AI development
AI development that reaches production.
Big Immersive is an AI development company with studios in Lahore and Dubai. We build production AI systems — LLM applications, retrieval-augmented generation, custom machine-learning models, and the platforms around them — on OpenAI, Anthropic, Google Gemini, PyTorch, and TensorFlow. Four AI products we built are live today, and you can open every one of them.
Studios in Lahore · Dubai · Since 2017
Discuss your projectWhat we build
LLM applications with a narrow, honest scope. Graysen reads a UAE contract in Arabic or English and returns the clauses that will cost you, each flagged with its reasoning, by scoping the model to named legal instruments rather than to law in general. Enlyte puts a hard strategic question through several expert lenses and always returns the same three things: the recommended action, its risk, and the blind spot. Yoluno is a conversational companion for children where the parent, not the model, decides what is in the world.
Retrieval and data systems. When the answer has to come from your documents rather than the model's memory, we build the retrieval layer: retrieval-augmented generation with LangChain or LlamaIndex over a vector database, scoped to the sources you trust. DISTRIQ AI gathers neighbourhood, school, safety, commute, and cost-of-living data once and serves three different products from it — consumers, employers, and relocating staff — through a single data layer.
Custom models where a general one will not do. Model training and evaluation in PyTorch and TensorFlow, predictive analytics, and the deployment and MLOps that turn a notebook into a service: monitoring, retraining, and the infrastructure underneath.
The product around the model. An AI feature is rarely the whole product. Graysen sits behind its own API with Postgres and Redis and a separate content system; DISTRIQ is one router over three shells. We build the application — web and mobile — as well as the intelligence inside it.
Included
- Model training & evaluation
- LLM and RAG integration
- Deployment & MLOps
- Monitoring and retraining
What we build with
The providers, frameworks, and infrastructure behind the four live products. Confirmed by the team, not inferred from the services list.
- OpenAI
- Anthropic
- Google Gemini
- PyTorch
- TensorFlow
- LangChain
- LlamaIndex
- Vector databases
- Python, Node.js
- AWS, Google Cloud, Azure
How we build it
Providers first, custom models when they earn it. Most products are better served by OpenAI, Anthropic, or Google Gemini behind a well-designed retrieval and prompting layer than by a model trained from scratch, and we say so. We train our own when the data is proprietary, the task is narrow, or the cost at volume demands it.
Grounding over fluency. A general model asked about UAE tenancy law is fluent, confident, and wrong. Every system we build is scoped to named sources — a legal framework, your knowledge base, a dataset you own — and says on the page what it reasons against, so a user can judge when it is standing off its ground.
The output has a fixed shape. Enlyte always returns a recommendation, a risk, and a blind spot; Graysen always returns discrete flagged clauses with a plain-language reason. Fixing the shape is what makes an AI product trustworthy rather than merely articulate, and it is what makes it testable.
Safety by construction, not by filter. Yoluno does not filter what the model says; it constrains what the child can reach, and puts the parent upstream of every session. Where the stakes are high, we move the boundary to where it can be guaranteed.
Evaluation before scale. The evaluation set is built alongside the product, so a prompt change or a model upgrade is measured rather than felt, and the system can be retrained or re-pointed without a rewrite.
How a project runs
Most AI projects take three to nine months from discovery to deployment; an MVP with the core functionality is possible in eight to twelve weeks. Discovery settles what the model has to be right about and what data exists to make it so. Architecture follows — provider or custom model, retrieval design, where the evaluation set comes from — before a line of product code.
Then short, reviewable sprints with a working system at the end of each, testing that includes the evaluation set and load, and a supported deployment with monitoring and a retraining path. We do not consider a milestone done until it meets the acceptance criteria we agreed.
We work from studios in Lahore and Dubai, and we build for the market we are in: Graysen reasons against UAE law in Arabic and English, and DISTRIQ launched in Dubai alongside Singapore and London.
Graysen
Reads a UAE contract in English or Arabic and names the clauses that are going to cost you.
DISTRIQ AI
Tells you what a neighbourhood is actually like before you commit to living in it.
Enlyte
Puts a hard decision through several expert lenses and returns the recommendation, the risk, and the blind spot.
Yoluno
A conversational companion for children where the parent, not the model, decides what is in the world.
AI development questions
7 answered
Most AI projects take 3-9 months from discovery to deployment. MVPs with core functionality are possible in 8-12 weeks. Complex enterprise implementations may require 6-12 months.
What sets the price is how much of the system is new: a retrieval layer over a provider model is a different build from a custom-trained model with its own data pipeline, and a feature inside your existing product is different again from a standalone platform. We scope the work in discovery and quote against fixed milestones, and an 8-12 week MVP exists precisely so you can see the system working before committing to the full build.
We build on the major model providers — OpenAI, Anthropic, and Google Gemini — and train in PyTorch and TensorFlow where a project needs its own model rather than a general one. Around them we use LangChain and LlamaIndex for orchestration and retrieval, with a vector database behind them. Which combination we reach for depends on your performance, cost, and deployment constraints.
Usually a provider model. OpenAI, Anthropic, and Google Gemini behind a well-designed retrieval and prompting layer serve most products better than a model trained from scratch, and cost less to keep current. We train custom models in PyTorch or TensorFlow when the data is proprietary, the task is narrow enough that a small model wins, or the volume makes per-call pricing the wrong shape. We will tell you which, and why, in discovery.
Retrieval-augmented generation is a way of making a language model answer from your documents rather than from its training data: the relevant passages are retrieved from a vector database first and handed to the model with the question. We use it whenever the answer has to be grounded in a specific, changing, or private source — a legal corpus, a product catalogue, an internal knowledge base — because it lets the system cite what it reasons against and be updated by changing the documents rather than the model.
Yes, and it is most of what we do. LLM and RAG integration is a standard deliverable: the intelligence runs behind its own API, as Graysen's does, and your existing application calls it. That keeps the model, the retrieval layer, and the evaluation set separable from your product's release cycle, so either can change without the other.
Deployment and MLOps are part of the build: monitoring for quality drift as well as uptime, a retraining or re-pointing path when the data or the provider model changes, and ongoing tuning. A system that is right on launch day and unwatched afterwards is not a production system.