Services
CRM Engineering & Data Automation
Your CRM, continuously fed with clean, structured, actionable data. We design and operate the pipelines — collection, enrichment, deduplication, synchronisation — so your teams work from records they can trust.
What we build
We apply data engineering to revenue operations: pipelines, integrations, and quality controls around your CRM.
- 01
Multi-source data collection
We connect product databases, transactional systems, files, and third-party APIs into a single ingestion layer. Each source is versioned, monitored, and documented.
- 02
Structuring and modelling
Raw inputs are mapped to an explicit data model: defined entities, typed fields, and normalised values. Your CRM stops being a free-text dumping ground.
- 03
Enrichment pipelines
We augment records with firmographic, technographic, and internal usage data through controlled API integrations. Every enriched field carries its source and timestamp.
- 04
Deduplication and matching
Record-linkage logic identifies duplicates across sources, even with inconsistent spellings and formats. Merge rules are explicit, reviewable, and reversible.
- 05
API workflow automation
Recurring data operations — record creation, field updates, ownership routing, list maintenance — run as automated API workflows with retries, rate-limit handling, and error logging.
- 06
Bidirectional synchronisation
We keep your CRM and operational systems consistent in both directions, with defined conflict-resolution rules. A change made in one system propagates predictably to the others.
- 07
Data quality monitoring
Automated checks measure completeness, validity, freshness, and duplicate rates against agreed thresholds. Regressions trigger alerts, not quarterly surprises.
How we work
Every engagement follows the same four steps, from audit to steady-state operation.
01
CRM and source audit
We audit your current CRM: field usage, duplicate rates, source systems, and existing integrations. The output is a factual assessment of data quality and pipeline gaps.
02
Target model and thresholds
We define the target data model, the pipeline architecture, and measurable quality thresholds. If you want ongoing operation, we specify it contractually in an SLA.
03
Build and incremental cutover
We build and test the pipelines, run an initial remediation pass on existing records, and cut over incrementally. Your team keeps working in the CRM throughout.
04
Monitored operation
Pipelines run under monitoring, with quality metrics reported on a fixed cadence. We adjust mappings and rules as your sources and processes evolve.
Stack
We work with established platforms and tooling; the architecture is designed around your systems, not the other way round.
CRM platforms
- HubSpot
- Salesforce
- Microsoft Dynamics 365
- Pipedrive
Pipelines & integration
- Python
- Apache Airflow
- dbt
- Apache Kafka
- PostgreSQL
- REST & GraphQL APIs
Data quality & observability
- Great Expectations
- Splink
- dbt tests
- Grafana
What you get
The result is a CRM your teams can rely on as an operational system, not a database they distrust.
One reliable record per entity
Accounts and contacts exist once, with complete, typed fields. Ownership, hierarchy, and history are consistent across systems.
Less manual data entry
Data that can be collected, derived, or synchronised automatically no longer depends on someone remembering to type it in.
Reporting you can defend
When fields are populated by monitored pipelines rather than ad-hoc habits, pipeline reviews and forecasts rest on numbers that hold up to scrutiny.
Systems that stay consistent
Bidirectional sync with explicit conflict rules keeps CRM, billing, and product data aligned. Divergence is detected and corrected automatically.
Frequently asked questions
01Which CRM platforms do you support?
Primarily HubSpot and Salesforce, where we go deepest on APIs and data models. We also integrate Microsoft Dynamics 365, Pipedrive, and other systems with a usable API — the engineering approach is the same.
02Our CRM is already full of bad data. Can you fix that first?
Yes. Every engagement starts with an audit and, where needed, a remediation pass: deduplication, normalisation, and backfilling of existing records. The pipelines then keep the data clean going forward.
03How do you handle personal data under GDPR?
Pipelines that process personal data run under a data processing agreement, with EU data residency, encryption in transit and at rest, and field-level minimisation. We only collect and store what the agreed data model requires.
04Is this a one-off project or an ongoing service?
Both models exist. A fixed-scope project delivers the pipelines and documentation to your team; under Managed operations (SLA), we operate them long term with monitoring, quality thresholds, and contractual response times.
Let’s talk about your technical operations
A 30-minute introductory call with a senior engineer — no sales script, no obligation. We listen, we ask questions, and we tell you honestly whether we can help.