Data pipelines
Reliable ingestion and transformation from every source.
Engineering Layer
Data Solutions
The data layer that makes AI work: pipelines, warehousing, vector databases, and analytics.
Clean, structured, retrievable data is the difference between an AI demo and an AI product.
- PostgreSQL
- MongoDB
- Redis
- Elasticsearch
- Pinecone
- PgVector
- Dashboards
CAPABILITIES
What we build
Data warehousing
One trusted place for reporting and analysis.
Vector databases
Embeddings and retrieval infrastructure for AI features.
Analytics dashboards
Decision-ready reporting for teams and customers.
Data quality
Validation, monitoring, and lineage.
Secure data handling
Access control and compliance-aware architecture.
OUR APPROACH
Our AI-native process
Seven steps, one team. Intelligence is scoped at the start and evaluated at the end.
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01
Discovery & AI Opportunity Mapping
We start every engagement by mapping where intelligence belongs in your product: which workflows an agent can own, where an LLM adds real value, and where plain software is the right answer. You get an honest AI scope — including what not to build.
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02
UI/UX Design
Research, flows, wireframes, and high-fidelity interfaces, validated with users before development begins. For AI features, we design the states that matter: streaming, uncertainty, and human-in-the-loop control.
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03
Intelligence Engineering
Model selection, prompt and agent architecture, RAG pipeline design, and evaluation harnesses — built with LangChain, LangGraph, CrewAI, and the vector store that fits your data.
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04
Web Development
Frontend and backend engineering on React, Next.js, Node, and Python, delivered in short sprints with working software at the end of each one.
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05
Mobile Development
Native and cross-platform apps for iOS and Android, built against the same APIs and design system as the web product.
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06
Cloud & Deployment
Infrastructure as code, CI/CD, and monitored releases on AWS, Azure, or GCP — including GPU/inference infrastructure and model-cost monitoring where AI workloads demand it.
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07
Testing & AI Evals
Automated test suites cover every release. AI features get their own evaluation suites — regression tests for prompts, hallucination checks, and output-quality scoring — so intelligence ships with the same rigor as code.
FAQ
Data Solutions — common questions
How do projects start?
With a short discovery: we map goals, users, and where AI belongs, then deliver a scope, timeline, and fixed price for the first phase.
Can you work with our existing team?
Yes. We run full projects or embed engineers, designers, and AI specialists in your team.
Which time zones do you cover?
We have teams in the US and Pakistan and keep overlapping hours with US, UK, and Middle East clients.
Who owns the code?
You do. Source code, designs, and infrastructure are handed over in full.
Ready to build AI-native?
Tell us what you're building — we'll show you where AI belongs in it.