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Data Analytics · Dashboards · Decision Intelligence

Turn your data into decisions, not just charts

Most teams have data everywhere and answers nowhere. We build the full stack, data warehouse, a governed metrics layer, and the dashboards on top, so every number has one trusted source. Then we add an AI layer: ask questions in plain English, get alerted when a metric moves abnormally, and let the numbers explain themselves.

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RG INSYS LLP builds decision intelligence systems, the data plumbing and dashboards that let teams act on their numbers with confidence. We consolidate scattered sources (product databases, Stripe, HubSpot, Google Analytics, spreadsheets) into a single warehouse on PostgreSQL, BigQuery, Snowflake or ClickHouse; model metrics once in dbt so every chart agrees; and surface them through internal KPI dashboards (Metabase, Apache Superset, Power BI) or customer-facing embedded analytics built in React. On top we add an AI layer for plain-English text-to-SQL querying, automated anomaly alerts and auto-written insight summaries. UK, US, UAE and Indian clients in SaaS, fintech, ecommerce and logistics rely on us to replace dashboard chaos with one source of truth.

What we deliver
Data warehouse setup, ETL/ELT pipelines, dbt metrics modelling, internal KPI dashboards, customer-facing embedded analytics, plain-English (text-to-SQL) querying, anomaly alerting, auto-written narrative insights, data quality tests and monitoring.
Typical timeline
2 to 4 weeks for a first warehouse and live KPI dashboard. 6 to 12 weeks for embedded customer-facing analytics inside your product. Ongoing on a managed analytics retainer.
Pricing from
$9,000 fixed-price first dashboard build. Or $3,500/month managed analytics retainer covering new dashboards, embedded analytics, the AI layer and pipeline maintenance.
Stack
PostgreSQL, BigQuery, Snowflake, ClickHouse, DuckDB, dbt, Airbyte, Fivetran, Metabase, Apache Superset, Power BI, Looker, Recharts, D3, Tremor, Kafka, OpenAI / Claude (text-to-SQL).
Best fit for
SaaS products that need customer-facing analytics, ops and finance teams drowning in spreadsheets, and founders who want one trusted dashboard instead of five that disagree.
What's included

From raw data to confident decisions

🔌

Data ingestion & pipelines

We pull your data out of product databases, Stripe, HubSpot, Salesforce, Google Analytics, ad platforms and spreadsheets into one place. Reliable scheduled syncs with Airbyte / Fivetran or custom pipelines, with freshness monitoring so you know the moment a source breaks.

🏛️

Warehouse & metrics layer

A proper cloud data warehouse (PostgreSQL, BigQuery, Snowflake, ClickHouse) with metrics modelled once in dbt. "Active customer", "net revenue" and "churn" are defined a single time, documented, and reused everywhere, so your charts never disagree again.

📊

Internal KPI dashboards

Clean, fast dashboards for revenue, operations, product usage, pipeline and cohort retention. Built in Metabase, Apache Superset or Power BI, role-based access, scheduled email/Slack digests, and drill-downs that answer the next question without a data analyst.

🧩

Embedded & white-label analytics

Ship analytics inside your own SaaS product. Custom React dashboards (Recharts, D3, Tremor) that match your UI, served through a secure multi-tenant layer with row-level security so each customer sees only their own data. A retention feature you can charge for.

🤖

AI insight layer

Ask questions in plain English and an LLM writes the SQL against your governed models and returns the chart. Automated anomaly detection watches key metrics and alerts Slack/email when something moves. Auto-written summaries explain what changed and why, in plain language.

✅

Data quality & governance

Automated tests for freshness, uniqueness, null and referential integrity run on every refresh, with alerts when a source drifts. Documented definitions, lineage and access controls. The AI layer only ever queries governed models, so plain-English answers stay trustworthy.

Our method

How an analytics build actually unfolds

01
Decisions workshop, week 1

We map the questions your team needs answered each week and the decisions behind them, then work backwards to the metrics and data sources. Output: a metrics definition doc and a prioritised dashboard scope, no guesswork.

02
Pipelines & warehouse, weeks 1 to 2

Connect your priority sources, land the data in a warehouse and model the core metrics in dbt with data tests. One trusted, documented layer that everything else builds on.

03
Dashboards & AI layer, weeks 2 to 4

Build the live KPI dashboards (or embedded analytics for your product), wire up role-based access and scheduled digests, and switch on plain-English querying and anomaly alerts. Demo against your real numbers.

04
Operate, expand & tune

New dashboards as questions evolve, more sources, embedded analytics rollout, and ongoing pipeline maintenance with monitoring. Optional managed retainer so the data stays fresh, correct and trusted.

Our tech stack for data analytics & dashboards

We default to open, ownable tools so you are never locked into per-seat licences or a black-box vendor. Data lands in a warehouse you control, metrics are modelled in dbt so the logic is versioned and reviewable, and dashboards run on open-source BI (Metabase, Superset) or a fully custom React layer when analytics is a product feature. The AI layer sits on top of governed models, so plain-English answers are grounded in your real numbers.

PostgreSQL BigQuery Snowflake ClickHouse DuckDB dbt Airbyte Fivetran Kafka Metabase Apache Superset Power BI Looker Recharts / D3 Tremor OpenAI / Claude (text-to-SQL)
Proof

A representative case study

SaaS · UK B2B SaaS platform

Embedded customer analytics for a B2B SaaS platform, 10 weeks

A UK SaaS company was fielding constant "can you pull these numbers for me" requests from customers, each one a manual CSV export. We replicated their Postgres into a ClickHouse warehouse, modelled the metrics in dbt, and built a white-label React analytics dashboard embedded in their product with row-level security so each tenant sees only their own data. We added plain-English querying so customers could ask their own questions. The feature shipped on a new paid tier and removed the manual export work entirely.

100%Manual CSV exports removed
New tierAnalytics as a paid add-on
10 wksScope to launch
2 devsTotal team size

See more case studies →

Pricing

Transparent pricing for data analytics

From $9,000

Fixed-price first build: warehouse setup, priority sources connected, a modelled metrics layer and a live KPI dashboard. Or move to a $3,500/month managed analytics retainer covering new dashboards, embedded analytics, the AI layer and pipeline maintenance.

  • Decisions workshop and documented metrics definitions
  • Warehouse plus modelled, tested data, one source of truth
  • Live KPI dashboard against your real data, not a template
  • Plain-English querying and anomaly alerts included
Full pricing & engagement models →

All pricing transparent. No hidden fees. Free 48-hour written estimate.

FAQ

Common questions

We start with the decisions, not the data. In a short workshop we map the 5 to 10 questions your team actually needs answered each week (where is revenue leaking, which customers are at risk, which campaigns pay back) and work backwards to the metrics and the data sources behind them. We then consolidate spreadsheets, product databases and SaaS tools (Stripe, HubSpot, Google Analytics, your app DB) into a single warehouse so every number has one trusted source. You get a working dashboard against your real data, not a generic template.
Internal dashboards are for your own team: revenue, operations, churn, pipeline, a single source of truth for the business. Embedded (or white-label) analytics is a dashboard you ship inside your own SaaS product so your customers can see their own data. Embedded analytics is a revenue feature, it increases retention and is often something you can charge for. We build both, and the data warehouse and modelling layer underneath is shared, so adding customer-facing analytics later is incremental, not a rebuild.
Usually not. For most teams we recommend Metabase or Apache Superset, open-source tools you own and host, with no per-seat licence creep. We use Power BI or Looker when you are already in that ecosystem, and we build fully custom React dashboards (Recharts, D3, Tremor) when analytics is a core product feature and the UX has to match your app. We recommend based on your budget, team size and whether the dashboard is internal or customer-facing, not on a vendor relationship.
It is the part that turns a dashboard from a wall of charts into answers. Three concrete things: (1) plain-English querying, ask "what was MRR by plan last quarter" and an LLM writes the SQL against your modelled tables and returns the chart; (2) automated anomaly alerts that watch your key metrics and message Slack or email when something moves abnormally, so you do not have to stare at dashboards; (3) auto-written narrative summaries that explain what changed and why in plain language. The AI runs on top of governed, modelled data, so answers are grounded in your real numbers, not hallucinated.
Every metric is defined once in a modelling layer (dbt) with documented business logic, so "active customer" or "net revenue" means the same thing on every chart. We add automated data tests (freshness, uniqueness, not-null, referential integrity) that run on every pipeline refresh and alert when a source breaks. The AI text-to-SQL layer queries these governed models, never raw tables, which is how we keep plain-English answers trustworthy. You get one source of truth, not five dashboards that disagree.
Yes. We keep analytics off your production transactional database by replicating data into a separate warehouse or a columnar store (ClickHouse, DuckDB, BigQuery) built for aggregation. Your app stays fast, and heavy analytical queries never compete with live user traffic. Embedded dashboards are served through a secure, multi-tenant layer with row-level security so each customer only ever sees their own data.
A focused first build, warehouse setup, one or two priority data sources connected, a modelled metrics layer and a live KPI dashboard, typically takes 2 to 4 weeks and starts at $9,000 fixed price. From there many clients move to a managed analytics retainer from $3,500/month for new dashboards, embedded analytics, the AI layer and ongoing pipeline maintenance. Pricing is transparent and you get a written scope before any work starts.
You do, from day one. The warehouse runs in your cloud account, the dbt models and pipeline code live in your repository, and the BI tool (Metabase, Superset) is self-hosted on your infrastructure or your licence. There is no proprietary lock-in and no data leaving your control. We document everything so any competent data team can take it over.
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