By industry
AI & Automation for Healthcare
Automate the administration, not the clinical judgement
We design around
- DPDP Act 2023
- ABDM and health data standards
- HIPAA (US-facing work)
- NABH documentation requirements
- ISO 27001
- 60%
- Pre-authorisation cycle time reduction
- 3x
- Coder throughput with assistance
- 0
- Clinical decisions made autonomously
A deliberate boundary, not a limitation
What we hear
The problems that bring people to us
If several of these describe your week, there is almost certainly something worth automating.
- Claims and pre-authorisation cycles measured in weeks
- Medical coding backlogs delaying revenue recognition
- Discharge summaries and records transcribed by hand
- Patient communication and follow-up handled inconsistently
- Patient data spread across systems that do not talk
What we build
Where automation pays off in healthcare
Claims and pre-authorisation
Extract from submitted documents, validate against policy terms, assemble the case file and flag what needs clinical review. Compresses cycle time without removing a clinician from any medical determination.
Coding assistance
Suggest ICD and procedure codes from the clinical record with the supporting text cited, for a coder to confirm. Assistive by design — the coder stays accountable and works considerably faster.
Administrative documentation
Draft discharge summaries and referral letters from the structured record, for clinician review and sign-off. Returns clinical time without touching clinical content.
Patient communication
Appointment reminders, preparation instructions, follow-up scheduling and results-ready notifications, automated with escalation to a human for anything clinical.
Records integration
Bring HIS, LIS, PACS and billing systems into one modelled layer so reporting and automation stop depending on manual exports.
The line we do not cross
Clinical decisions stay with clinicians. We do not build diagnostic tools, treatment recommendations or triage systems that act without a clinician — those are regulated medical devices requiring clinical validation and regulatory approval, and that is a different business.
What we automate is the administrative machinery around care: claims, coding, documentation, scheduling, communication and reporting. That is where most of the recoverable cost sits, and it does not require anyone to accept an algorithm's medical opinion.
Why claims and pre-authorisation come first
Both are document-heavy, rule-bound, and currently slow in a way that costs money at both ends — delayed revenue for providers, poor experience for patients. Both have a verifiable correct answer against policy terms, so accuracy can be measured.
And critically, neither requires a clinical judgement to be automated. The clinical review step stays; what disappears is the fortnight of document handling around it.
DPDP and health data
Health data carries heightened obligations, and the erasure right is the one with architectural consequences. If patient information sits inside embeddings in a retrieval index with no lineage back to source records, you cannot honour a deletion request. Chunk-level lineage is the fix and it has to go in at design time — see AI Governance for how we document this.
FAQ
Questions we get asked
Talk to someone who has worked in healthcare
Bring a process that annoys you. In 30 minutes we will tell you whether AI helps, what it would cost, and where it would fail — even if the answer is don't bother.
Or email [email protected] · we reply within 1 business day