Rajipo / Services

Advice you can hold. Software you can run.

Advisory, custom AI builds, and deployment we stay to operate — from LLM assistants to voice AI, integrated with the systems you already run.

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Advise

AI · Feasibility · Roadmaps

Use-case triage, feasibility and data-readiness — an honest map of where AI pays off in your operation, and where it doesn’t. If AI isn’t the right tool for the problem, we say so before you spend.

We sit with the people who do the work — the front desk, the ward, the analysts — before recommending anything.
You get a written, prioritised map: where AI genuinely pays off, what the data can support today, and what it would take.
If the honest answer is “you don’t need AI for this,” that’s the answer you get.

AI opportunity assessment
Feasibility & data readiness
Roadmaps scoped to budget

Assembly

Build

Custom · Voice · Decision

We design and build working, product-grade AI around your actual workflow — diagnostic support, voice agents, decision support — scoped to the budget and constraints you really have. Not a proof of concept.

Working software around your workflow — not a slide deck, not a lab demo that dies in procurement.
Scoped to the budget and hardware you actually have; our flagship clinic product exists because small facilities needed exactly that.
Every build carries a clear record of how the AI reaches its suggestions.

Custom AI products
Voice & conversational agents
Decision support & automation

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Calibration

Run

Deployment · Accuracy · Ongoing

We put the product into the ward, the control tower, the close — wherever your work lives — then stay to tune accuracy, trust and adoption long after a demo would have ended.

Deployment into the live room, ward or phone line — where the product has to earn the team’s trust.
We measure accuracy, trust and adoption against real use, and tune until they hold.
Support continues long after launch; the demo is the beginning, not the end.

Deployment into live workflows
Accuracy & adoption tuning
Ongoing field support

Engagement packages

Four ways in. Each one concrete.

No mystery scoping. Every engagement starts from one of these four packages — and the first one is designed to be a safe first step.

01Start here

AI Readiness Assessment

2 weeks · fixed scope

A two-week, fixed-scope audit of your workflows, data and constraints. You get a written, prioritised map of where AI genuinely pays off in your operation — and where it doesn’t.

What you get

Workflow & data-readiness audit, done with the people who do the work
A prioritised opportunity map with honest effort and budget ranges
A build / buy / wait recommendation you can act on immediately

Leaders who suspect AI could help but want an honest map before spending.

Start with this package →

02Prove it

Pilot Sprint

4–6 weeks

One workflow, one working AI system — deployed live with real users and measured against real work. Not a demo: a pilot that earns (or doesn’t earn) the rollout.

What you get

A working agent on the single workflow that hurts most
A live pilot where the work happens, with real users
A measured accuracy-and-adoption report and a go / no-go rollout plan

Teams with one obvious bottleneck who want proof before committing.

Start with this package →

03Ship it

Full Implementation

Scoped per engagement

Product-grade AI built and integrated with the systems you already run — EHR, phones, calendars, ERP — with guardrails, evals and audit trails engineered in from day one.

What you get

Production deployment — cloud, on-premise or hybrid
Integration with your existing systems, not beside them
Guardrails, human gates, eval suites and audit trails as architecture
Training for the team who’ll live with it

Organisations ready to put AI into daily operation.

Start with this package →

04Keep it working

Managed AI

Ongoing

We operate what we build. Monitoring, accuracy reviews, prompt and model updates, and tuning against real use — long after a demo would have ended.

What you get

Monitoring and accuracy reviews against live traffic
Model, prompt and guardrail updates as your work changes
A quarterly value review: what it carried, what it should carry next

Teams who want the system to keep earning trust without hiring an AI department.

Start with this package →

AI implementation

What we implement.

The building blocks of an engagement — each one deployed inside your workflow, not beside it.

AI assistants & copilots

Assistants grounded in your own documents, data and rules — retrieval-augmented, with an audit trail for every answer they give.

Voice AI for the phone line

Agents that answer, schedule and route front-desk calls naturally — and hand off to a human the moment it matters.

Document & workflow automation

Intake, notes, claims, reports — the paperwork read, drafted and filed by AI inside your existing process.

Decision support & forecasting

Risk flags, demand forecasts and recommendations that show their reasoning — built for audited environments.

System integration

EHR/EMR, calendars, phone systems and databases — clean APIs where they exist, careful bridges where they don’t.

Private & compliant deployment

Cloud, on-premise or hybrid. HIPAA-aware handling for health data, role-based access — and your data never trains outside models without agreement.

The agentic engineering behind all of this

Deployment

From first conversation to running in production.

A typical implementation, end to end. Scope moves the numbers; the sequence doesn’t change.

Scan

Discovery & data readiness

We map the workflow, the data and the constraint that matters — budget, hardware, compliance.

Weeks 0–2

Assembly

Build & integrate

The product takes shape against your real systems — EHR, phones, calendars — not a sandbox.

Weeks 2–6

Rollout

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Pilot where the work happens

A live pilot in the clinic, the warehouse or the back office. Real users, real work, measured accuracy and adoption.

Weeks 6–8

Calibration

Operate & tune

Monitoring, accuracy reviews and tuning against real use — long after a demo would have ended.

Week 8+

Typical ranges for a first deployment — larger integrations move them.

Deployment principles

The rules we don’t bend.

Every engagement, every industry, every deployment — these hold.

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i No black boxes — every suggestion keeps a record of how it was reached
ii Your data stays yours — it never trains outside models without written agreement
iii Humans stay in charge — handoff and override are designed in from day one
iv Scoped to reality — sized for the budget and hardware you actually have