We build the ingestion, warehouse, and orchestration layer that everything else on your data stack sits on, then we run it. Sources land on a schedule, schema changes get handled before they break anything downstream, and when a load fails, we know before you do.
No reseller commissions · You own the code, the infra, and the warehouse
Sources
CRM · store · ads · DB
Ingest
Raw / landing
Transform
Warehouse
Trusted by teams in the US, UK, and India
Siddhraj
Unoloft
Kofeko
3nStar
Veda
Cerata
Shubham
Consultup IndiaSiddhrajUnoloftKofeko3nStarVedaCerataShubhamConsultup IndiaManaged connectors are very good and getting cheaper. Most companies should use them for most sources. Find your situation below.
You need Shopify, HubSpot, Stripe, and Google Ads in one place, and none of them is enormous.
Verdict: Buy the connectors, don't build them. Airbyte or Fivetran will do this in an afternoon for less than we'd charge to reproduce it, and doing it well is genuinely somebody else's full-time job. What you may still want from us is the warehouse underneath and someone to own it, that's a much smaller scope than a pipeline build, and we'll quote it that way.
There's a connector for four of your sources and none for the fifth, a legacy ERP, a regional logistics provider, an internal database, a supplier who sends CSVs by email.
Verdict: This is the job. Unsupported sources are where custom engineering earns its money, and where most stacks quietly stall. We build the connector properly, incremental, idempotent, rate-limit-aware, with backfill, and it sits alongside your managed connectors rather than replacing them.
Managed platforms price on rows synced, not on business value. One high-churn table can multiply a bill while your revenue sits still.
Verdict: Worth an hour of arithmetic before anything else. Sometimes the fix is a configuration change and you keep the platform. Sometimes two tables account for most of the bill and moving only those two onto owned pipelines pays for the build in months. We'll tell you which, and the first answer is common.
The pipeline runs, on a VM someone set up, on a schedule nobody documented, and when it fails you find out because a client asks why a number looks wrong.
Verdict: This is a rescue, and it's usually cheaper than you think. We rarely rebuild these. We wrap what exists in orchestration, monitoring, and version control, document it, and take the pager. The engineering is sound more often than not, what's missing is everything around it.
Illustrative shape, not measured data. The crossover depends on your row volume and churn, not on a fixed timeline.
A managed connector costs you money every month and costs you nothing to run. A custom pipeline costs money once and then costs attention forever. The crossover isn't a row count, it's whether anyone on your side is willing to own the thing at 3am. If the answer is nobody, buy the connector or buy our monthly plan. Don't build a pipeline you're planning to ignore.
Managed connectors where they exist, custom ones where they don't, REST and GraphQL APIs, SFTP drops, database replicas, change-data-capture off Postgres or MySQL, and the supplier who still emails a spreadsheet. Incremental by default, idempotent so a re-run can't double-count, and rate-limit-aware so you don't get throttled into a two-day backlog.
Best for: anyone with a source no platform supports. That's most people, once you get past the top ten SaaS tools.
One place where everything lands and stays, with history retained even where the source overwrites it, which is most of them. Raw and modelled layers kept separate so a transformation bug never destroys the original. Postgres, BigQuery, ClickHouse, or Snowflake depending on your volume and budget, and we'll pick the cheapest one that works, because we earn nothing on the choice.
Best for: everyone. This is the foundation, and it's the piece most often skipped in favour of syncing straight into a BI tool.
Jobs with declared dependencies, retries with backoff, alerting that names the failed step, and a run history you can look at. Dagster or Airflow where the graph is real; something lighter where it isn't, because a four-source stack does not need a distributed scheduler and we won't sell you one.
Best for: anyone whose current answer to “what runs when?” is a cron file and a person's memory.
Freshness checks on every table, row-count anomaly detection, schema-drift handling that quarantines rather than crashes, and alerts that reach a human in Slack. Plus a last-updated timestamp visible wherever the data is consumed.
Best for: everyone, and it's the line most often cut from a competing quote.
Moving off a legacy warehouse, consolidating after an acquisition, or loading eight years of history into a new one. Reconciled row by row against the source, with a documented cutover and a rollback path, so you're never trusting a migration on the strength of a total that looked about right.
Best for: one-off projects with a hard deadline. Priced separately from ongoing pipeline work.
We stop at the modelled warehouse. Deciding what the numbers mean, agreed metric definitions, a semantic layer, self-serve exploration, is business intelligence, and it's a different scope with a different conversation. If you just need one screen answering a known set of questions, that's a custom dashboard and it's smaller than either.
Pipelines rarely fail loudly. That's the whole problem, a crash gets fixed on Tuesday, and silent wrongness gets discovered in a board meeting.
A vendor renames a field, adds a required parameter, or deprecates a version. Your loader either errors out or, worse, keeps running and writes nulls into a column that used to hold revenue.
The control: schema contracts on every source. Unexpected changes quarantine the affected rows and alert, rather than failing the whole run or silently accepting the new shape. Additive changes flow through; breaking ones stop at the gate.
The load didn't run. The charts drew perfectly, using yesterday's data. Nobody noticed for three weeks, and after that nothing in the warehouse was trusted again, including everything that was still correct.
The control: freshness monitoring on every table with an expected-arrival window, and a visible last-updated timestamp everywhere the data is consumed. Trust is lost once and regained slowly. The monitoring costs almost nothing by comparison.
Something failed halfway. Someone re-ran it. The load wasn't idempotent, so half the rows landed twice, and the fix was a manual delete against production that nobody wants to talk about.
The control: every load is idempotent and keyed. Re-running any window produces the same result as running it once, which means recovery is a button rather than a judgement call.
It worked at 50,000 rows. At two million it takes six hours, overlaps the next scheduled run, and the warehouse bill is now a line item somebody's asking about.
The control: incremental loading from day one, even where a full refresh would be simpler at current volume. Partitioning and clustering decided at build time, not retrofitted under pressure.
The credentials were in someone's environment. The transformation logic was in a notebook. There was no repository, no documentation, and no second person who had ever looked at it.
The control: everything in version control, infrastructure as code, credentials in a managed secret store, and a written runbook covering the failure modes above. Handed over whether or not you keep us on afterwards.
Four of these five are decisions made before any data moves. None of them are about which warehouse you picked.
Shopify · Amazon Seller Central · WooCommerce · HubSpot · Salesforce · Pipedrive · Stripe · QuickBooks · Xero · NetSuite · Zoho · Google Ads · Meta Ads · Google Analytics 4 · Postgres · MySQL · MongoDB · SFTP and CSV drops · anything with an API, and several things without one
We hold no reseller relationship with any vendor on this list and take no commission on any of it. That's unusual in this category and it's the reason we can tell you a $50/month Postgres instance is enough.
ETL transforms data before it lands. ELT lands it raw and transforms it inside the warehouse.
Verdict: ELT, almost always, for the companies we work with. Storage is cheap, warehouse compute is fast, and keeping the raw layer means a transformation bug is a re-run rather than a re-extraction. ETL still wins in two cases: when you're legally not allowed to land the raw data, and when the source volume is large enough that filtering before load saves real money. Both are rarer than the internet suggests.
A warehouse stores structured, modelled data for querying. A lake stores raw files of any shape, cheaply, for processing later.
Verdict: You almost certainly want a warehouse. Lakes solve a problem, unstructured data at volume, machine-learning workloads on raw files, that most mid-market companies don't have. A lake bought without that problem becomes a folder of files nobody queries. If your data is rows and columns from business systems, a warehouse is the answer and the lakehouse conversation is a distraction.
Moving history once and syncing continuously are different projects, and quoting them together is how scopes get confused.
Verdict: Separate them, and do the migration second. Build the ongoing pipeline first, confirm it's landing correctly, then backfill history through the same code path. Migrating first with a separate script means you've written the logic twice and reconciled it never. Migration is priced as its own fixed scope.
Send us your source list and roughly what you're paying today. We'll tell you which sources are worth owning, which to leave on a managed connector, and whether the honest answer is that your current setup needs monitoring rather than replacing.
Book a 30-minute technical callWe list every system, what's in it, how it can be reached, and what it costs to extract. This is also where we tell you which sources aren't worth pulling yet. You get the list whether or not you go ahead.
One source, ingested, landed, modelled, and visible, with monitoring attached. It proves the architecture and gives you something to look at before most of the budget is committed.
First source lands hereThe rest, in the order you'll actually use them rather than easiest-first. Each one gets freshness checks, schema contracts, and a documented failure mode as it lands.
Backfill through the same code path as the live pipeline, then reconcile against the source system row by row. You get the reconciliation output, not a summary of it.
Repository, infrastructure as code, runbook, and a walkthrough with whoever will own it on your side. Then the monthly plan starts and the alerts route to us.
Most vendors say "ongoing support" and mean "email us." Here's what ours covers.
Freshness and failure alerts route to us, not to your team. You hear about a broken load from us alongside what we've already done about it.
Vendors change APIs. When one does, adapting the connector is inside the plan, not a change order.
A source was down, a field was wrong, a correction landed late. Re-running a window is routine work and it's covered.
Warehouse and connector spend reviewed monthly. If a table is costing more than it's worth, we'll say so, including when the saving comes out of our own scope.
A new field, a new table, a tweak to a transformation. Anything under the agreed threshold gets done without a quote.
The same person, who knows your stack. Not a queue and not a rotating ticket.
This is data-specific ongoing work. For general software maintenance across everything else you run, see infrastructure & monitoring.
You need data out of several systems and into one place, reliably. If that's not quite it, one of these is closer:
You need to agree what the numbers mean and let people explore them.
You need one screen answering a known set of questions.
You need something to happen automatically when data changes.
You need the stock number to be right across channels.
You need a single financial and operational system of record.
You need a screen where a specific piece of work gets done.
If you're not sure, the discovery call sorts it in twenty minutes and we'd rather route you correctly than sell you the wrong scope.
| Pipeline build | Migration project | Data partner | |
|---|---|---|---|
| Best for | a defined set of sources, warehoused and monitored, once. | moving a legacy warehouse or loading history into an existing one. | systems that need ongoing eyes, most clients end up here. |
| Includes | Fixed scope, fixed price. Source audit, warehouse setup, agreed source list, monitoring, documentation, handover. Migration and backfill quoted separately. | A defined move, with reconciliation output and a documented cutover included. Priced on source count and history depth, not on hours. | Monitoring, alerting, source-change handling, backfills, cost review, and small additions, with a named engineer. Available on systems we built and on systems we didn't, the second case starts with a two-week audit before we take the pager. |
| Pricing | Scoped and quoted after the source audit | Priced once we know source count and history depth | A monthly plan sized to your pipeline |
Book a 30-minute call. Bring your source list. We'll tell you what's worth owning, what to leave alone, and give you a fixed price for the part that's worth building.
