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AI and software case studies

Case studies, measured in production.

AI, platforms and data systems built and run by our engineers: an AI support assistant that took 90% of tickets off the queue, backends at 40,500 requests a minute, and client systems measured to the percent.

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Measured live
Every number comes from a system in production
Built and run
By the engineers who designed it
Every layer
From the processor to the AI agent

Proven at scaleSystems built and run for millions of users, and for clients who measured the result.

Flagship case studies

The hardest problems, told from the inside.

Systems our engineers built and ran in production for a consumer platform with millions of players. Each one is told the way an engineer would want to read it: the problem, the architecture, the decisions and the numbers.

AI in productionEngineering record · consumer platform

An AI support assistant that answers before a ticket exists.

Support queries about games, payments and fraud surged by 700%. Every one of them waited in a queue for a person, and players waited with it.

  • −90%support ticket volume
  • −85%time to resolve the queries that still reach people
  • +30%customer ratings

The helpdesk behind it absorbed the 700% surge and lifted revenue per user by 10% among the most active players.

Support that resolves itselfarchitecture

  1. SignalsPlayer eventsgames · payments · sessions
  2. StreamKafkalive game events
  3. UnderstandLive analysisevery game as it happensHistoryEKS jobs over Athena
  4. ResolveAI support assistantanswers on its own
  5. EscalateHelpdeskZendesk · Freshdesk · SQSSupport agentsonly what is left

What we built

  • A helpdesk service that brings game, payment and fraud queries into one place, kept in step with Zendesk and Freshdesk through SQS so agents work in the tools they know.
  • An in-house assistant that resolves queries on its own. It reads each player's games as they happen through Kafka, and their history through scheduled jobs on Kubernetes over Athena.
  • People see only what the assistant cannot close, and they close it faster.

Stack

  • Node.js
  • Kafka
  • Amazon EKS
  • Athena
  • S3
  • SQS
  • Zendesk
  • Freshdesk

Delivered today as

Platforms at scaleEngineering record · consumer platform

A reward engine at 40,500 requests a minute.

The business had to win lapsed players back, lift deposits and reward its best players in real time, without a coupon ever reaching someone it should not.

  • +25%average revenue per user
  • +20%conversion
  • +15%return on investment, from live segmentation

Referrals lifted signups by 10%. The VIP programme lifted revenue per power user by 8% and their retention by 10%.

Rewards, decided per player, in real timearchitecture

  1. PlayersApp and gamesdeposits · games · referrals
  2. ServeReward serviceNode.js · auto-scalingReferral service23,300 requests a minute
  3. DecideLive segmentsRedis, per player
  4. LearnKafkaevery eventS3 and Athenaaggregates, near real time
  5. DeliverSQS workerscoupons · VIP rewards

What we built

  • A reward service that runs targeted coupon campaigns to keep players, lift deposits and bring lapsed players back, on an auto-scaling fleet with SQS between the steps.
  • A segmentation service that holds every player's segment in Redis, updated live from Kafka and synced from S3 and Athena, for fraud control, targeted offers and matchmaking.
  • A referral service at 23,300 requests a minute, and a VIP programme with free passes, tax refunds, priority support and leaderboard multipliers.

Stack

  • Node.js
  • MongoDB
  • Redis
  • SQS
  • Kafka
  • S3
  • Athena
  • Docker
  • EC2
  • Auto Scaling
  • Zookeeper

Delivered today as

Real-time systemsEngineering record · consumer platform

A game backend built from scratch, carrying 3% of company revenue.

A new real-money game had to hold thousands of live matches at once, run tournaments to the second, and never keep a player waiting for an opponent.

  • 10,000players in live games at once
  • 3%of company revenue, from one game
  • +50%games per player, after continuous gameplay

The platform's services later moved from Docker hosts to Kubernetes on Amazon EKS, for scale and reliability.

Live play, matched in the momentarchitecture

  1. PlayersMobile players10,000 at once
  2. ConnectLive gameplaySocket.io
  3. MatchMatchmakingRedis · Bull, instant
  4. RunTournamentsHTTP servicesScheduled workersBull jobs
  5. PlatformAmazon EKScontainers, scaled

What we built

  • Tournament services over HTTP, scheduled workers on Bull and Redis, and Socket.io for live gameplay, designed from the first line for 10,000 players at once.
  • Continuous gameplay: a new opponent is matched the moment a game ends, through scheduled matchmaking on Redis, so the next game starts without a wait.
  • The game shipped as its own backend and grew to carry 3% of the company's revenue.

Stack

  • Node.js
  • Socket.io
  • Redis
  • Bull
  • Docker
  • Amazon EKS

Delivered today as

Client systems

Built by DigyAi, measured by the client.

Systems in production today, in our clients' own environments. Each carries the number its client measured and reported.

  • Agriculture and food

    92%fewer payment calculation discrepancies

    Dairy collection and payout platform

    Dairy collection network

    Field apps that record every collection at the point of entry and work offline, with a payout engine that computes each farmer's payment from quantity and rate.

    Reporting went from monthly to daily.

    DigyAi helped us replace manual registers and spreadsheets with a centralised digital system that improved operational visibility and accuracy.

    Vikash Joshi, dairy collection network
    • React Native
    • Node.js
    • Postgres
    • Offline sync
  • Finance and lending

    85%less time on interest calculation and reconciliation

    Interest accounting ledger

    Wholesale credit business

    A ledger per customer that computes interest on every balance and traces every figure to a transaction, with statements on demand from a mobile-first app.

    Balances now agree across every customer ledger.

    Before this system, interest calculations and customer balances were handled manually. DigyAi helped us automate the entire process and significantly reduced accounting effort.

    Naresh Goyal, wholesale credit business
    • Next.js
    • Node.js
    • Postgres
    • PWA
  • Education

    55%faster admissions processing

    College ERP

    Private college

    One platform for admissions, student records, attendance and faculty, with reports read straight from live data.

    Four disconnected manual systems became one.

    DigyAi delivered a platform that simplified administration and improved communication across the institution.

    Deepak Bagaria, private college
    • Next.js
    • Node.js
    • Postgres
  • Healthcare

    50%faster patient intake and scheduling

    Hospital ERP

    Private hospital

    Patient management, appointments, billing and staffing on one system of record, with role-based access for each department.

    Administrative reports take minutes instead of days.

    The new system streamlined our operations and provided visibility across departments that we never had before.

    Manish Kumar, private hospital
    • Next.js
    • Node.js
    • Postgres
    • RBAC
  • Construction

    35%less time on manual status reporting

    Construction operations platform

    Shri Krishna Construction

    One view of progress and resources across every active site, with status updates filed from a phone in the field.

    Resource gaps surface before they hit the timeline.

    We used to find out about a resource gap after it had already slowed down a site. Now we see it coming.

    Shri Krishna Construction
    • Next.js
    • React Native
    • Postgres
  • Regulatory and trade advisory

    Livesince 2 September 2026

    Trade Bridge Advisors

    Mumbai

    The firm's practice as a website built to be found and quoted: every practice area a service with its own lifecycle of work, a library of sourced articles dated to their last legal check, a registrations catalogue, and plain-text summaries for AI answer engines.

    Served static from a CDN, with every article dated to the day it was checked.

    Visit tradebridgeadvisors.com
    • Next.js
    • Static export
    • CDN
    • llms.txt
    • Schema.org

Range

From the processor to the agent.

Our engineers have built at every level of computing, from the logic inside a chip to agents that run a business. Whatever you need built, someone here has already built what sits beneath it.

How we engineer
  1. SiliconA pipelined 16-bit RISC processor on an FPGA, with hazard detection, forwarding and branch prediction.
  2. SignalsBrainwave patterns read from an EEG headset and classified with CNN and RNN models, for wheelchair and home control.
  3. LanguageQuestion answering over a two-million-line corpus, with word embeddings and similarity search.
  4. ML platformsA self-serve platform that runs data scientists' models over very large Hadoop datasets, at one of India's largest e-commerce companies.
  5. Real timeLive gameplay for 10,000 players at once, and backends at 40,500 requests a minute.
  6. DataKafka, S3 and Athena pipelines, and 100 GB moved between databases with its cost down 40%.
  7. CloudServices moved to Kubernetes on Amazon EKS, with CI/CD that stands up a stage environment on demand.
  8. SecurityMore than 100 critical vulnerabilities found and fixed, in internal hackathons won in consecutive years.
  9. AI in productionA support assistant that took 90% of tickets off the queue.
  10. AgentsToday: AI agents that finish work inside a client's own systems.

The full record

Every system, on one sheet.

Client systems first, then the engineering record behind them. Every result was measured in production.

Client systemsBuilt by DigyAi. Results measured and reported by each client.
SystemWhat it doesResultService today
Dairy collection and payout platformField apps and a payout engine for a dairy collection network92% fewer payment calculation discrepanciesCustom software
Interest accounting ledgerA ledger per customer, interest on every balance, statements on demand85% less time on interest calculation and reconciliationCustom software
College ERPAdmissions, records, attendance and faculty on one platform55% faster admissions processingModernization
Hospital ERPPatients, appointments, billing and staffing on one system of record50% faster patient intake and schedulingCustom software
Construction operations platformProgress and resources across every site, filed from the field35% less time on manual status reportingCustom software
Trade Bridge Advisors websiteA trade advisory firm's practice, built to be found and quotedLive since 2 September 2026Custom software
Engineering record: consumer platformBuilt and run in production by our engineers, for a consumer platform with millions of players.
SystemWhat it doesResultService today
AI support assistantResolves game, payment and fraud queries on its own−90% tickets, −85% resolution time, +30% ratingsAI agents
Helpdesk serviceOne queue, synced with Zendesk and FreshdeskAbsorbed a 700% surge; +10% revenue per power userManaged services
Reward engineTargeted coupon campaigns at 40,500 requests a minute+25% revenue per user, +20% conversionCustom software
Segmentation serviceLive segments for fraud control, offers and matchmaking+15% return on investmentData engineering
Referral serviceOrganic acquisition at 23,300 requests a minute+10% signupsCustom software
VIP programmePasses, refunds, priority support and multipliers+8% revenue per power user, +10% retentionCustom software
Game backendTournaments, workers and live play, from scratch10,000 players at once; 3% of revenueCustom software
Continuous gameplayInstant matchmaking on Redis and Bull+50% games per playerCustom software
Operations dashboardsProduct, engineering, CRM and analytics, with access control and logsOne console for four teamsCustom software
Query serviceOn-demand, scheduled and long-running Athena queriesSelf-serve analyticsData engineering
Datastore migration100 GB of player aggregates, DynamoDB to Aerospike−40% costModernization
Kubernetes migrationServices moved from Docker hosts to Amazon EKSBetter scale and reliabilityCloud & DevOps
Security hackathonsInternal hackathons, won in consecutive years100+ critical vulnerabilities fixedManaged services
Engineering record: ML platformBuilt by our engineers at one of India's largest e-commerce companies.
SystemWhat it doesResultService today
Self-serve ML platformModels over very large Hadoop datasets, on distributed computeParallel runs and inputs, automatedAI development
Sandbox APIsA command line where data scientists try models before they deployModels tried before they deployAI development
  • Measured in productionEvery result here was measured on a live system. Nothing is modelled or projected.
  • Reported by the clientA client system's result is the number that client measured and reported.
  • The engineering recordSystems our engineers built and ran earlier in their careers are marked as their record, never as client work.

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The record

Which of these systems did DigyAi build for clients?

The client systems section is DigyAi's own client work: the dairy payout platform, the interest ledger, the college ERP, the hospital ERP, the construction operations platform and the Trade Bridge Advisors website. The flagship case studies and the engineering record are systems our engineers built and ran in production earlier in their careers, for a consumer platform with millions of players and at one of India's largest e-commerce companies. The same engineers build for our clients today.

How were the results on this page measured?

Every result was measured on a system in production, never modelled or projected. A client system's result is the number that client measured and reported to us. The engineering record's results come from the platforms' own analytics, measured by the teams that ran them.

What are examples of AI and custom software in production?

On this page: an AI support assistant that cut support tickets by 90%, a reward engine serving 40,500 requests a minute, a real-time game backend for 10,000 players at once, a dairy payout platform with 92% fewer payment errors, an interest ledger that took 85% of the time out of reconciliation, and a college ERP that made admissions 55% faster. Each is described with its problem, its architecture and its measured result.

Why are most clients not named?

A client is named only with its permission, because the systems we build run its business. Trade Bridge Advisors and Shri Krishna Construction are named; the others are described by sector, with their results and their own words.

Scale and depth

What scale have your engineers run in production?

Backends at 40,500 requests a minute at peak, 10,000 players in live games at once, a referral service at 23,300 requests a minute, and near-real-time data on Kafka, S3 and Athena for millions of users. The same standards of load, failure and cost go into every client system.

Have you put AI into production, or only built pilots?

In production. An in-house AI support assistant our engineers built cut support ticket volume by 90%, cut the time to resolve the remaining queries by 85% and lifted customer ratings by 30%. Every AI system DigyAi builds is graded on the client's own cases before it reaches a customer.

Which technologies are behind these systems?

Node.js, Java, Python and C++; MongoDB, Redis, PostgreSQL, MySQL, DynamoDB and Aerospike; Kafka, SQS, RabbitMQ and Kinesis; and AWS from EC2 and Auto Scaling to EKS, S3, Athena, CloudFront and Aurora. For client work the stack follows the problem and the client's own environment.

Your system

Can DigyAi build a system like one of these for us?

Yes. Write to us with the problem, the systems involved and the number that would tell you it worked. After a short discovery you receive a written plan with the scope, architecture, risks and milestones of each phase.

Who owns the code of a system DigyAi builds?

You do: the code, the data and the IP, from the first commit, in your own repository and cloud account. The runbooks and architecture records are yours too, so any team can run the system after us.

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