Prove DataFuseAI on One Real Pipeline — Free for 30 Days
Bring 2–3 data sources and one recurring workflow. We'll help you connect, transform, validate, schedule, and monitor it so you can evaluate DataFuseAI using your own data before making a larger commitment.
No POC feeNo obligation to continueCloud, private-hosted, or on-premise
Why It's Free
We'd rather show you a working pipeline on your own data than ask you to take a demo on faith. That's the whole reason the POC costs you nothing — and it's also why we're selective about which workflows we take on. See who this is a good fit for below.
We assess your problem first
A short scoping conversation before anything is built — sources, destination, current process, and what a good outcome looks like — so the POC targets a workflow that actually matters to your team, not a generic demo scenario.
We connect and build it — in DataFuseAI's own canvas
A real pipeline, built on your real sources, in the same product you'd run in production — connectors, transformations, engines, and scheduling included, not a slide deck.
There's no POC fee
DataFuseAI platform access and our implementation support during the POC are provided at no POC fee, with no obligation to continue afterward. If the evaluation runs on customer-owned or third-party infrastructure — Databricks, a cloud provider, or your own Spark cluster — any external infrastructure cost stays with you and is agreed during scoping.
Workflows We've Built and Tested in DataFuseAI
Real workflows built and executed in DataFuseAI — not marketing mockups.
Healthcare Sample Workflow
3 PostgreSQL sources · 1.03M rows → 2 tables · Databricks engine · 2 min 19 s
Retail Sample Workflow
5 MySQL sources → 3 tables (MySQL + PostgreSQL) · Databricks engine · 14 min 21 s
60M-Row Benchmark Workflow (TPC-H SF10)
8 PostgreSQL + MySQL sources · 78.6M rows → 6 tables · DataFuseAI engine · 6 h 34 min
Benchmark Detail — TPC-H Suite on Databricks (2-core / 16 GB)
- Source dataset
- TPC-H Scale Factor 10
- Row count
- 60,000,000 source rows
- Processing engine
- Databricks cluster — 2-core, 16 GB RAM
- Transformations
- Joins, aggregations, derived columns, multi-sink routing — 0 lines of code
- Runtime
- 15.5 min fastest pipeline · 35.3 min average across 8 pipeline types
- Output
- 6 analytics-ready tables · 3,535 total rows
Source: DataFuseAI benchmark (TPC-H SF10), 2026 — 31 runs across 8 pipeline types and 4 scale factors. These figures come from that Databricks-based suite; the pipeline pictured above is a separate, heavier run (78.6M rows, 8 sources, 6 outputs) executed on DataFuseAI's native engine. Full figures on data pipeline automation and analytics-ready data. Your POC runs at your own volume — this is the scale the platform has been tested at, not a projection of your workflow.
From One Data Problem to a Measurable Result
This is the engagement itself — what happens week by week, not just the product mechanics.
Bring Your Workflow
Select one recurring data workflow that's expensive, manual, difficult to maintain, or hard to monitor.
We Build It in DataFuseAI
We configure the workflow using Connect → Transform → Validate → Schedule → Monitor, using the same DataFuseAI product capabilities intended for production deployment, not a scripted demo.
We Run It and Measure
We operate the workflow for real and compare the result against your existing process, against the criteria we agreed on up front.
You Decide
Review the results and decide: stop after the evaluation, move the workflow to production, or expand DataFuseAI to more pipelines.
Week 1 — Assess & Connect
Week 2 — Build & Transform
Week 3 — Run & Validate
Week 4 — Measure & Decide
Your sources
DataFuseAI
- Connect
- Transform
- Validate
- Schedule
- Monitor
Output
Reporting datasetWhere it lands
Power BIWhat Your Team Brings
Keep the scope focused. The goal isn't to migrate your entire data estate in 30 days — it's to prove value on one real workflow.
- One real business workflow
- 2–3 relevant data sources
- Sample or approved access to the required data
- One technical point of contact
- Information about the current process
- A clear business or operational objective
We deliberately keep this list short. Most POCs stall not because the technology doesn't work, but because the scope was too broad to evaluate in 30 days — one workflow, evaluated properly, tells you more than five workflows evaluated poorly.
- Sources
- PostgreSQL + REST API + Excel
- Current problem
- Operations staff manually combine data every week before reporting.
- POC objective
- Automate ingestion, transformation, validation, scheduling, and reporting preparation.
What We Help You Evaluate
Connect → Transform → Validate → Schedule → Monitor → Deliver. Our team runs this operating flow on your workflow — you tell us the source, we handle the rest. Each step below is a real screen from the platform, not a mockup — click any image to look closer.
Connect
We configure validated connections to your sources — databases, APIs, files, or SaaS platforms.
Transform
We clean, join, filter, and shape the data on DataFuseAI's canvas for your exact workflow.
Validate
We profile the output and validate it before it reaches anything downstream.
Schedule
We set the pipeline to run automatically, on the cadence your workflow needs.
Monitor
We give you visibility into every run — succeeded, failed, or in progress.
Deliver
We prepare and deliver a trusted dataset for your warehouse, Power BI, Tableau, analytics, or AI applications.
This isn't the whole platform — see the full data connectors, processing engines, and architecture. The goal of the POC isn't to migrate your whole data platform — it's to prove value on one production-relevant workflow first.
Already have a workflow in mind?
Start Your POC Application →What We'll Measure Together
We agree on success criteria up front, so the result is something you can evaluate objectively — not just a good demo.
Setup Effort
How much work is required to configure the workflow.
Manual Work Removed
Which recurring manual steps get automated.
Pipeline Maintenance
How much custom code or ops work remains.
Failure Visibility
Whether failed or incomplete runs surface sooner.
Reconciliation Effort
Whether comparisons between systems get easier.
Reporting Turnaround
How fast trusted data reaches reporting and analytics.
Operational Handoff
What it takes for your team to run this day to day.
Deployment & Security Fit
How well the setup matches your constraints.
We won't promise a fixed percentage improvement before we've seen your workflow — these eight criteria are what we'll actually measure together, against your current process, during the 30 days.
What Could a 30-Day POC Look Like?
A few starting points from workflows we see most often. Yours doesn't need to match one exactly — it's the shape that matters: a few sources, one workflow, one destination.
Healthcare
Sources
EHR / HIS + Billing + Database
Workflow
Operational or procurement reporting
Output
Validated dataset → Power BI / analytics
Manufacturing
Sources
ERP + MES + CSV / Database
Workflow
Production, inventory, or supplier reporting
Output
Unified operational dataset
Finance
Sources
ERP + Transaction Database + API
Workflow
Data reconciliation
Output
Validated reconciliation / reporting dataset
Retail
Sources
POS + Ecommerce + Inventory
Workflow
Sales and inventory consolidation
Output
Unified reporting dataset
Evaluate DataFuseAI Where Your Data Needs to Run
We'll structure the POC around the deployment model that fits your environment.
Managed Cloud
Run the POC in a managed environment for the most straightforward evaluation.
Private Hosted
Run the platform in a controlled private environment where that's required.
On-Premise
Evaluate closer to the systems and data that must stay inside your infrastructure.
Not sure which fits? See deployment options or tell us in the application below.
Your POC Stays Scoped to the Workflow
We only request the access required for the agreed workflow. Deployment, networking, credentials, and data-access requirements are reviewed before the POC begins.
Is This a Good Fit for Your Team?
We don't take every applicant — a POC only proves something if the workflow behind it is real.
Good Fit
- ✓A real, recurring data workflow
- ✓2–3 identifiable data sources
- ✓A named destination or outcome (warehouse, BI, analytics, AI)
- ✓Someone who can grant access to the data
- ✓Manual, error-prone, or hard-to-maintain data work today
Probably Not the Right Fit
- –Pure research with no real workflow behind it
- –An expectation of a full enterprise migration in 30 days
- –Sub-second streaming as the primary requirement
- –A one-time file conversion with no recurring need
- –No one internally who can own the go/no-go decision afterward
What You Have at the End of 30 Days
Every POC follows a defined scope and ends with concrete deliverables.
Working Workflow
A scoped DataFuseAI pipeline built around your agreed use case — not a demo environment.
Configured Sources
Validated connection profiles for the 2–3 sources you brought, ready to reuse.
Automated Execution
Scheduling and execution configured where applicable, running on your cadence.
Validation & Monitoring
Run history, data profiling, and operational visibility, demonstrated on your data.
POC Results Review
A comparison against the success criteria we agreed on before starting.
Production Recommendation
Concrete next steps for production deployment or broader adoption — or a clear no.
The POC Ends With a Decision, Not an Obligation
Every path below is a decision you make after seeing results — not a default.
You leave the POC with a workflow review, the pipeline configuration itself, a results summary against the criteria we agreed on, identified improvements, and a recommended production architecture — whether or not you continue.
Stop
No cost, no further commitment. The evaluation is complete.
Go to Production
Move the validated workflow into a production DataFuseAI deployment.
Expand
Bring more sources, workflows, or teams onto DataFuseAI under an agreement.
Bring Us One Pipeline
Answer in your own words where it's easier — this takes about 3 minutes.
Your application is reviewed by the DataFuseAI team. We proceed only when the workflow, access requirements, and success criteria are suitable for a focused 30-day evaluation.
Frequently Asked Questions
DataFuseAI platform access and our agreed implementation support during the POC are provided at no POC fee, with no obligation to continue afterward. If the evaluation uses customer-owned or third-party infrastructure — your own Databricks, cloud, or Spark environment — any external infrastructure cost remains with you and is agreed during scoping.
2–3 real data sources, a clear target or destination for the output, and a short description of the workflow or problem you want solved.
We recommend 2–3 meaningful data sources so the POC stays focused enough to prove one workflow properly in 30 days.
Yes, where it fits your environment — we'll confirm deployment requirements during the initial assessment.
No — the POC evaluates one workflow properly. It isn't a full migration of your data estate.
Access is scoped to the sources you approve for the POC, connected the same validated way DataFuseAI connects any source in production.
You review the result against what we agreed to measure and decide: stop, go to production, or expand. There's no default obligation either way.
A demo shows the product. This builds and runs your actual workflow on your actual data.
Yes. DataFuseAI supports scalable compute through engines like Databricks, Apache Livy, and its native engine — you choose where computation runs, so the POC can execute against your existing compute rather than a separate sandbox.
Read access (or a representative sample) for the 2–3 sources you bring, and a technical point of contact who can approve connection credentials. We don't need broader access to your systems than the workflow itself requires.
Typically the assessment and first connections happen within the first few days; the rest of the 30 days is building, running, and measuring.
Recurring batch workflows involving data integration, transformation, validation, reconciliation, reporting preparation, scheduling, or monitoring — not one-time file conversions or sub-second streaming requirements.
Prove DataFuseAI on One Real Pipeline
Bring 2–3 data sources and one recurring workflow. We'll help you build, run, and evaluate it before you decide whether to move to production.
Discuss Your Workflow