Custom AI / LLM Integrations

We embed large language models into your existing products and internal systems, search, summarisation, classification, generation, with the guardrails, evaluation and cost control production needs.

What is an LLM integration?

An LLM integration embeds a large language model, such as GPT, Claude or Llama, into an existing application or internal system to add capabilities like search, summarisation, classification, drafting and question answering over your own data.

Generative AI is most valuable when it works inside the tools your team and customers already use. We add AI features to web apps, mobile apps, CRMs, ERPs and internal portals, so users get answers, drafts and insights in context.

We choose the right model for each task, balancing quality, speed and cost, and use retrieval-augmented generation (RAG) to ground responses in your own documents and databases. For specialised tasks we apply prompt engineering, structured outputs and, where it pays off, fine-tuning.

Production AI needs more than a prompt. Every integration includes automated evaluation, safety guardrails, monitoring, usage limits and cost tracking, plus private or on-premise model options for sensitive data.

Benefits

AI features users love: Smart search, summaries and drafting inside your existing product.
Secure by design: Data protection, access control and private deployment options.
Controlled costs: Caching, model routing and limits keep usage costs predictable.
Measured quality: Evaluation suites ensure answers stay accurate as you scale.

What’s included

LLM feature design
Retrieval-augmented generation (RAG)
Fine-tuning & prompt engineering
Evaluation & guardrails
Private / on-prem model options
Cost & latency optimisation

Use cases

Knowledge search: Let staff ask questions across policies, manuals and documents and get cited answers.
Document summarisation: Turn long contracts, reports and transcripts into clear summaries and action items.
Content generation: Draft product descriptions, emails and proposals in your brand voice.
Classification & routing: Automatically tag tickets, emails and leads and send them to the right team.

Our process

Use-case selection: We identify where AI adds measurable value and define success criteria.
Prototype: We build a working prototype on your data to validate quality quickly.
Productionise: We add evaluation, guardrails, monitoring and integrate into your system.
Optimise: We tune prompts, models and costs based on real usage.

Technologies

OpenAI, Anthropic Claude, Llama, Python, pgvector, AWS

Frequently asked questions

Which LLM is best for my use case?

It depends on the task, language, data sensitivity and budget. We benchmark leading models like GPT, Claude and open-source alternatives on your real examples before recommending one.

What is RAG and why does it matter?

Retrieval-augmented generation fetches relevant information from your own documents or databases and gives it to the model, so answers are accurate, up to date and traceable to a source.

Can we keep our data private?

Yes. We use providers with no-training data policies, restrict access, and can deploy open-source models on your own cloud or servers for sensitive workloads.

Do we need to fine-tune a model?

Usually not at first. Good prompting plus RAG solves most needs. Fine-tuning is useful for highly specialised formats or tone once you have enough examples.

How do you control AI running costs?

We route simple tasks to smaller models, cache repeated results, trim context and set usage limits, and report costs so there are no surprises.

Our offices

Dubai, UAE (Head office)
Rigga Business Center, Al Rigga, Deira, Dubai, United Arab Emirates
Phone: +971 52 497 0227

Peshawar, Pakistan (Development centre)
Hayatabad Phase 7, Peshawar, Khyber Pakhtunkhwa, Pakistan
Phone: +92 334 893 5202

Islamabad / Rawalpindi, Pakistan (Office)
Bahria Town Phase 7, Rawalpindi / Islamabad, Pakistan
Phone: +92 334 893 5202

Email: [email protected] · WhatsApp: +971 52 497 0227