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Case studies

We take over the technology and build fast, so you can focus on your customers.

We take ownership from the first build or inherited codebase through production, ongoing improvements and support.

Technologies used in these builds

TypeScriptReact.NETAzureAWSGoogle CloudRailwayPostgresRedisWhatsAppTemporalDockerKubernetesNext.jsNode.jsPythonRustBun
An Australian healthcare platform

Stuck in development for 8 months with no clear deadline in sight.

Grail took over from the previous vendor, rebuilt the platform and had it live in production within 30 days. By day 40, 100 business customers had been onboarded.

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Problem

The company had spent 8 months building the platform with another vendor, but it was still incomplete and unstable. Even a small bug fix could take days or a week, and fixing one problem often broke something else. With no reliable launch date, the company could not go to market.

What we built

Grail audited the three inherited codebases, kept the business rules that mattered and rebuilt the platform on one modern stack. We took responsibility for the application, infrastructure, testing and production.

Technology
TypeScriptReactHonoPostgresTemporalSentryAWS

Result

The rebuilt platform was live in production within 30 days. By day 40, the company had onboarded 100 business customers. Grail stayed on as its technology partner for new features, infrastructure and production support.

A telehealth software company

Months with a previous vendor. Grail completed the clinical workflow.

A previous vendor had left the clinical-record workflow unfinished. Grail took over and completed the path from doctor-patient conversation to a structured note with checked medication details and required doctor approval.

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Problem

The company had spent months with another vendor trying to add a clinical assistant to its telehealth product. The work was still unfinished, so doctors would have had to reconstruct the call, write the note and prepare medication instructions themselves.

What we built

Grail completed the workflow inside the consultation screen. It separates the speakers, turns the call into a structured draft, checks each medication detail against the doctor’s words and waits for approval before saving anything.

Technology
Next.jsTypeScriptGrail LLM GatewayOpenAIHL7 FHIR

Result

Doctors can finish a call with the clinical note and medication instructions ready to check. The public video shows the complete workflow with a fictional patient, so it explains the build without exposing customer or patient information.

A small financial-services operator

Three scattered lead sources. One live sales queue in 11 days.

A six-person company was losing hours every day copying leads from its website and social channels into the CRM. Grail put all three sources into one live sales queue in 11 days.

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Problem

New enquiries arrived through the website, Meta and TikTok. An administrator had to check every source and retype each lead into the CRM, which cost about two hours a day and delayed the sales team.

What we built

Grail connected the website, Meta and TikTok to a small TypeScript service. It checks each submission, puts the details into one format and creates or updates the CRM record. If a lead cannot be processed, the team can see the reason and retry it.

Technology
Next.jsTypeScriptRailwayZendeskMetaAWS

Result

The first 10 days processed 360 submissions. Meta and TikTok leads reached the CRM in about 1–2 minutes, and the old manual routine no longer consumed about two hours of admin time each day.

A Singapore-based neobank

A working project-management bot in 3 days

Important work was getting lost between calls, Microsoft Teams and the project board. Grail put a working project-management bot inside Teams in 3 days, then kept improving it as the team used it.

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Problem

People agreed work in conversations but did not consistently create or update tasks. The existing board no longer showed the real plan, permissions were unclear and follow-up depended on someone remembering to chase it.

What we built

Grail built a bot that turns a Teams conversation into a task on the company’s existing board. It records the owner, applies the right privacy rules and sends scheduled reminders through Teams or email. The board remains the team’s official task list.

Technology
Microsoft TeamsVikunjaPostgresMicrosoft GraphTypeScript

Result

The bot was working inside Teams in 3 days. The team can create and update work where conversations already happen, and later requests have been turned into working changes within hours.

A Singapore-based neobank

Eight transaction checks live in under 30 days

An inherited financial platform could show that money did not match, but not why. Grail built eight production checks in under 30 days, with every mismatch linked to the records and rule behind it.

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Problem

Deposits, withdrawals, card transactions and fees moved through different parts of the platform. When the numbers did not match, operators had to search through records and old code to work out where the difference began.

What we built

Grail mapped where each transaction started, which records it should create and how each balance should change. We turned those expectations into eight repeatable checks inside the existing .NET platform. A failed check shows the exact records, amounts and rule an operator needs to investigate.

Technology
.NETReactMicrosoft SQL ServerAzure FunctionsAzure

Result

Eight checks were live in under 30 days. The operations team gained one place to see correct transactions, missing records and amount differences, with the evidence needed to explain every result.

A Singapore-based neobank

A one-week build raised automated approvals from 44% to 90% in testing

A transaction review could take an operator 30 minutes or more. In one week, Grail built a rules-based review system that cleared straightforward cases automatically and sent uncertain cases to the compliance team.

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Problem

Reviewers had to collect the same evidence and apply the same policy rules to every transaction by hand. Even a straightforward approval could take 30 minutes, and the team had no safe way to see how a rule change would affect past decisions before using it.

What we built

Grail turned the written policy into a rules engine and built a review screen around it. The team can run new rules against the same set of past transactions, compare every changed decision and approve a policy separately before it affects live work.

Technology
BunTypeScriptReactMicrosoft SQL ServerAzure

Result

On the same set of past transactions, automated approvals rose from about 44% to about 90% while the compliance rules stayed in place. Straightforward cases could be cleared in seconds, while uncertain cases still went to the compliance team.

Singapore property workflow prototype

Lobang: a WhatsApp property-matching agent built in 2 weeks

Grail built a working first version in 2 weeks. It turns property listings and buyer requirements sent through WhatsApp into relevant matches, while keeping contact details private until both sides agree to connect.

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Problem

Property demand and supply already moved through WhatsApp, but matching them by hand was slow and inconsistent. Any automation also had to protect contact details until both sides wanted an introduction.

What we built

Grail built a WhatsApp agent that reads listings and buyer requirements, checks property-agent details against the Singapore CEA register and ranks the best matches. Contact details stay hidden until both sides agree to connect.

Technology
WhatsAppTemporalPostgresTypeScriptSlack

Result

The working agent was built in 2 weeks. Grail is now working with a small group of Singapore property agents to take the product into live use.