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AI & Data

Artificial Intelligence for production

Custom AI products, assistants and automation grounded in your business data and rules.

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300+ projects delivered

Overview

Artificial Intelligence with a clear purpose.

A focused artificial intelligence engagement that connects business outcomes, users, data and production engineering from the start.

Technology is selected after we understand the workload, constraints and operating model. We can work within an established stack or recommend a change with the trade-offs made explicit.

Business challenges

Business challenges we solve.

The engagement begins with the operating problem and a measurable baseline, not a predetermined tool.

  • AI experiments cannot reach production or show measurable value

    Addressed through ai opportunity and readiness assessment, with the baseline and owner agreed before delivery.

  • Data quality and ownership are unclear

    Addressed through data, retrieval and model architecture, with the baseline and owner agreed before delivery.

  • Answers cannot be traced to reliable business sources

    Addressed through agentic workflows and production applications, with the baseline and owner agreed before delivery.

  • Model risk, cost and human approval are not governed

    Addressed through governance, evaluation and mlops, with the baseline and owner agreed before delivery.

Solutions

What we can build with Artificial Intelligence.

Representative solution types - the right starting point depends on your users, systems and operating constraints.

  • Knowledge assistants with citations

    Designed around the workflow, users, integrations, data and measurable outcome required in your environment.

  • Bounded workflow agents

    Designed around the workflow, users, integrations, data and measurable outcome required in your environment.

  • Document intelligence

    Designed around the workflow, users, integrations, data and measurable outcome required in your environment.

  • Forecasting and decision support

    Designed around the workflow, users, integrations, data and measurable outcome required in your environment.

  • Recommendation and scoring

    Designed around the workflow, users, integrations, data and measurable outcome required in your environment.

  • AI governance and evaluation

    Designed around the workflow, users, integrations, data and measurable outcome required in your environment.

Aarvik capabilities and delivery capacity.

Aarvik brings strategy, product, design, engineering, AI, quality and operations together across teams in the UAE and India.

  • 80+designers and developers
  • 300+projects delivered
  • 4offices across the UAE and India

Scope

Scope of services.

Strategy, design, engineering, integration, modernization and support are scoped around the outcome and ownership model.

  • AI opportunity and readiness assessment

    Included with clear ownership, acceptance criteria, documentation and handover.

  • Data, retrieval and model architecture

    Included with clear ownership, acceptance criteria, documentation and handover.

  • Agentic workflows and production applications

    Included with clear ownership, acceptance criteria, documentation and handover.

  • Governance, evaluation and MLOps

    Included with clear ownership, acceptance criteria, documentation and handover.

Engineering foundation

Technology stack and integrations.

The final stack follows the workload, existing estate, team capability, security and ownership model.

  • OpenAI
  • Azure OpenAI
  • Anthropic
  • Gemini
  • Python
  • LangGraph
  • PostgreSQL
  • Vector databases
  • dbt
  • MLflow

Applied AI

AI-native, with human control.

Every engagement starts with a business decision or workflow, then connects trusted data, models, tools, evaluation and human oversight around it.

  • Grounded data
  • Scoped access
  • Human approval
  • Evaluation and audit

Process

A delivery flow that keeps risk visible.

  1. 01

    Frame

    Agree the outcome, constraints, baseline and decision owners.

  2. 02

    Design

    Map the experience, architecture, data flows, controls and delivery plan.

  3. 03

    Deliver

    Build and validate in short releases with visible demonstrations and acceptance criteria.

  4. 04

    Run

    Release with observability, documentation, support and a measured improvement backlog.

People

Relevant Aarvik specialists.

A representative team for this capability. Final staffing follows the scope and availability.

Meet the full team

  • Vikram TKV

    CTO

  • G Rabi Kumar

    Project Manager

  • Madatha Satish Siddiram

    Senior Software Engineer

  • Kamlesh Gupta

    Full Stack Developer

Engagement models

Choose the right way to engage.

Start small, commission a complete delivery team, or add specialists to your own team.

  • Best for an unclear opportunity or a high-risk assumption

    Discovery or proof sprint

    • Workflow and requirement map
    • Feasibility and architecture
    • Prototype or technical spike
    • Roadmap and delivery estimate
    Talk to us

  • Best for an outcome with defined ownership and releases

    Managed project team

    • Cross-functional Aarvik team
    • Milestones and working demos
    • Quality and security gates
    • Launch, handover and support
    Talk to us

  • Best for adding specialist capacity to an existing programme

    Team extension

    • Role and skill matching
    • Shared delivery practices
    • Transparent reporting
    • Knowledge transfer and continuity
    Talk to us

Industry fit

Artificial Intelligence across industry workflows.

We adapt the same engineering discipline to the data, regulation, customer journey and operating reality of each sector.

Success measures

How success will be measured.

For Artificial Intelligence, targets are agreed during discovery and reviewed after launch. Where verified project evidence is available, we present it separately as a case study.

  • Cycle time

    Baseline the time from request to completed outcome, then measure the change after release.

  • Quality and control

    Track defects, overrides, grounded answers, failed actions and approval outcomes.

  • Adoption

    Measure active use, task completion, abandonment and the points where people still need help.

  • Reliability and cost

    Monitor availability, latency, unit cost, incidents and recovery against agreed targets.

Related

Explore more in AI & Data.

Adjacent capabilities that often form part of the same programme.

FAQ

Frequently asked questions

When is Artificial Intelligence a good fit?

When its strengths match the workload, team and constraints. Discovery makes that decision explicit before a larger build begins.

Can Aarvik work with our existing team and systems?

Yes. We can lead a defined scope, embed specialists, or work alongside internal and vendor teams with shared quality gates.

How do you use AI in the engagement?

Every engagement starts with a business decision or workflow, then connects trusted data, models, tools, evaluation and human oversight around it. We also use AI-assisted delivery behind review, testing and security controls.

What happens after launch?

We provide monitoring, documentation, knowledge transfer and an improvement backlog, with support matched to the system's importance.

How are scope, timeline and cost estimated?

After a focused discovery, we break the work into outcomes, dependencies and releases. The proposal states assumptions, team shape, milestones and what is outside scope.

Can we start with a smaller validation?

Yes. A discovery or proof phase can test the riskiest assumptions on representative data before you commit to full delivery.

Consultation

Discuss your artificial intelligence requirement.

Share a work email now. On the next page, add the workflow, systems, constraints and outcome you have in mind.

  1. 01We review the requirement

    No automated sales sequence or generic proposal.

  2. 02Discovery conversation

    We clarify the outcome, data, systems, risks and decision owners.

  3. 03Scoped next step

    You receive a recommended team, delivery path, assumptions and estimate.