AI & Data Intelligence

AI that ships to production, not just to a slide deck

Plenty of companies have an AI pilot. Far fewer have AI their staff rely on every day. We focus on the second part: data, integration, guardrails and measurable results.

Robotic hand reaching towards a human hand, representing AI

Start with the decision, not the model

Every useful AI system improves a specific decision or task — approving a claim, routing a ticket, forecasting stock, spotting a defect. We begin by finding the decisions where better predictions are worth real money, then check whether your data can support them. Sometimes the honest answer is a simple rules engine. We will tell you.

From notebook to dependable system

Models are maybe 20% of an AI product. The rest is data pipelines, evaluation, monitoring, fallbacks, user interface and change management. That is where our engineering team spends its time — so the system still works six months after launch.

Responsible by default

We build in explainability where decisions affect people, bias testing for sensitive use cases, and data-handling that respects DPDP, GDPR, UAE PDPL and US sector rules.

What we deliver

How we can help

01

AI strategy & feasibility

A short, practical assessment of where AI will pay back in your business, with a ranked roadmap.

02

Custom machine learning

Prediction, classification, recommendation and forecasting models trained on your data.

03

Generative AI & LLM apps

Assistants, copilots and document intelligence grounded in your own knowledge.

04

Computer vision

Inspection, recognition, counting and video analytics for physical operations.

05

AI agents & automation

Systems that plan and complete multi-step work across your tools, with human approval where it matters.

06

MLOps

Pipelines, evaluation, monitoring and retraining that keep models accurate over time.

Toolbox

Technology we use

Proven, well-supported tools chosen for your requirements — never for novelty.

  • Python
  • PyTorch
  • TensorFlow
  • scikit-learn
  • LangGraph
  • OpenAI
  • Anthropic Claude
  • Llama
  • Hugging Face
  • MLflow
  • AWS SageMaker
  • Azure AI
  • Vertex AI
  • pgvector

What sets us apart

Why teams choose BeyondCode

01

You own everything

Code, designs, cloud accounts and store listings are in your name from day one.

02

Product thinking, not just code

We care whether the feature moved the metric, and we will help you measure it.

03

Time zones covered

Bengaluru-based with practical overlap for the Gulf, the UK, Australia and the Americas.

04

Transparent, predictable pricing

Fixed-price phases or monthly teams with clear reporting. No surprise invoices.

05

Security as a habit

NDAs, least-privilege access, encrypted devices and secure development practices on every project.

06

Senior people on your project

The engineers you meet in the pitch are the engineers who build your product. No bait and switch.

How we work

Discover. Build. Evolve.

The same disciplined process whether you are a startup or a listed enterprise.

01

Discover.

We dig into your goals, users, constraints and existing systems. You get a clear scope, architecture outline, risks and a fixed estimate — usually within two weeks.

02

Build.

Cross-functional sprints with a demo every fortnight. You see working software early, change direction cheaply and always know exactly where the budget stands.

03

Evolve.

Launch is the starting line. We monitor, measure and improve — or hand over cleanly to your team with documentation, training and a support window.

More on how we work

FAQ

AI Development: common questions

How do we know if AI will work for our business?

Start with a 2–4 week discovery. We review your processes and data, prototype the most promising use case and give you a clear go/no-go with expected ROI.

Do we need a lot of data?

Less than you think for generative AI, which can work from your documents and a few hundred examples. Traditional ML usually needs thousands of labelled historical records. We assess this in discovery.

Is our data safe with LLMs?

Yes, with the right setup. We use enterprise API terms that exclude training on your data, private cloud deployments or self-hosted open models, plus redaction and access controls.

How much does an AI project cost?

Proofs of concept usually range from USD 10,000–35,000. Production systems vary widely with scope and integrations; we provide a fixed quote after discovery.

Which countries do you work with?

We work with clients across India, the UAE, Saudi Arabia, Kuwait, Qatar, Bahrain, Oman, the USA, the UK, Australia, Canada and Singapore. Our delivery team is in Bengaluru, with a UK presence in Hampshire.

How do we get started with ai development?

Book a free 30-minute call. We will ask about your goals, users and constraints, then propose a short discovery phase or a direct estimate if the scope is already clear.

Start a conversation

Tell us what you are building

Share a few lines about your idea or problem. A senior consultant — not a salesperson — will reply with questions, a suggested approach and a ballpark budget.

We reply within one business day. Your details stay private — see our privacy policy.

Thirty minutes. One honest conversation.

Tell us what you are building or fixing. You will leave the call with a clearer scope, a realistic timeline and a ballpark budget — whether or not we end up working together.