Your CRM and your billing system don’t return the same number for the same account. Relevant Software maps every source into one dataset your engineers own outright.
We audit every connected system and build a field-level map showing what corresponds to what, and which system wins when two disagree.
Once sources are mapped, our data pipeline development team builds and schedules the pipelines that keep the dataset current, from nightly batch to real-time sync.
Our team runs deduplication and validation against every mapped record, so errors specific to one source never propagate into the dataset your business runs on.
Our architects connect cloud-native applications, SaaS platforms, and APIs into the same mapped layer as your on-premise systems, so cloud sources don’t get a separate, second-class pipeline.
For sources that expose an API but no documented data contract, our engineers build the extraction point that pulls records on a schedule your systems can sustain, using the same patterns our API integration team applies more broadly.
We connect Salesforce, HubSpot, and other CRM platforms into your mapped dataset, so the same customer record reads the same way in your CRM, your billing system, and your reports.
Every system, field, and format in scope gets documented, along with what manual reconciliation is currently costing your team each week. You get a scoped list.
Field-by-field mapping and the rules for resolving conflicts between systems get built and documented. The logic stays yours.
Our team builds pipelines, then runs them through AI-powered testing at your actual data volumes, so edge cases surface before launch.
Validation and deduplication rules run against production data, and your team reviews the output before anything goes live. Nothing ships without sign-off.
The mapped dataset goes live with monitoring for schema drift and failed syncs, plus runbooks your team can run without us. Support stays available.
ISO 27001:2022 certified, GDPR covered, HIPAA-ready. Every mapped field carries an audit trail, so a compliance review has an answer instead of a guess.
96% employee retention. The team that builds your mapping logic is still there when you need to add a source six months later.
98% client satisfaction. Most of our data engagements come from clients returning for the next system, not a one-off project.
Our engineers use AI-assisted development to cut mapping and pipeline delivery time by up to 50% and reduce bugs by up to 20%, with every mapping rule still passing senior code review before it touches production data.
14 days to roadmap. You get a scoped plan and cost estimate before committing to the full build.
Data integration doesn’t replace data engineering, it comes before it. Data integration resolves conflicts inside the data itself: which system holds the correct value for a shared record, where duplicate entries need merging, how mismatched identifiers get reconciled into one identity.
Data engineering then builds the pipelines, warehouse, and analytics layer on top of that resolved dataset. A separate need exists when systems require a live, operational connection rather than data reconciliation, for example a claims system triggering an update in a billing platform in real time. That’s a software integration problem, and our team builds exactly that kind of connection.
Describe the symptom you’re seeing and we’ll scope the right engagement, under one team either way.
Legacy systems without a REST API can usually still be extracted from, most commonly through a flat-file export, an ODBC connection, or a JDBC connection. A documented API is only one possible access path: older ERPs and mainframe systems routinely support at least one of the other three, even when no one has checked recently.
If the legacy system itself needs to move to the cloud, rather than just feed a mapped dataset while staying where it is, that’s a cloud migration project with a different scope than the extraction work covered here. Either way, contact us with what the system currently runs and our engineers will map the extraction path from there.
Timeline and cost both come down to two variables: how many source systems are in scope, and how many of them already have documented schemas. Undocumented sources are what add time, because mapping has to be reverse-engineered from the data itself.
If you already know that count, contact us with it and we’ll turn it into a scoped timeline. If you’re not sure how many sources you’re actually working with or which ones fall into which category, that’s something our engineers can map out with you on the same call.
Your team does, from day one. The mapping documentation, the pipeline code, and the conflict-resolution rules all transfer as build artifacts. If the engagement stops after the mapping and pipeline are built, they keep running; nothing is licensed back to us.
No, the engagement clears the path for them. If your analytics team is already reporting cleanly, integration just gives them a faster, more reliable dataset to work from. If they’re spending real hours each week reconciling source data before they can even open a dashboard, that’s usually the sign the integration layer is missing, and it’s the first thing we’d fix. We also build the data analytics layer itself if your team needs that built out alongside the integration work.
Every mapped field carries an audit trail back to its source: which record it came from, when it moved, and which rule resolved it if two sources disagreed. That’s what most compliance reviews ask for directly. If governance decisions like data ownership or retention policy haven’t been made across your systems yet, we scope those as part of the same engagement, or run them upfront through data strategy consulting if you’d rather settle governance before integration work starts.
We build the data layer that resolves it, once.
Tech label
Tech label
Healthcare
