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Generative AI & LLM Apps

Assistants, copilots and content engines grounded in your own knowledge, so answers are accurate, cited and safe to use.

Overview

Language models that know your business.

A general-purpose model knows a great deal about the world and nothing about your contracts, policies, products or customers. Generative AI becomes useful at work when it is connected to that knowledge, constrained by your rules and measured on the tasks your people actually do.

We build retrieval-augmented generation (RAG) systems that find the right passages in your documents and data, hand them to the model and require answers to cite their sources. Where retrieval is not enough, we add structured tools, fine-tuning or smaller specialised models.

Every application ships with an evaluation set built from real questions, guardrails for sensitive topics and personal data, and dashboards that show quality, usage and cost over time.

When it fits

Signs generative AI will pay off.

  • Answers are buried in documents

    Staff search shared drives, PDFs and old emails to find a clause, a price or a procedure.

  • Experts are interrupted all day

    The same specialists answer the same questions from colleagues and customers.

  • Public chatbots are used anyway

    Teams paste company information into consumer AI tools with no controls.

  • Content work doesn't scale

    Proposals, reports and listings take days to draft and come out inconsistent.

What ships

What we build with generative AI.

  • Knowledge assistants

    Chat and search over your documents, intranet and databases, with answers that cite their sources.

  • Role copilots

    Assistants inside the tools people already use, tuned for sales, legal, HR, finance or operations.

  • Document intelligence

    Extraction, classification and summaries of contracts, invoices, reports and forms.

  • Content generation

    Drafts of proposals, listings, reports and emails that follow your templates and tone of voice.

  • Multilingual experiences

    Arabic, English, Hindi and other languages handled in the same assistant, tested per language.

  • Guardrails and evaluation

    Topic, privacy and accuracy guardrails, plus test sets that measure quality before and after launch.

Use cases

Generative AI in practice.

Company knowledge assistant

We connect the assistant to approved sources such as policies, manuals, product sheets and past proposals, respect existing permissions so people only see what they are allowed to, and show the passages behind every answer.

  • Permission-aware retrieval
  • Citations on every answer
  • Feedback loop that improves answers

Process

How the engagement runs.

  1. 01

    Scope

    Define users, questions and success measures; gather real examples.

  2. 02

    Curate

    Select, clean and structure the sources; set access rules.

  3. 03

    Build

    Retrieval pipeline, prompts, tools and interface.

  4. 04

    Evaluate

    Score accuracy, grounding and safety against the test set.

  5. 05

    Launch

    Roll out to a pilot group, collect feedback, close gaps.

  6. 06

    Improve

    Monitor quality and cost; refresh sources and tests.

Deliverables

What you keep.

  • Application code and prompts
  • Ingestion and retrieval pipelines
  • Evaluation set and quality reports
  • Guardrail configuration
  • Admin and analytics dashboards

Stack

Technology we work with.

Models

  • OpenAI GPT
  • Anthropic Claude
  • Google Gemini
  • Open-weight Llama and Mistral

Retrieval

  • Hybrid keyword and vector search
  • pgvector
  • Qdrant
  • Re-ranking

Platforms

  • Azure OpenAI
  • AWS Bedrock
  • Google Vertex AI
  • Self-hosted inference

Quality

  • Evaluation sets
  • Model-graded checks with human review
  • Tracing

FAQ

Frequently asked questions

What is RAG and why does it matter?

Retrieval-augmented generation finds relevant passages in your own data and gives them to the model along with the question. The model answers from that evidence and cites it, which makes answers more accurate, more current and easier to check than relying on what the model memorised in training.

Does it work in Arabic?

Yes. Current leading models handle Arabic and English well, including mixed-language messages. We test retrieval and answers in every language you need, because quality can differ between languages.

Should we fine-tune a model?

Usually not first. Most business needs are met with good retrieval, instructions and tools. Fine-tuning helps when you need a consistent format or style at scale, or a smaller, cheaper model for a narrow task. We decide on evaluation results.

How do you handle confidential documents?

Access rules from your source systems are applied at retrieval time, sensitive data can be masked before it reaches a model, and deployments can run in a cloud region or environment you control.

More capabilities

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