Data · Automation · AI
The framework is built. The model is yours.
We built PDQ to remove the repetitive work and the technical idiosyncrasies that make running your own data warehouse expensive and hard to trust. Production-ready orchestration, quality monitoring and active metadata from day one — on any database, any storage, any infrastructure.
Simplifying Analytics
ScrollThree ways in
Three clear entry points. The rest of the story is further down, and it gets more detailed the further you read.
Trusted by
Outcomes
runs and develops it today. It used to take 2–3.
Svenska Lotterier och Insamlingar
from kickoff to a new data warehouse in daily use across the organisation.
Nobina
is what a new report takes today. The same thing used to sit in a month-long cycle.
Nobina
Business value
Five questions that decide what your data really costs — and how PDQ answers them.
Fewer, and no single key person. Load logic and documentation are generated from the model, so day-to-day operations need fewer specialists and do not depend on whoever built it.
Yes. Every run logs source, rules and version, so the answer is already there when the auditor, the board or a regulator asks — nobody has to dig it out after the fact.
An adjustment, not a project. An acquisition, a new product or a new system is added to the model without rebuilding what is already there, which means lower cost and less time per change.
Yes. PDQ sits on top of your database, storage and infrastructure, so you can change one part at a time when it suits you. Knowing a switch is possible gives you a stronger hand in every negotiation.
Within weeks. The first flow goes into production with monitoring and quality control from the start, so you get usable data straight away instead of paying for a long start-up project.
What we do
The best of both worlds: the assurance of an experienced partner and the innovation of a specialised product company. We do not just hand over advice, and we do not just throw a licence over the fence. We build it, we ship it, and we own the outcome.
Build vs. Buy
The choice is usually framed as binary. It is not — and both of the familiar routes carry a cost that rarely makes it into the business case.
Build it yourself
Buy off the shelf
Simplitics
Offering
The only thing we know about the future is that it will change. PDQ is built for exactly that: its architecture is made of parts that can be replaced and built on one at a time. We have migrated and replaced a number of older data warehouses and know what needs doing — and we do it step by step, with the business running throughout.
You have nothing in place, or want to start over. We build all the way to production: model, flows, quality and reports. The foundation is PDQ, so the time goes into what is unique to you, and you never have to build and maintain a platform of your own.
Senior architects at your side for the big decisions. We help you avoid the pitfalls and take a proven route, focused on what creates value for the business.
We take responsibility for keeping it working: monitoring, support, incident handling and continued development. You choose the level — we can run the whole thing or back up your own team.
How we work
A typical first engagement, week by week. Not the whole data warehouse in a month — but the first flow all the way out, on real data.
Doesn’t this take months?
We start with one bounded engagement rather than a programme. Current state, target state and the actual constraints — technical, organisational and budgetary.
The framework is already built.
The framework goes up while the business works through what the data has to answer. Neither track waits for the other.
Who runs this? It runs itself.
The model generates the load logic, quality rules run inside the flow and orchestration takes the nights. Nobody hand-writes ETL — that is the whole point of PDQ.
Can we add sales too?
More sources in, same model and same quality bar. This is where the analysis starts saying something the business did not already know.
Is that in production already?
The reports stand on quality-assured, traceable data rather than on a spreadsheet. What gets accepted is the delivery, not a demo.
We will take the next round.
The goal is for your team to own the data warehouse themselves. That does not mean we disappear — we stay in the background as support and a sounding board, and take on operations or managed services if you would rather we did.
After that we work in short steps: build, measure, adjust. The requirements get clear by testing against reality, not by guessing up front.
PDQ
PDQ combines advanced automation with active metadata management across the whole chain. It is the foundation for data operations that are efficient, transparent and fully automated.
PDQ integrates into your existing infrastructure and holds modelling, quality, versioning, integration, code generation and orchestration together as one unit — instead of a collection of loosely coupled tools. PDQ covers the whole chain on its own — there is no need to buy expensive ETL tools on top.
The flow
Five steps from data as it looks today to numbers the business can trust. PDQ handles the way there — you see the result.
Business systems, files, APIs and databases are connected as they are. No clean-up up front.
All data is brought together in one place with its full history intact, so you can always see how things looked before.
Quality rules run inside the flow and catch errors before they reach a report.
Data is joined up in one shared model that speaks the language of the business.
The same numbers in reports, BI, APIs and AI — one truth instead of several.
Technology-agnostic by design
The value sits in the model, the logic and the metadata — not in the platform underneath. That is why you can change database, storage or where it runs whenever you need to. Your vendors know you could leave, and it shows in the negotiation.
Capabilities
The data model is the heart of the framework — everything else hangs off it. Load logic, historisation and structures are generated from the model, consistent no matter who builds them, and without hand-written ETL that drifts out of sync over time.
Profiling and quality rules run as part of the flow, not as an afterthought. Anomalies are caught where they occur, and the rules stop what needs stopping.
Dependencies, run order, re-runs and alerting are handled centrally. Production-ready from day one instead of months of scaffolding.
The metadata does not just describe the system — it runs it. Lineage, versions and compliance are by-products of execution, not separate projects.
Sensitive fields are classified in the source contract, and the classification travels with the field through the whole chain. Masking and row- and column-level access are generated from the same metadata as the load logic — so the protection holds in every layer, not only in the report at the far end.
We put real work into the context around the data: what each concept means, where it came from, how it relates to everything else, and when it was true. That context is what an AI agent needs to reason about company data instead of guessing — and it lives in the model and the metadata, not in a prompt.
AI-ready data
PDQ does not only structure data. The framework holds meaning, origin, history, quality and relationships together — so people, BI and AI agents all work from the same traceable foundation.
Business concepts and relationships live in the model and in the metadata — not hidden in a prompt or in one person’s head.
Lineage, versions and runs make every answer traceable back to the source it came out of.
Quality rules and profiling run inside the data flow itself and catch anomalies where they arise, not after the fact.
PDQ + AI One shared, understandable data foundation for the next generation of analytics.
Client cases
Month-long reporting cycles replaced by new reports in hours. A new centralised data warehouse on Azure and a full Power BI implementation delivered in six months. Power BI is now used daily across the organisation, and the data warehouse is the foundation for what comes next — including a planned supplier portal.
An on-premise solution replaced by a modern platform on Azure. Maintenance and development are now handled with 0.5 FTE instead of the previous 2–3 FTE. Deeper customer insight, the ability to analyse customer churn, and considerably more effective marketing communication.
A technical review of the iBOS Data platform, layer by layer. Two priority areas — data quality and edge roll-out — became a risk-prioritised roadmap, and the platform is now ready for broad roll-out.
Direction
The framework is built in layers that can be swapped one at a time. That is what makes it possible to move both data and operations onto European infrastructure without rewriting the model, the logic or the metadata.
Data and operations inside the union, with European providers. No part of the chain has to sit under non-European jurisdiction.
In regulated industries the sovereignty question arrives early. A clear answer shortens the buying process instead of extending it.
Because database, storage and infrastructure are independent layers, moving to a sovereign provider is a migration — not a new platform project.
About us
Simplitics was founded in 2016 to stop solving the same problem over and over: data warehouses built by hand, differently each time depending on who built them. What we kept building became a product — PDQ — which makes modelling, historisation and quality part of the platform itself, instead of a habit that lives in each individual engineer. That is what “Simplifying Analytics” means to us: abstracting the complexity away until data is something the business uses, rather than a project every time.
You work directly with the architects and engineers who build — no intermediaries. The team has hands-on experience both building and running modern data solutions.
We see ourselves as an extension of your team — invested in your long-term success, not in the next billable hour.
A finished framework means the work starts at the model rather than the foundations. That is why value arrives in weeks, and at a lower cost than building from scratch.
Careers
We are a small team that would rather be senior than numerous. Here you own a problem the whole way — from model to production — alongside people who have built real data warehouses. No resource pool, no route in through a sales organisation.
Latest news
A role for someone early in their career who wants to learn properly — close to an experienced team, with real responsibility from day one.
Someone who can bridge data engineering and the business’s AI needs: scalable cloud platforms, automated data flows, and data you can trust in dashboards and language models alike.
Our data ingest agent now runs on DuckDB, with streaming processing. In a test with only 2 GB of memory it handled both a single 15 GB file and 2,000 files totalling 32 GB.
Frequently asked
The important things about Simplitics and PDQ, without hunting through the whole site.
Simplitics is a Swedish data company, founded in 2016. We build the PDQ framework and help organisations build, modernise and run their data warehouses — with senior architects on every engagement.
PDQ — Pretty Darn Quick — is our product: a finished framework for modelling, code generation, data quality, orchestration and active metadata across the whole data flow. The framework is built and maintained by us. The model and the business meaning are yours — that is the part that makes every installation unique.
A language model handed raw company data without context will guess. PDQ supplies the context: what a concept means, where a value came from, which version was in force, and whether the quality rules held. Reports, APIs and agents all read the same foundation.
A field is classified in the source contract, not in the report at the far end. Masking and row- and column-level access are then generated from the metadata alongside the load logic, so the same rule holds in the data warehouse as in the BI tool and in the API. That the rules ran, and against which data, is there in the run log.
No. PDQ is technology-agnostic and sits on top of the database, the storage and the infrastructure you already have — Databricks, Snowflake, Fabric, Synapse, SQL Server and more. Your stack decides, not the framework.
No. PDQ covers the whole chain — ingestion, load logic, quality, orchestration and catalogue. You need a database and storage to run on, but no separate ETL, orchestration or catalogue tool. For a smaller company, PDQ on top of Postgres or SQL Server is often all it takes.
A first scoped data flow goes all the way to production in weeks rather than months. Exactly how many weeks depends on the scope and on the state of the sources, but you will see real data in a real report long before the whole warehouse is standing.
The model and the metadata are yours, and they are dialect-independent. The load logic is regenerated against the next database. A switch is therefore a migration with a timeline and a cost — not a new platform project, and not a question of what is technically possible.
Contact
Whether it is a concrete project, a second opinion on an architecture, or plain curiosity about PDQ — write to us and the person who knows the answer replies. We never send a salesperson first.
We read everything that comes in and normally reply within one working day.