CLAN Developers
A centralised management system for a property developer: property tracking, transactions, revenue analytics and operational reporting in one place.
Build something similarWe turn scattered data into one source of truthlive Power BI dashboardsAI-ready dataanswers you can ask forbetter decisions.
Pipelines, warehouses, Power BI and data agents — so every number is trusted, every dashboard stays live, and your AI finally has data it can use.
Connects every source you already have — and the platforms you choose
HubSpot
Salesforce
Shopify
Stripe
QuickBooks
Xero
Zoho
Odoo
Google Sheets
Airtable
Notion
Slack
WhatsApp
Google Analytics
Google Search Console
PostgreSQL
MySQL
MongoDB
Supabase
Snowflake
Google BigQuery
Databricks
Power BI
Looker
Metabase
Apache Kafka
Airbyte
dbt
Apache Airflow
OpenAI
Anthropic
n8nIn 2026, people ask questions in plain words and AI agents answer from your data. That only works when the data underneath is clean, governed and defined once. That’s what we build.
Which region grew fastest this quarter?
EVALUATE TOPN(5, SUMMARIZE(Sales, Region[Name], "Growth", [Revenue QoQ %]), [Growth])North grew fastest — up 31%, driven by repeat customers. Online is close behind at 27%.
Why did sales dip in March?
SELECT month, new_customer_orders, repeat_orders
FROM semantic.orders WHERE year = 2026 ORDER BY monthNew-customer orders fell after the February campaign ended; repeat orders held steady. Sales recovered in April.
Which accounts might churn next month?
SELECT account, churn_risk FROM ml.churn_scores
WHERE month = next_month() ORDER BY churn_risk DESC LIMIT 5Five accounts show lower usage and late payments. A follow-up list is ready for your team — nothing is sent without approval.
Illustrative example
From the first pipeline to a data platform your whole company — and its AI — runs on.
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Your business data — tables, PDFs, emails and chats — cleaned, connected, defined once and secured, so AI agents and people get the same right answer.
Plan your data platformReliable pipelines from every app and database — batch or real time, tested, monitored and self-healing.
Snowflake, BigQuery, Databricks, Microsoft Fabric or Postgres — on open table formats like Apache Iceberg, so you’re never locked in.
Semantic models, DAX and dashboards people actually use — now built faster with Power BI’s AI agents, reviewed by us.
Natural-language analytics on governed metrics — in Power BI, Fabric, Snowflake, Databricks or your own app.
Kafka, change data capture and streaming so dashboards and alerts update in seconds, not overnight.
Forecasts, churn and lead scores and anomaly alerts — trained on your data and kept in production.
Tests, data contracts, lineage, row-level security and PII masking — trust you can prove.
From Excel, Access and on-prem SQL Server to a modern cloud stack — without losing history.
Dashboards for developers, consultancies and multi-branch businesses — and the data pipelines behind AI products people use every day.
A centralised management system for a property developer: property tracking, transactions, revenue analytics and operational reporting in one place.
Build something similar
A CRM and management portal that tracks every lead and application, with real-time dashboards and performance analytics.
Build something similar
A full-scale ERP across 38 branches nationwide — finance, case management and CRM in one system, with analytics like student university preferences.
Build something similar
Pakistan’s AI legal assistant. Ask in English or Urdu and get a plain answer with the statutes and judgments it rests on; lawyers search judgments, citations and statutes with court and year filters.
Visit pakistanlawbot.com
A knowledge assistant over Professor Khurshid Ahmad’s body of work — his writing indexed for accurate, context-aware answers.
Visit khurshidanswers.org
Syed Abul A’la Maududi’s books, made searchable through conversation in Urdu and English.
Visit pixelpk.com/ask-maududi-case-study/
Reads incoming invoices with OCR, extracts and checks the fields and organises them automatically — no more manual data entry.
Build something similar
Turns every Google review into structured data — sentiment, topics and concerns — and drafts a personal reply.
Build something similarBefore we build your data platform, we build ours. These pipelines and dashboards run every day — the same patterns we bring to your business.
Reads every system we run — each one’s own database, read-only, plus the APIs we use — into one overview: status, headline numbers, 14-day activity and what needs attention.
Merges every lead file — CSV, Excel, JSON — with our lead server and email tool into one database: one row per email, clear rules for which source wins, and clean exports.
Pulls Search Console data, crawls every page, checks AI-crawler access and rankings, stores it all — then an AI writes a short brief of what to fix.
Collects launches, founder communities, app charts and news, scores the opportunities and briefs us on a dashboard.
Six quick questions. Gartner warns that through 2026, organisations will abandon 60% of AI projects that lack AI-ready data — see where you stand.
Start with one source of truth: connect your top systems into a warehouse and build one dashboard everyone trusts.
Add a semantic layer and data tests, then pilot an ask-your-data agent with one team.
You’re ready for agents: expose governed metrics over MCP and automate routine decisions — with guardrails.
Tap a layer to see what we do there and the tools we use. We pick for your size and budget — not for the biggest invoice.
We connect the systems you already run — CRM, ERP, store, finance, spreadsheets, analytics — plus the unstructured data most teams ignore: PDFs, emails and chats.
HubSpotSalesforceShopifyStripeQuickBooksOdooGoogle SheetsGoogle AnalyticsPostgreSQLMongoDBManaged connectors where they fit, custom Python where they don’t. Change data capture and streaming for anything that can’t wait until tomorrow.
AirbyteApache Kafka
Apache FlinkApache Airflow
Prefect
Pythonn8nThe right home for your size and budget — on open table formats, so tomorrow’s tools (and agents) can read the same data without copies.
SnowflakeGoogle BigQueryDatabricksPostgreSQL
DuckDB
ClickHouseSupabaseTested transformations and one set of business definitions — revenue, customer, churn — that every dashboard, app and AI agent uses.
dbt
Apache Spark
pandas
Polars
TrinoPower BI and BI tools for people; APIs and MCP endpoints for apps and AI agents; RAG for chat over your documents.
Power BILookerMetabase
Apache Superset
GrafanaOpenAIAnthropic
LangChainWho can see what, where every number comes from, whether data is fresh — and what it all costs. Alerts before anyone notices a broken dashboard.
GitHub Actions
DockerGrafanaWe start with one high-value dashboard or data product and grow from there — so you see value early.
We map your sources, questions and quick wins — then plan a stack that fits your size and budget.
Pipelines bring every source together; tests catch bad data before it reaches a report.
One semantic layer: the metrics your business runs on, defined once and documented.
Power BI dashboards, alerts and data agents — answers where your team already works.
Freshness, quality and cost watched every day, with new data products as you grow.
More and more business questions are now asked by AI agents working for your team. The companies getting value from AI are the ones whose data those agents can trust.
of AI projects without AI-ready data will be abandoned through 2026. The model is rarely the problem — the data is.
of organizations don’t have — or aren’t sure they have — the right data management practices for AI.
of agentic analytics projects that rely only on MCP will fail by 2028 without a consistent semantic layer.
of day-to-day work decisions will be made autonomously by agentic AI by 2028, up from 0% in 2024.
Describe the report; Power BI’s agents draft the model and visuals — we review and ship.
Natural-language answers on governed metrics in Power BI, Fabric, Snowflake and Databricks.
PDFs, emails and chats turned into searchable knowledge for RAG and agents.
Streaming and change data capture — numbers that update in seconds.
Apache Iceberg lets every engine and agent read the same data.
Already in production: our Super Dashboard, Lead Master, SEO Desk and Business Radar pipelines run every day, and LawBot, Khurshid Answers and Ask Maududi answer from data we prepared. Your platform gets the same care.
We treat you like a partner: we start with the questions your business needs answered, then build the platform to answer them — and keep it running.
Every team works from the same numbers — no more “which spreadsheet is right?”
Dashboards and alerts that update themselves, from data you can trace.
Clean, defined, secured data that agents and RAG can actually use.
Row-level security, PII masking and audit trails built in from day one.
We size the stack to your business and watch warehouse spend for you.
Your data, code and dashboards — documented and owned by you.
Not sure where your data stands? Talk it through in a free 30-minute strategy call.
If your numbers live in more than a few apps and spreadsheets, almost certainly. A warehouse or lakehouse gives you one place for clean, historical data — and it’s what dashboards and AI agents should read from. For smaller teams we often start with Postgres or BigQuery and grow from there.
Power BI is our most-requested tool and a great fit if you use Microsoft 365. We also build with Looker, Metabase and Superset, or dashboards inside your own app. We recommend based on your team, licences and budget.
Data that is connected, clean, defined once (a semantic layer), secured and traceable — including documents and messages, not just tables. It’s what lets AI agents and chatbots give answers you can trust.
Yes, with the right setup: read-only access, row-level security, governed metrics exposed through MCP endpoints, and an audit trail of every query. Agents see only what the person they act for is allowed to see.
It’s one shared definition of your business metrics — revenue, active customer, churn. Without it, people and AI calculate the same number differently. Gartner predicts that by 2028, 60% of agentic analytics projects relying only on MCP will fail without one.
We start with one high-value dashboard or data product, so you see value early, then expand. We’ll give you a realistic plan after a free strategy call.
You do — the data, pipelines, models and dashboards are yours, documented and in your accounts.
Tell us the questions your business can’t answer today. We’ll map the data, the stack and the first dashboard — and show you what AI can do with it — in a free 30-minute strategy call.