Transform raw data into decisions your team can act on.
The problem we solve.
Your data lives in twelve different places — the CRM, the billing system, the marketing platform, three spreadsheets, and someone's inbox. Getting a simple answer like "which customers are most profitable?" takes a week and a data analyst.
When you finally get the report, the numbers don't match what finance has. Different teams use different definitions of "active customer" or "revenue," and nobody trusts the data enough to make fast decisions.
How we work.
We start with the questions you need answered, not the data you have. What decisions would move the business if you could make them faster? What metrics would change behavior if your team saw them every morning? That's the target.
We build backward from there — identifying data sources, cleaning and modeling the data, and delivering dashboards that answer those specific questions. The warehouse architecture follows the use case, not the other way around.
What's included.
Custom Dashboards & Reporting
Dashboards built for the people who use them — not the people who requested them. We design for the daily decisions your team makes, with the right metrics at the right level of detail.
Data Warehouse Architecture
We consolidate your data sources into a single, clean warehouse. Snowflake, BigQuery, Redshift — we choose the right tool for your volume and budget, and build the pipelines that keep it current.
Self-Service Analytics Enablement
Your team shouldn't need to file a ticket every time they have a question. We set up tools and training so business users can explore data, build reports, and find answers on their own.
What you get.
Data warehouse with unified schema across your key systems
ETL/ELT pipelines with automated scheduling and monitoring
Executive and operational dashboards in Looker, Tableau, or Metabase
Data dictionary documenting definitions, sources, and refresh cadence
Self-service query layer for business users
Training for analysts and business stakeholders
Ideal for
- Companies making decisions based on gut feel or outdated reports
- Teams with data spread across multiple disconnected systems
- Leaders who need real-time visibility into KPIs
- Organizations ready to build a data-informed culture
See it in action.
Technology — A MENA-region AI startup shipping two consumer-facing products
Production-Grade Multi-Product AI Backend on a Shared Core
Two AI products (one retrieval-heavy, one vision-heavy) were being built as separate backends. Duplicated auth, duplicated RBAC, duplicated observability, and diverging fast.
- Auth, RBAC, and observability built once and consumed by both products
- Per-product workers (ARQ) keep heavy work off the request path
- Token-versioning invalidates all sessions on password change with zero revocation-list growth
Finance — A Gulf-region tax and compliance advisory firm
RAG Assistant for MENA Regulatory Compliance
Advisory staff were answering the same 40-50 recurring UAE corporate-tax questions by hand, each pulling 2-3 regulatory PDFs. Turnaround averaged 6 hours and junior staff frequently missed cross-references between VAT and CT documents.
- Avg. first-answer latency under 4s end-to-end
- Retrieval precision@5 improved from 0.61 to 0.88
- L1 staff resolution rate climbed from 35% to 82%
Retail — A UAE home-furnishing retailer with a 40k-SKU catalogue
Visual Product-Discovery Assistant for Home-Furnishing Retail
Customers shared Pinterest-style inspiration photos over WhatsApp and expected matching SKUs in return. Manual matching cost 20-30 minutes per enquiry; most customers dropped off before the retailer could respond.
- Enquiry-to-shortlist time compressed from ~25 minutes to ~45 seconds
- 58% of enquiries now resolve without staff involvement
- Rate-limited (5 req / 5s burst, 20 req / 60s sustained) to cap SerpAPI spend
Let's price your data analytics & bi.
A fixed number in writing, agreed before any work begins — not a discovery call.