Pipelines that run unattended. A warehouse the finance team trusts on quarter close. Predictive models you can put in front of a regulator. We build the data plumbing first, then the dashboards everyone actually asked for, then the next three they will ask for next month.
Pick the engagement that matches where you actually are – a fresh warehouse, a stack of brittle pipelines that need rescuing, a BI tool nobody opens, or a model that should be in production but isn't.
Before anyone builds, we write down what the business actually wants to answer, what data already exists, and the gap between the two. The deliverable is a roadmap your CFO and your CTO both sign off on – not a 60-page deck.
Reliable pipelines from your source systems into the warehouse. Idempotent, observable, alerted, and version-controlled. The kind of plumbing where the first sign of a problem is a Slack message from us, not a wrong number in the Friday report.
Snowflake, BigQuery, Redshift, or Databricks — sized to the workload, not to the sales pitch. We design the schemas, set up the environments, and write the cost guardrails so you do not discover a six-figure query in next month's bill.
Dashboards your finance, sales, and ops teams actually open — because the definitions match what they expect, the numbers reconcile, and the filters do what the label says. Power BI, Tableau, Looker, Metabase, or Superset.
Funnel reporting that ties campaign spend to revenue, not just to clicks. Cohorted retention, LTV by segment, attribution that actually attributes, and the one chart product managers ask for in every QBR.
Demand forecasts, churn scores, propensity models, and fraud detection – built on your data, evaluated honestly, and shipped with a drift monitor so the model tells you when it stops being useful. Every model comes with a written list of what it cannot do.
Sub-minute pipelines on Kafka, Kinesis, or Pub/Sub for the workloads that genuinely need it — fraud, logistics, operational monitoring. For the rest, micro-batch is usually cheaper and just as fresh, and we will tell you when that is the honest answer.
Quality is not a slide in a deck. It is dbt tests on every model, row-count reconciliation on every load, lineage you can trace end-to-end, and a definition of "right" that finance, product, and engineering all signed off on.
White-labelled dashboards inside your product. Multi-tenant isolation, per-customer row security, fast loads on top of a shared warehouse, and the kind of UX that customers will actually pay extra for instead of exporting to Excel.
Different industries, same pattern — too much data, not enough trust in it. These are the verticals where we have built the playbook more than once.
Regulatory reporting, credit risk dashboards, AML and fraud monitoring, and treasury analytics. Audit trails built in, because the regulator will ask.
Demand forecasting by SKU and store, marketing-mix modeling, customer LTV, and inventory-turn dashboards that update before the morning stand-up.
OEE dashboards, predictive maintenance on the machines that actually break, supplier scorecards, and inbound-logistics tracking down to the lane.
Claims analytics, clinical-trial tracking, patient cohort analysis, and HIPAA-aligned warehouses. PII handling that survives a compliance review.
Product analytics, MRR and churn cohorts, expansion-revenue tracking, and a usage dashboard customer success can actually use to drive renewals.
Route profitability, on-time-delivery analysis, fleet utilisation, and the kind of operations dashboard that tells the dispatcher what to do, not just what happened.
We pick what fits your team and your budget, not what gets us a partner badge. Most engagements land on whichever of these your engineers are already comfortable with.
No surprises, no shifting scope, no slide that says "magic happens here". Every stage has a deliverable your team can review and reject before the next one begins.
We inventory the source systems, profile the data for quality, write down which questions the business wants answered, and define what "right" looks like for each one. Output: a one-page scope and an honest list of risks.
Pick the warehouse, design the dimensional model, decide what runs in batch versus stream, and document the contracts between source systems and the warehouse. The diagram fits on a single page or it is not ready.
Two-week sprints with dbt tests on every model, row-count reconciliation against source on every load, and a staging environment your analysts can poke holes in before anything hits production.
Build the dashboards finance and ops asked for, plus the three they will ask for next month. Train the team, write the methodology notes, and run a two-week parallel with whatever they were using before.
Freshness monitors, anomaly detection on the metrics that matter, a defined process for new metric requests, and a named engineer who answers when a number looks wrong on a Friday at 4pm.
Analytics platforms are juicy targets. They concentrate every system's data into one place — which is the point, but it is also the problem. We design the build so the predictable failures cannot happen.
TLS 1.2 or higher in transit, AES-256 at rest, customer-managed keys when your compliance team wants them. Backup encryption too — not just the warm copy. The certificate rotates on its own; nobody has to remember.
Sales sees their region. Finance sees the totals. HR sees salaries; nobody else does. Access policies live in code, get reviewed in PRs, and apply uniformly whether the user is in Tableau, dbt, or the raw warehouse console.
Automated PII scanning on ingestion. Tokenisation for analytical use, full masking for non-prod, and a documented retention policy that respects GDPR Article 17 and India's DPDP Act. No spreadsheets of customer phone numbers floating around.
Every query logged, every model versioned, every dashboard change traceable to a person and a ticket. Column-level lineage so when finance asks "where does this number come from", the answer takes 30 seconds, not a week.
We are not the cheapest analytics shop. We are also not a consultancy that ships a deck and disappears. Four reasons teams stick with us past the first dashboard.
Every model has dbt tests. Every load reconciles against source. You hear about a broken pipeline from us, on a Tuesday — not from a board member on a Friday.
We will tell you when "real-time" actually means "fresh by 9am". We will flag the query that costs ₹40,000 a week and rewrite it. The cloud bill is part of the deliverable.
Column descriptions in dbt. Methodology notes alongside every dashboard. A README that tells the next engineer how to add a metric. We write for the team that inherits the work.
A two-week parallel with your analysts. A 30-day warranty. A choice to extend on support or not — no five-year contracts. The goal is your team running the platform, not us being permanent.
If yours isn't here, a 30-minute discovery call is usually faster than email.
A focused dashboard built on an existing warehouse usually ships in four to six weeks. A new warehouse with ETL pipelines, modeled data, and three to five executive dashboards sits in the ten to sixteen-week range. Multi-domain platforms with governance, lineage, and a self-service layer typically run four to six months. Anything shorter than four weeks tends to be a one-off report dressed up as a project — fine, but a different conversation.
Yes. Most engagements start with the stack you already pay for. We build on Snowflake, BigQuery, Redshift, Databricks, and Postgres. We publish into Power BI, Tableau, Looker, Metabase, and Superset. We will only recommend a migration if the current setup is genuinely costing you more than the switch — and we will put numbers behind that claim before suggesting it.
Three layers. First, dbt tests on every model — uniqueness, not-null, referential integrity, and business-rule assertions. Second, row-count and total reconciliation against source systems on every load. Third, anomaly detection on the metrics that matter most, so finance hears about a broken pipeline from us, not from a board member at the wrong moment.
Yes. Kafka or Kinesis for the bus, Flink or Spark Structured Streaming for the processing, and a serving layer that fits the question — Pinot or ClickHouse for sub-second OLAP, Materialize for incremental SQL views, or just a warehouse with micro-batch loads when the business actually means hourly. We push back when "real-time" really means "fresh enough for the 9am meeting" — it usually saves a few zeros on the bill.
That is the whole point. Every model is documented in dbt with column-level descriptions. Every dashboard ships with a one-page methodology note explaining the definitions, the filters, and the known caveats. We run a two-week handover with your analysts and a 30-day warranty on anything we shipped. After that, support is a separate contract — not something you are locked into.
Both. Demand forecasting, churn scoring, propensity-to-pay, fraud scoring, and inventory optimisation are the most common ones we ship. We work in scikit-learn, XGBoost, LightGBM, and PyTorch when the problem warrants it. Every model comes with a baseline, a holdout evaluation, a drift monitor, and a written explanation of what it cannot do — because the second one matters more than the first.
PII gets discovered on ingestion, tagged in the catalog, masked or tokenised in analytical layers, and removed from non-production environments. Retention policies are documented and enforced in code. For HIPAA workloads we run in a dedicated, encrypted environment with BAAs in place. For GDPR and India's DPDP Act, subject-access requests get a defined process — not a panic email when one arrives.
That is the normal starting point, not the exception. We spend the first sprint profiling the data, finding the obvious quality issues, and writing down the assumptions everyone has been making silently. The fix is rarely a "data cleanup project" — it is usually a few well-placed contracts at the source plus transformation rules in the warehouse, applied consistently. We will tell you which of the two it is on week two.
Most of our engagements are hybrid. We lead architecture and the first releases, your analysts and engineers pair with ours, and ownership transfers as the project moves. The code and documentation we produce is built to survive that handover. If you would rather we stay fully in charge, that is also fine — but it is not the default we recommend.
Fixed-price for scoped work like a warehouse migration, a defined set of dashboards, or a specific model. Time-and-materials on long-running platform engagements where requirements evolve sprint to sprint — which is more honest than padding a fixed price to cover unknowns. You see the rate card, the team, and the timeline before anything is signed. Indicative ranges are shared on the first call so nobody wastes a month on a proposal.
A KPI you can't trust. A dashboard nobody opens. A pipeline that breaks every Wednesday. We will come back with a one-page diagnosis and a realistic timeline. No 40-slide proposal, no upsell on a platform we don't yet know you need.
