Customer Stories

Building AI-Native Organisations

Q: Are these case studies real?

Yes. Each one is a delivered engagement with the client named where they agreed to it, and the metrics are the ones we reported at handover rather than rounded-up marketing figures.

Q: What kinds of results do you publish?

The numbers the client used to judge the work — throughput, accuracy, time saved, routing precision. Where a result was mixed we say so, because a page of unbroken wins is not credible.

Q: How long did these take to deliver?

Four weeks to a first agent in production is the standard shape. Programmes that continued past that are described as ongoing rather than presented as one-off wins.

Q: Can we speak to a reference?

For several of these, yes. Ask on a scoping call and we will arrange it where the client has agreed to take reference calls.

Q: Do you have a case study in our industry?

Financial services, healthcare, e-commerce and BPO are covered. If yours is not, the closest workflow shape usually matters more than the sector label.

Q: What did the client have to do?

One process owner, one engineer with system access, and a weekly review. Every one of these was delivered with the client team owning the system afterwards.

Q: Do you only publish successes?

We publish engagements that reached production. Where something in one did not work we describe it, because that is usually the most useful part for the next team.

Q: How much did these cost?

Costs are not published per engagement because scope varies. Pricing is a platform subscription plus a fixed-scope implementation fee, quoted before work starts.

Q: Are the systems still running?

Yes. Clients own the weights, datasets, eval suites and runbooks, and most keep a quarterly eval review with us to catch drift.

Q: How do we start something similar?

Book a working session with the workflow you have in mind. If we have done something close we will show you what it took.

How teams across industries used Reinforce to ship AI agents that actually work in production.

VOICE AI PLATFORM

Kallix

How Kallix automated 30,000+ minutes of sales & support calls with AI voice agents

30k+AI call minutes handled monthlyMultilingual agents across Hindi, English, and regional Indian languages — with human-level accuracy and zero dropped context.

30%Reduction in missed leadsAI agents pick up every call, qualify leads in real time, and book appointments without human intervention.

24/7Uptime coverage achievedNo after-hours drop-offs, no staffing gaps — the AI fleet runs continuously across time zones.

Read full case study

Bhartiya Didi — AI-guided triage connecting patients to the right specialist at ShardaCare

HEALTHCARE AI

ShardaCare / Bhartiya Didi

Bhartiya Didi — AI-guided triage connecting patients to the right specialist at ShardaCare

< 2 minAvg triage time from symptom to referralPatients describe symptoms in plain language and are routed to the right department instantly — no forms, no hold music.

98%Routing accuracy verified against clinical pathwaysValidated against 12,000 verified clinical pathway records by ShardaCare's senior medical staff.

71%Cost reduction in triage operational costAI handles the first-touch triage layer, freeing medical staff for clinical work that requires judgment.

Read full case study

Automating 91% of claims processing with AI agents

INSURANCE

Dutch Insurance Provider

Automating 91% of claims processing with AI agents

91%Automated of motor claimsHandled thousands of motor claims monthly, freeing adjusters for complex cases.

46%Faster processing timeAutomated claims processed significantly faster, reducing customer wait times.

9%Boost in NPS scoreFaster turnarounds and consistent decisions boosted customer satisfaction.

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Reducing patient response time to minutes while lowering costs by 93%.

HEALTHCARE

Avi Medical

Reducing patient response time to minutes while lowering costs by 93%.

81%Automated of Patient InquiriesRoutine inquiries were handled efficiently and accurately, allowing the human support team to focus on higher-value interactions.

87%Decrease in Response TimePatients received faster answers to their questions, improving their overall experience and satisfaction.

93%Decrease in Operational CostThe AI solution proved to be far more cost-effective than hiring additional staff, delivering significant savings for Avi Medical.

Read full case study

Automating 93% of Data Aggregation with AI Agents

AGRICULTURE

US Agribusiness Insurance

Automating 93% of Data Aggregation with AI Agents

93%Automated Data AggregationAutomated extraction of 15,000 data points monthly, enabling faster policy issuance and improved responsiveness to customer needs.

85%Faster Data AggregationWe significantly accelerated the underwriting process, allowing underwriters to focus on complex risk analysis and decisions.

7%Increase in NPSFaster processing times and improved accuracy led to a significant increase in customer satisfaction reflected by NPS.

Read full case study

Explore More

E-COMMERCE

Scaling Batavia's digital store with AI automation

Helping Batavia automate product delivery and customer workflows so their store handles more sales with less manual work.

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E-COMMERCE

Mandala: intelligent order and notification automation

Helping Mandala automate order processing and customer workflows to handle more sales without increasing operational workload.

Read story

UGC AGENCY

Pandawa: faster onboarding, less overhead

Helping Pandawa automate customer onboarding and internal workflows to scale faster without adding more operational overhead.

Read story

RESEARCH & DEVELOPMENT

"Humanity's Last Exam": The Benchmark That Proves AI is...

MMLU is solved. GSM8K is a joke. 'Humanity's Last Exam' is the new wall, an...

Read story

TOOLS AND FRAMEWORK

Rust for AI: The Antigravity Manager and the Python Exodus

Python is the language of training, but Rust is becoming the language of inference an...

Read story

AI ECOSYSTEM

"Data Engineering Zoomcamp": Why AI Engineers Are Learning...

The hottest repo on GitHub isn't a new model; it's a course. AI Engineers have...

Read story

Questions people ask

Are these case studies real?

Yes. Each one is a delivered engagement with the client named where they agreed to it, and the metrics are the ones we reported at handover rather than rounded-up marketing figures.

What kinds of results do you publish?

The numbers the client used to judge the work — throughput, accuracy, time saved, routing precision. Where a result was mixed we say so, because a page of unbroken wins is not credible.

How long did these take to deliver?

Four weeks to a first agent in production is the standard shape. Programmes that continued past that are described as ongoing rather than presented as one-off wins.

Can we speak to a reference?

For several of these, yes. Ask on a scoping call and we will arrange it where the client has agreed to take reference calls.

Do you have a case study in our industry?

Financial services, healthcare, e-commerce and BPO are covered. If yours is not, the closest workflow shape usually matters more than the sector label.

What did the client have to do?

One process owner, one engineer with system access, and a weekly review. Every one of these was delivered with the client team owning the system afterwards.

Do you only publish successes?

We publish engagements that reached production. Where something in one did not work we describe it, because that is usually the most useful part for the next team.

How much did these cost?

Costs are not published per engagement because scope varies. Pricing is a platform subscription plus a fixed-scope implementation fee, quoted before work starts.

Are the systems still running?

Yes. Clients own the weights, datasets, eval suites and runbooks, and most keep a quarterly eval review with us to catch drift.

How do we start something similar?

Book a working session with the workflow you have in mind. If we have done something close we will show you what it took.