Data & AI services for hospitals — advise, build, transform, run.
DaasLabs teams set your data and AI strategy, build the hospital datahub, transform patient access, care, capacity and the revenue cycle, and run what we build. Our Data Fabric Framework, Healthcare-in-a-Box accelerators and supervised AI agents are how we deliver it faster — with clinicians always deciding.
Hospital data and AI programmes stall because every one starts from zero.
Clinical, operational and financial systems don't agree, and AI that cannot show its working never earns clinicians' trust.
Qualitative themes from our healthcare work — no industry statistics claimed.
1 · No connected patient record
EHR, lab, radiology, pharmacy and billing use different identifiers — lineage and quality are unknown.
2 · Integrators rebuild from scratch
Each project re-invents interfaces, data models and controls, so timelines and fees grow.
3 · Point tools don't change the workflow
A scheduling or coding tool solves one task and leaves the ward, the bed board and the claim untouched.
4 · AI pilots never reach the ward
Assistants without grounding, role policy and audit fail clinical-safety review and stay on the shelf.
Hospitals are moving from copilots to grounded, supervised agents.
Agents now prepare whole pieces of work — a discharge summary, a claim reply, a bed proposal — and a clinician or named owner confirms.
So what for a hospital: once agents prepare the work, grounding, role policy, confirmation and audit become the deciding capabilities — and clinical decisions stay with clinicians.
Directions we build for in our healthcare work — not market statistics.
A services firm that arrives with its own IP — so hospitals pay for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — for patient access, clinical care, capacity and the revenue cycle.
Data Fabric Framework
The governed foundation: the 4C method, 167+ pre-built connectors, metadata, lineage, data quality and security.
Healthcare-in-a-Box
Hospital 360, Patient & Doctor 360, Revenue 360 and a grounded assistant on one datahub — configurable starting points.
Supervised digital workforce
Agents that prepare the routine work, with clinicians and named owners confirming every action and every step audited.
Nine service lines cover the lifecycle, organised the way hospitals buy them.
Select a stage on the wheel, or start from your role.
Set direction and the rules the data and AI must meet.
Data & AI StrategyMaturity assessment, AI operating model, clinical-safety governance Data Governance, Privacy & Clinical DataPrivacy, consent, masking, master patient index, AI governanceEngineer the datahub and the AI that runs on it.
Data Engineering & Platform ModernisationHospital datahub; EHR, LIS, RIS, pharmacy & billing integration AI & Agentic EngineeringGrounded assistants, validated actions, GenAI draftingChange how a hospital function works, end to end.
Clinical Care & QualityPatient 360, early warning, work-ups, discharge summaries Patient Access & ExperienceScheduling, referrals, eligibility, patient service Operations & CapacityBeds, discharge planning, theatres, supply chain Revenue Cycle & FinanceClaims, denials, TPA desk, leakage, remittancesKeep it healthy and improving after go-live.
Managed ServicesDataOps, MLOps & AgentOps under SLAsEvery service line runs on the same framework, accelerators and digital workforce. All services in detail →
A repeatable 4C method turns raw hospital data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect167+ pre-built connectors to EHR / HIS, lab and radiology, pharmacy, billing and payer claims, scheduling and documents; batch and near-real-time.
- Step 2 · Week 2–4CurateStandardise, cleanse and enrich: MDM, de-duplication and data quality rules.
- Step 3 · Week 3–5ContextualizeMetadata catalog, critical data elements, multi-hop lineage, ownership and policy.
- Step 4 · Week 4–6ConsumeData products, APIs, BI, NLQ GenAI studio and the agents behind each accelerator.
Twelve capability layers give every hospital one blueprint — and one way to measure progress.
The same layers we build and score in the maturity assessment. Hover a layer to see what it does.
Runs in your cloud tenancy or on-prem. Code, ontology and agents are handed over — you own what we build. Explore the blueprint →
MIT CISR found enterprises at stages 3–4 perform well above their industry average financially, while those at stages 1–2 perform below it. Source
Healthcare-in-a-Box and cross-industry accelerators mean no service line starts from a blank page.
Pre-built, configurable starting points on the framework. Hover or tap a tile.
Clinical Care & Quality CMO
Data Governance CDO
Operations & Capacity COO
Revenue Cycle & Finance CFO
Four connected views on one datahub, ready to configure for your hospital.
Start-my-day brief, admissions queue with bed allocation, capacity against demand per department, discharge planning board and a NEWS2-style early warning board.
Cross-specialty record, work-up assistant (70 rules, 30 conditions), pharmacy-aware prescribing with interaction checks, critical-results inbox and discharge-summary drafts.
Claims workqueue (resubmit, appeal, follow up), TPA desk with replies drafted from the treating doctor's record, insurer scorecard, leakage recovery and patient balances.
Answers only from the database with the SQL shown, a number verifier, role-based masking and an audit of every question, draft and action.
Capabilities of the Healthcare-in-a-Box demo, which runs on synthetic data — not client results.
Question
“Which cardiology patients discharged last month have unpaid bills above 50,000?”
Answer, with its working
A list of patients and balances drawn from the datahub, with View data source showing the SQL and the row count.
Signed in as finance? Diagnosis fields are masked and diagnosis questions are refused by role policy.
Healthcare-in-a-Box · grounded assistant. Every answer can be checked; every action waits for a person. Open the demo ↗
Agent squads by healthcare domain — clinicians and named owners decide.
Agents prepare work across access, clinical care, diagnostics, population health, beds and flow, supply, the revenue cycle and governance. Anything clinical stays at Suggest; nothing commits without a person.
Agent designs from our AI & Agentic Engineering practice; several mirror Healthcare-in-a-Box modules.
Counts of agent designs on the healthcare agents page — not live or client figures.
An agent does the legwork on every case; a person makes the call and sends it.
Replay: an insurer asks the hospital to justify a length of stay. The TPA Query Responder (Revenue 360) works it.
- 1Source systemsAn insurer's TPA asks the hospital to support the length of stay on an inpatient claim.
- 2Data fabricThe encounter, observations, procedures, results, medication and bill are already joined on the hospital datahub.
- 3Metadata & ontologyThe query resolves to one patient, one encounter and one treating doctor, with lineage back to each source system.
- 4ContextOnly that encounter's record is assembled — diagnosis, first observations, procedures with dates and operator, abnormal labs, imaging impressions.
- 5AgentDrafts the formal reply strictly from that record, citing what supports each day of the stay.
- 6PolicyRole, encounter scope and claim state are checked by the server: the agent may draft, never send.
- 7Human gateThe finance controller reviews the letter, edits it if needed and sends it.
- 8ActionThe response is stored on the claim and the claim's status and history update.
- 9AuditThe draft, the edits and the send are audited by user and role; edits feed the evaluation of the agent.
Autonomy is set per agent and kept conservative in clinical work — inside hard guardrails.
Grounded on the record
Agents draft only from the hospital's data — the encounter, the panel or the claim in scope.
Role & record checks
The server re-validates every proposal against the user's role and the record's state; finance never sees diagnosis.
Clinician decides
Orders, work-ups and discharge summaries stay at Suggest; the treating doctor confirms and signs.
Everything audited
Every question, AI draft and action is logged by user and role; overrides feed evaluation.
Kill switch
One control halts every agent at once.
Delivered for a healthcare group — and a demo you can click through today.
Master data management delivered for a GCC & India healthcare group, plus the Healthcare-in-a-Box storyline on synthetic data.
From a red ward to the patient behind it
Land on Hospital 360: wards under pressure, the emergency queue and discharge bottlenecks. Open the deteriorating patient: risk reasons, a warfarin + aspirin interaction alert and a grounded AI summary.
From a denied claim to an answer you can check
Revenue 360 shows the denied claim and a leakage flag to mark recovered. The assistant answers a finance question with the SQL and row count; signed in as finance, diagnosis is masked and refused by role policy.
Rule-based where it matters
Work-up recommendations (70 rules, 30 conditions) and early-warning scores are rules, not a language model — nothing to hallucinate.
AI auto-commentary at a Tier-1 bank
Balance sheet and P&L commentary drafted by AI and signed off by people.
One pharma item master for a healthcare group
MDM across 30+ hospitals, 125+ clinics and 250+ pharmacies, integrated with Oracle ERP and the HIS; multi-level approvals and bulk uploads.
What could a supervised agent squad free up in your hospital?
Move the sliders to your own volumes. Agents work the cases; people review only the exceptions.
Estimate only, not a quote or a DaasLabs result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − STP share) × review minutes ÷ 60. Hours saved = manual − supervised. FTE = hours saved ÷ 150. Cost saved = hours saved × cost per hour. Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Faster than services alone, a better fit than software alone.
How the DaasLabs model compares with the usual ways hospitals deliver data, AI and agentic automation.
| Criterion | Big-4 / SIservices only | Point productssoftware only | In-house build | DaasLabsservices + framework + accelerators |
|---|---|---|---|---|
| Time to production value | 6–12 months | 3–6 months + integration | 12–18 months | 30–45 days |
| Healthcare data models & domain depth | Generic methods | One use case | Build | Pre-built |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | Data Fabric Framework |
| Ready-made accelerators | Varies | Single product | None | Healthcare-in-a-Box + cross-industry |
| Supervised AI agents in operations | Pilots / PoCs | Copilot features | Build & govern | Agent squads with guardrails & AgentOps |
| Process change & adoption | Yes | Left to hospital | Partial | Yes |
| Run & continuous improvement | Separate contract | Product support | Internal team | Managed services |
| Total cost of ownership | High | Medium–High | Very High | Low |
Big-4 / SI (6–12 months), point products (3–6 months + integration) and in-house builds (12–18 months) are compared on larger screens.
Four phases, each ending with something you keep — and three ways to buy it.
Discovery & Planning
You receive a maturity assessment and target blueprint, with a prioritised roadmap.
Gate: pilot scope signed offAnalysis & Design
Target-state design and ontology, mapped to your controls; agent autonomy agreed with clinical governance.
Gate: design authorityBuild & Deploy
Landing zone as code, pipelines, accelerators and agents configured and tested on real data.
Gate: go-live readinessSupport & Embed
Runbooks, evaluation suites and knowledge transfer to a trained team.
Gate: handover sign-offStaff Augmentation
Architects, data and AI engineers embedded in your teams, under your delivery lead.
Project Delivery
Outcome-based delivery of an accelerator or platform build by a DaasLabs pod, with phase gates.
Managed Services
We run and improve the platform, models and agents — DataOps, MLOps and AgentOps under SLAs.
A 30–45 day accelerator pilot proves value in one department before you commit to scale.
Fixed scope, fixed timeline, agreed success criteria — and a scale-up business case at the end.
One accelerator & its agents
Typically Revenue 360 (claims & TPA desk) or Hospital 360 (beds & discharge), agents at Suggest or Act with approval.
2–3 source systems
e.g. HIS / EHR, billing and the laboratory system.
One department or service
Named clinical or finance owner and SMEs for rules and UAT.
DaasLabs pod
Engagement lead, data engineer, healthcare SME, AI / agent engineer.
Example targets; success criteria are agreed with you in week 1.
Three steps take us from this conversation to value on the wards.
Start small with one accelerator in one department, prove it, then scale on the same framework.
Scoping workshop
Walk through your systems and pain points — beds, discharges, denials — pick the pilot and agree success criteria.
1–2 weeksAccelerator pilot
Deploy one Healthcare-in-a-Box accelerator and its agents on your data, and measure the result.
30–45 daysScale & embed
Roll out across departments and further accelerators through project delivery or managed services.
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