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Enterprise AI Assistants

Secure assistants that know your business and act within your rules.

Generic chat tools answer general questions well and company questions badly. An enterprise assistant is grounded in your documents, data and systems, respects who is allowed to see what, and can take bounded actions such as drafting, filing or raising a request.

We design assistants around a specific job (a role, a process or a service desk) rather than a company-wide chatbot, so value can be measured and risk can be contained.

The problem

What usually gets in the way

  • Staff spend hours searching policies, contracts and past cases spread across systems.
  • Public AI tools are used informally, with no control over what data leaves the organisation.
  • Pilots impress in demos but stall on security review, access control and accuracy.
  • Arabic and bilingual users get noticeably weaker answers than English users.

What we deliver

Capabilities

01

Use-case and persona design

Pick the roles and tasks where an assistant saves measurable time, and define what it may and may not do.

02

Grounding in enterprise knowledge

Connect approved sources with retrieval that cites where each answer came from.

03

Permission-aware access

Answers respect existing access rights, so users only see what they are already allowed to see.

04

Tools and bounded actions

Let the assistant draft, look up, create tickets or update records through approved APIs, with human approval where it matters.

05

Model choice and hosting

Select models and hosting (global, regional or sovereign) against quality, cost, language and residency needs.

06

Evaluation and guardrails

Test sets, red-teaming, content filters and monitoring before and after launch.

Delivered by

Services involved

The service lines combined to deliver this solution.

How it fits together

Reference architecture

A vendor-neutral outline. Components are selected for your workloads, skills and regulatory context.

  1. ExperienceWeb and Teams/Slack assistantEmbedded in business appsArabic and English
  2. OrchestrationPrompting and tool callingAgent workflows with approvalsConversation memory policies
  3. Knowledge and contextRetrieval over approved sourcesPermission filteringCitations
  4. ModelsHosted or regional LLMsArabic-capable modelsModel routing by task and cost
  5. ControlsIdentity and accessLogging and auditEvaluation and monitoring

How we engage

Start small, prove value, scale

  1. 01Assess

    2–4 weeks

    Prioritise use cases, check data readiness, risk and residency constraints.

    • Use-case shortlist with value cases
    • Target architecture
    • Risk and control plan

  2. 02Pilot

    6–10 weeks

    Build one assistant for one team, measured against a baseline.

    • Working assistant
    • Evaluation results
    • Go/no-go decision

  3. 03Scale

    Ongoing

    Extend to more teams and sources on a shared platform.

    • Reusable assistant platform
    • Operating model and support
    • Adoption reporting

Outcomes

What we measure

  • Time to find and answer
  • Share of answers with correct citations
  • Deflected tickets or requests
  • Adoption and weekly active users
  • Cost per conversation

Where it applies

Example use cases

  • Banking

    Policy and product assistant for branch and contact-centre staff.

  • Government

    Regulation and procedure assistant for case officers.

  • Telecommunications

    Technical knowledge assistant for field and network teams.

  • Healthcare

    Clinical-protocol and administrative assistant with strict access control.

Accelerators

Reusable assets we are building

Delivered today as services. These accelerators are being developed from that work to shorten future engagements.

In development

Assistant reference platform

A reusable stack for retrieval, permissions, logging and evaluation.

In development

Evaluation kit

Test-set templates and scoring for accuracy, grounding and safety, in Arabic and English.

Technology

Platforms we design around

Vendor-neutral: examples of platforms commonly used for this solution. Names identify technologies only and do not imply a partnership.

  • Azure OpenAI
  • Amazon Bedrock
  • Anthropic
  • Google Gemini
  • Jais
  • LangChain
  • LlamaIndex
  • Microsoft 365
See the full technology ecosystem →

Insights

Further reading

Building Trustworthy Enterprise RAG

Most RAG teams tune prompts and swap models. The measurable gains are in retrieval, and the failures you cannot undo are in permissions and injected content. Build from the index outward, and let evaluation decide what ships.

Read →

Designing Agents With Guardrails

An instruction in a prompt is a request, not a control. Design every agent on the assumption that its model will eventually be manipulated, and put the guardrails where the model cannot reach them.

Read →

Arabic-First LLMs vs Global Models

Regional models win on dialect, control and cost per Arabic document, not by default. Public leaderboards mostly test exam-style standard Arabic, so the choice belongs to an evaluation set built from your own users.

Read →

Securing Enterprise AI Data

Most enterprise AI data exposure is not a model memorizing secrets. It is ordinary access control failing in new places: data sent out under the wrong terms, retrieval that ignores permissions, and agents that take instructions from what they read.

Read →

Talk to us about Enterprise AI Assistants

Start with a short assessment: we will rank the opportunities, check readiness and agree how success will be measured.

Start a conversation

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