracy
✔ 24/7 Support
⚠ Manual Process Overview
Errors: 23Pending: 47Manual: 100%
The Problem

When Off-the-Shelf LLMs Are Not Enough

General-purpose models miss domain nuance, generate costly errors in regulated industries, and send sensitive data to external APIs. Custom LLMs solve all three.

• Legal, medical, or financial terms misunderstood by general models\n• Data privacy requirements blocking use of cloud APIs\n• Accuracy too low for high-stakes automated decisions\n• Inference cost prohibitive at your target volume\n• Brand voice and style not captured by prompt engineering alone
Our Approach

What We Build

Domain-specific LLMs through fine-tuning, RLHF, and custom training — plus the serving infrastructure to run them reliably in production.

Discover & Assess

We map your current workflows, identify bottlenecks, and pinpoint every opportunity where automation saves time and cost.

Design & Build

Custom automations built precisely around your data, tools, and team — no generic templates, no wasted effort.

Launch & Optimise

Continuous monitoring, live dashboards, and iterative improvement so your automations compound in value over time.

Capabilities

Our LLM Development Services

Model Fine-Tuning

Supervised fine-tuning on Llama, Mistral, Gemma, and Falcon using your domain data — for tasks like classification, extraction, summarisation, and code generation.

RLHF & Alignment

Reinforcement Learning from Human Feedback to align model outputs with your quality standards, brand voice, and domain conventions.

Quantisation & Efficiency

INT4/INT8 quantisation and distillation to reduce model size and inference cost — running capable models on smaller, cheaper infrastructure.

Private Model Deployment

Self-hosted model serving on your cloud (AWS, Azure, GCP) or on-premise — so your data never leaves your environment.

Evaluation & Benchmarking

Rigorous evaluation frameworks benchmarking your fine-tuned model against GPT-4 and Claude baselines on your specific tasks and data.

Continuous Learning Pipelines

Feedback collection, data flywheel, and scheduled retraining pipelines so your model improves continuously with production usage.

Our Process

Our LLM Development Process

01 — Discovery Call

Free 30-min session — we listen, ask, and size the opportunity before quoting anything.

02 — Workflow Audit

We document your current processes and flag every step that can be automated or improved.

03 — Build

Clean, documented automations built to your exact specs using the tools you already use.

04 — Testing & QA

Every edge case, error path, and integration tested before anything goes live.

05 — Launch

Go-live with a live dashboard and real-time monitoring from day one.

06 — Ongoing Support

24/7 uptime monitoring, monthly performance reviews, and unlimited iterations.

20+
Hours saved per week
Automation active — 99.2% accuracy
The Outcome

What a Custom LLM Delivers

  • Domain accuracy 20–40% above general-purpose baselines
  • Sensitive data stays within your infrastructure
  • Inference cost dramatically lower than frontier API pricing
  • Brand voice and terminology captured in the model itself
  • A model that gets better as your team uses and rates it
  • Full control — no dependency on a third-party API
  • Transformation

    Before vs After Automation

    ❌ Before

    • Manual data entry
    • Slow approval chains
    • Spreadsheet chaos
    • Human errors & rework
    • Missed follow-ups

    ✅ After

    • Automated workflows
    • Instant approvals
    • Connected systems
    • AI-powered accuracy
    • Real-time dashboards
    Case Study

    How We Fine-Tuned a Legal LLM That Outperformed GPT-4

    Challenge
    A legal technology firm needed clause extraction from UAE commercial contracts. GPT-4 misclassified 23% of clause types due to local legal nuances.
    Solution
    We fine-tuned Llama 3 on 8,000 annotated UAE contracts. The fine-tuned model achieved 97% accuracy vs GPT-4’s 77%, at 1/10th the inference cost.
    Faster Processing
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    Saved Weekly
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    Cost Reduction
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    Technology

    Tools We Work With

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    FAQ

    Frequently Asked Questions

    How much data do we need to fine-tune an LLM?+
    Less than you think. For supervised fine-tuning on a specific task, 1,000–10,000 high-quality examples are often sufficient to see significant improvement over a base model.
    Is fine-tuning always better than prompt engineering?+
    Not always. For many tasks, advanced prompting (few-shot, chain-of-thought) is sufficient. We evaluate both before recommending fine-tuning, since it adds cost and complexity.
    How long does fine-tuning take?+
    Data preparation takes 2–4 weeks. Model training and evaluation takes 2–3 weeks depending on dataset size and compute. Plan for 6–8 weeks total to a production-ready model.
    Can we run the fine-tuned model on our own servers?+
    Yes. We specialise in private deployments on-premise or in your own cloud account, using optimised serving stacks (vLLM, TGI, Triton) for production-grade throughput.
    How much ROI can we expect?+
    On average, our clients see a 40% reduction in operational costs and 3× faster process completion within 6 months.

    Ready to Build an LLM That Knows Your Domain?

    Book a free LLM evaluation call. We’ll assess whether fine-tuning is right for your use case and scope what a custom model would cost to build.