Healthcare data that ties out.
Every time.
Hospital systems, patient records, claims, devices — unified into platforms clinicians trust and auditors can follow. Built by engineers who have spent their careers in regulated data.
Nobody went into care delivery to chase records.
Every health organisation runs on systems that were bought separately and never taught to agree. The cost shows up as staff time, denied claims, and reports leadership cannot defend.
Before
- The same patient exists four times, under four identifiers, in four systems.
- Clinical, billing, and pharmacy numbers each tell a different story at month end.
- Reports get rebuilt by hand because nobody trusts the last version.
- Compliance evidence is assembled after the fact, from memory and spreadsheets.
- Every new system means another integration nobody owns.
After
- One governed patient identity, resolved across every source system.
- Clinical, financial, and operational figures reconcile by construction.
- Dashboards run on modelled data with lineage back to the source.
- Audit evidence is a by-product of the pipeline, not a fire drill.
- New systems plug into a documented integration layer.
We do not sell dashboards. We make the numbers agree — then build on top.
Engineering discipline from industries that get audited.
Our background is enterprise data and AI in heavily regulated environments — energy, finance, and compliance-bound reporting. Healthcare is the same problem with higher stakes: many source systems, one truth required, and a regulator who may ask.
/ 01
Reconciliation first
We prove where systems disagree, by how much, and why — with queries you can re-run — before anything is built on top.
/ 02
Identity as infrastructure
Master patient index and record matching treated as a platform capability, not a per-report chore.
/ 03
AI with controls
Document automation with deterministic validation and human review, so AI output is trustworthy enough for regulated workflows.
/ 04
Interoperability by standard
HL7 and FHIR-based integration rather than bespoke point-to-point links that rot the moment a vendor updates.
/ 05
Audit-ready by default
Lineage, access governance, and evidence trails designed in from the first sprint, not retrofitted before an inspection.
/ 06
Principal-level delivery
Reference architectures, costing, deployment scripts, UAT documentation, and direct communication — no handoff to juniors.
Everything a modern care organisation runs on.
Twenty-one capabilities across clinical systems, data and AI, and delivery. Engage one, or a programme.
Hospital & clinical systems
8 capabilitiesHospital management systems
Admissions, transfers, bed and theatre management, departmental workflow, and the reporting layer above it.
EHR / EMR integration
Interoperability across electronic record platforms using HL7 v2 and FHIR, with mapping and conformance testing.
Clinic & practice management
Scheduling, registration, encounter capture, and billing handoff for outpatient and multi-site practices.
Healthcare HR & workforce
Rostering, credentialing, competency tracking, and payroll integration for clinical and non-clinical staff.
Telemedicine platforms
Virtual consultation architecture — scheduling, secure sessions, documentation, and record write-back.
Patient portals & engagement
Self-service access to results, appointments, and messaging, built on governed identity and consent.
Pharmacy & inventory
Dispensing workflow, stock and expiry control, formulary data, and supply reconciliation.
Laboratory systems integration
LIS and diagnostics interfaces, result routing, reference-range handling, and turnaround reporting.
Data, AI & analytics
9 capabilitiesMaster patient index
Probabilistic and deterministic record matching that resolves one patient across every source system.
AI medical document automation
Referrals, forms, and correspondence turned into structured data with validation layers and human review.
Healthcare data platforms
Cloud warehouses and lakehouses modelled for clinical, financial, and operational reporting together.
Clinical & operational analytics
Throughput, capacity, outcomes, and utilisation reporting on data that reconciles to source.
Revenue cycle & claims analytics
Denial analysis, coding gaps, and payer performance surfaced where finance teams can act on them.
Remote monitoring & device data
High-volume device and wearable telemetry ingested, normalised, and made queryable at scale.
Health data quality frameworks
Rule-based checks over critical views, trended on dashboards, with ownership made visible.
AI knowledge platforms
Governed, permission-aware assistants over internal systems and documentation for clinical and admin teams.
Population health & risk
Cohort definition, stratification, and registry data foundations for proactive care programmes.
Consulting & delivery
4 capabilitiesHealth IT consultancy
Architecture review, vendor and build assessment, roadmaps, and costed options for leadership.
Compliance-ready pipelines
Data flows designed around access control, retention, lineage, and evidence for regulated review.
System-to-system migration
Dependency mapping, structured release promotion, and parity validation at every layer of the move.
Multi-cloud infrastructure
Azure, AWS, and GCP architecture, security controls, access governance, and cost management.
Figures describe delivered work across regulated industries, not healthcare-specific engagements. See the FAQ below.
Clinicians get answers. Leadership gets evidence.
For clinical & operational teams
The record is complete.
- One patient view instead of four partial ones.
- Results, history, and documents where the workflow already is.
- Less time reconciling systems, more time on care.
- Reports that match what the floor actually saw.
For executives & compliance
The number holds up.
- Financial, clinical, and operational figures that agree.
- Lineage from every reported number back to its source.
- Audit evidence produced by the pipeline, not by a scramble.
- Costed architecture decisions before the invoice arrives.
Verifiable, not decorative.
The questions worth asking first.
Have you delivered healthcare projects before?
Not yet — and we would rather say so than dress up someone else's work. Our delivered portfolio is enterprise data and AI in other regulated industries: multi-system reconciliation, master data and identity resolution, compliance-bound document automation, and cloud platforms that pass audit. Those are the same engineering problems healthcare has. If a proven healthcare track record is a hard requirement for your procurement, we are not the right fit for that engagement, and we will tell you early.
What transfers from regulated industries to healthcare?
The hard parts. Resolving one entity across five systems that disagree. Automating document-to-system workflows where every output must be validated and traceable. Building warehouses where clinical, financial, and operational numbers reconcile by construction. Designing for the audit before anyone asks. The domain vocabulary changes; the discipline does not.
How do you handle patient data and privacy?
Access governance, least-privilege roles, and lineage are designed in from the first sprint rather than retrofitted. Where engagements involve protected health information, the appropriate agreements and controls are put in place before any data moves, and de-identified or synthetic data is used for development wherever it is workable.
Do you build systems, or advise on them?
Both, and we are explicit about which you are buying. Advisory work produces reference architectures, costed options, and assessments leadership can act on. Delivery work produces working systems with deployment scripts, UAT documentation, and a handover that survives our departure.
Where does AI genuinely help — and where does it not?
It helps where staff currently retype information from one system into another, and where documents must become structured data. It does not replace clinical judgement, and it should never be the only control in a regulated workflow. Every AI pipeline we build has deterministic validation and a human review path.
How do engagements start?
Usually with a short scoping conversation, then a paid discovery: profile the data, prove where systems disagree, and produce a costed plan. That gives you something defensible to decide with — and gives us the evidence to build on.