AI and software for hospitals and diagnostics chains, across claims, records, coding and day-to-day operations.
Nothing here diagnoses or treats. Every draft goes to the person who signs it, with the source in front of them.
In brief
EigenSpark builds AI systems and the software around them for hospitals and diagnostics chains. We work on the money first: denied claims, pre-authorisation, coding and billing, where the return is quickest and easiest to measure. Then documentation, discharge summaries and records. Then the day-to-day work of running a hospital, from rosters and beds to purchasing and stores. Clinical judgement stays with your clinicians.
What we cover
Patient data, records and telemedicine, handled the way the regulator expects.
The starting position
Claims denied and never reworked, records finished late, and tools built for American billing.
An incomplete pack, a query, a waiting patient, an ageing claim. Denials are the least automated part of the revenue cycle almost everywhere.
Notes written at the end of a shift and summaries assembled from memory. Gaps have to be caught while the person who can fix them is still there.
Most healthcare AI assumes a US revenue cycle. Package rates, empanelment rules and TPA behaviour do not map onto it.
Personal health data carries the heaviest obligations under the DPDP framework. Where processing happens is an architecture decision, made first.
How an engagement runs
Start with your own denied claims, ship one workflow, then reuse what sits underneath it.
Two to three weeks with your medical records, billing and insurance desks, working on real denied claims, real pre-authorisation packs and real discharge summaries, to agree what a correct output looks like on your documents and your tariffs.
A single workflow taken to a working interface: capture, draft, review, submit, audit trail. Denials or pre-authorisation is the usual first choice, because the return is measurable within a billing cycle.
Ingestion, extraction, the rule engine and the tariff library are shared. Documentation, records completeness and operations use cases reuse them, and your team edits the rules without waiting for us.
Use cases
Revenue first, then documentation, then operations, then patients and the back office.
Rejections read for the reason behind them, grouped by payer, package and department, and the appeal drafted from the case file with the supporting pages attached.
Continuous Compliance Monitor →The pack built from the record: indication, investigations, the package sought and the documents each insurer wants, with anything missing flagged before it goes.
Document Intelligence Engine →Codes and package selections suggested from the clinical record against your own tariff, with the supporting text shown, for your coder to accept or change.
Generative AI →A draft assembled from the notes, orders, investigations and medication record, in your format, ready for the treating doctor to correct and sign.
Generative AI →Spoken notes turned into the fields your hospital system expects, with gaps in the template shown while the clinician is still with the patient.
Generative AI →Every record checked against the documentation standard as it is created, with omissions routed to the person who can still fix them.
Continuous Compliance Monitor →Admission, discharge, booking and seasonal patterns read into a forecast by unit and day, so theatre lists, beds and OPD slots are planned against real load.
AI & Data Strategy →Real consumption, case mix and lead times read into a forecast per item, with expiry exposure and slow-moving stock surfaced for the pharmacist.
AI & Data Strategy →Rosters built against forecast load, leave, skill mix and your working-hours rules, with the trade-off shown whenever the roster is tight.
Data Engineering & Platforms →An assistant that answers routine questions on timings, preparation, empanelment and billing from your approved material, and hands anything clinical to a person.
Agentic AI →Complaints, feedback and survey text read for the recurring cause behind them, grouped by department, ward and shift, so the process gets fixed.
AI & Data Strategy →Insurer, TPA and scheme agreements read into a record of package rates, exclusions, documents and payment terms, with every change from the last version tagged.
Contract Risk Analyser →The engineering half
Four layers that decide whether a hospital can run it, trust it and audit it.
The hospital system, the lab, the pharmacy and the billing desk each hold part of a patient encounter. Most healthcare use cases are a data engineering answer first.
The record stays where it is. We connect to it, and to everything a claim has to pass through on its way out.
Built for a billing desk, a medical records department and a front office, with a review step before anything is submitted on the hospital's behalf.
Where processing happens, what is de-identified and who can see what are decided before any code is written, and recorded so an audit can follow them.
We also run the training that goes with it: SQL and data engineering, cloud, enterprise systems and cybersecurity, alongside the AI tracks. A team that cannot query its own data cannot specify a model either.
What we offer healthcare teams
Most hospitals start with one revenue workflow, and use three of the four.
Where most hospital engagements start.
The build, and the engineering under it.
AI tracks, and the foundations under them.
The teams that own this work.
Working in a specific function? See how we help Finance & Accounting teams.
Security and controls
Clinical decisions stay clinical, identifiers are separated, and your data stays inside your boundary.
We build administrative, documentation, revenue and operations systems. Diagnosis, treatment and triage are outside what we take on, and every draft a system here produces goes to a clinician to correct and sign.
Personal identifiers are held apart from the data models work on, de-identified wherever the use case allows it, and re-joined only where a named record is genuinely required.
In your own tenancy or on your hardware, with open-weight models where health data cannot leave. Where a hosted model is used at all, what is sent to it is agreed and recorded before build.
Consent artefacts, access, purpose and retention are recorded as a matter of design, so an impact assessment or an audit is answered from the record by your own team.
FAQs
No, and we will decline it. Systems that suggest a diagnosis, a treatment or a triage category carry patient risk and sit in a regulated medical device space with its own evidence and approval requirements. That is a different discipline from ours. Everything on this page is administrative, documentation, revenue or operations work, and every clinical judgement stays with the treating doctor.
It matters more than anything else on the shortlist. The imported tooling is designed around a US revenue cycle and its code sets and payer behaviour. It has nothing to say about package rates, empanelment conditions, state scheme documentation or how a particular TPA actually queries a pack. We scope on your own tariffs, your own empanelment terms and your own denied claims, which is why the first phase uses your own documents.
In your tenancy or on your own hardware. Identifiers are separated from the working data, de-identified wherever the use case allows, and re-joined only where a named record is genuinely needed. Where a hosted model is used at all, what is sent to it is agreed in writing before build, and for most of the work on this page nothing needs to leave at all.
Denials, in almost every hospital we have looked at. It is the least automated part of the revenue cycle, the reason for each rejection is usually recoverable from the file, and the return shows up inside a billing cycle. Pre-authorisation is the close second, because the same rules and the same documents drive both.
No. The HIS stays the system of record and so does the lab and the pharmacy system. We connect to them, reconcile what they hold, and build the workflows that currently happen in spreadsheets and email between them.
Linking your facility and practitioners to the registries, handling ABHA identity at registration, capturing consent as a real artefact, and exchanging records on that consent. We build it into the front office and records workflow so it is part of the job itself, with nothing for anyone to remember separately.
No, and the parts that are not are usually what makes the AI work. Integration with the HIS and the payer portals, the tariff and rule library, access control and workflow are ordinary engineering, and they are most of the effort. The same is true of the training: alongside the AI tracks we run SQL, data engineering, cloud, enterprise systems and cybersecurity.
Most of it. Registration and eligibility, empanelment terms, denials, records completeness, consumables forecasting and the patient assistant all carry over. The documentation use cases change shape: reporting turnaround and report completeness matter more than discharge summaries.
Related
The products this ships as, the services behind them, and the teams that own it.
Denials, records completeness and accreditation evidence.
→ ProductPre-authorisation packs and incoming documents.
→ ServiceDischarge summaries, dictation and coding support.
→ ServiceThe HIS, ABDM, and the insurer and scheme portals.
→ FunctionThe function that owns billing, claims and collections.
→ PillarThe engineering side, for systems that run a hospital.
→One month of rejections, with the packs that produced them. You get back a written assessment within 48 hours: why they were denied, how many were recoverable from the file, and what a first build would take.
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