AI case studies
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Browse 45+ production engagements across 12 industries, with the numbers each system shipped.
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An independent US trading analytics firm lacked market sentiment analysis, so one analyst read financial news by hand & often missed breaking stories. Brainy Neurals built a scoring pipeline that uses generative AI to find the company named in every news article & social post. The trading desk reads four news sources in parallel on one dashboard, beside live price data. Tickers resolve from the sentence with no manual lookups, & sentiment reaches the desk in seconds rather than minutes.
View detailsA US digital security company needed multi-channel fraud detection, because scams reached its customers through fake profiles & phishing emails at once. Brainy Neurals built an agent-based platform that uses specialist AI modules under one coordinator to score every submission for fraud. Analysts submit profiles, emails, links or images through a review interface, & it replaced separate queues that people checked by hand. Each submission now returns one ranked risk verdict with the signals behind it, & unknown links escalate to deeper checks.
View detailsAn Indian diagnostic laboratory group needed medical document OCR because its clinicians read every lab & radiology report by hand before each consultation. Brainy Neurals built a pipeline that uses AI to read each page & fill one fixed summary. Running in production across the group’s full intake, the pipeline hands clinicians one summary page instead of eleven pages read by hand. The client reports that document review time fell by 60 to 80 percent, with no extra clinicians hired.
View detailsA global healthcare diagnostics & medical technology manufacturer couldn’t get quarterly revenue figures on demand across its regions & its sales teams. Brainy Neurals built a self-service revenue reporting platform that uses one shared set of business rules to pull & consolidate figures from the data warehouse. Finance & sales leaders now sign in to a secure web app, which replaced analysts rebuilding the same spreadsheets by hand every quarter. Reports run on demand with no manual data pulls, & the regional & team figures match every time.
View detailsA large US clinical diagnostics organization needed a self-service rate lookup, because every negotiated commercial rate took SQL & a data team ticket. Brainy Neurals built a browser tool that uses pre-computed rate tables & fixed weighting rules to return benchmark rates in seconds. Analysts on the Value & Access team sign in through single sign-on, search by code, payer, state or provider, & never open the warehouse. Four benchmark views now come back on every search, & every login, search & export lands in an audit log.
View detailsA US public health analytics organization pulled weekly influenza data by hand, one region at a time. Brainy Neurals built a natural language to SQL layer that uses a language model to turn plain English questions into database queries. An automated pipeline collects, cleans, deduplicates & loads the data into one warehouse on a schedule, replacing the manual downloads staff used to do. Now the data refreshes on its own, & non-technical staff get answers in seconds without writing SQL.
View detailsA US electrical contracting business handled electrical symbol detection by hand, & each estimator spent 30 to 60 minutes counting one power-plan sheet. Brainy Neurals built a symbol detection pipeline that uses trained computer vision detectors to find & label every symbol. Estimators upload the flat PDF drawing sets they already receive to a web app, & the app now makes the first count for them. A power-plan sheet that took 30 to 60 minutes by hand now comes back boxed & labeled in under 60 seconds.
View detailsA US banking group needed immediate answers from its customer & transaction databases, yet every question waited on engineers translating it into SQL. Brainy Neurals built an agentic text-to-SQL assistant that uses open-source language models to turn plain English questions into checked database queries. The assistant runs inside the bank’s own network, where business users ask in a web chat instead of filing a ticket. Answers that took up to a full working day now arrive in seconds, & no customer data leaves the building.
View detailsA global molecular diagnostics enterprise lacked GenAI observability, so nobody could tell leadership how its internal AI assistants got used or what they cost. Brainy Neurals built a monitoring platform that uses shared metric definitions to compare usage, cost, feedback & access across every assistant. Read-only collectors pull the records each assistant already writes, so commercial & finance leaders read one dashboard instead of analyst spreadsheets. Analysts no longer assemble quarterly usage reports by hand, & leadership now reads adoption, cost, latency, feedback & access in one place.
View detailsA large multinational molecular diagnostics enterprise needed an enterprise AI assistant, because its answers sat in systems most staff couldn’t query. Brainy Neurals built one that uses question routing & grounded search to answer from the right source with a citation. Teams across research, commercial & compliance ask it through one interface instead of sending tickets to analysts or IT. Staff now get sourced answers in seconds, & analysts no longer field routine lookups.
View detailsA US medical imaging organization needed DICOM de-identification on every CT & MRI study it shares with researchers, & staff handled each one manually. Brainy Neurals built a one-pass pipeline that uses a computer vision model to outline organs while it strips patient identifiers. The pipeline runs inside the client’s own network, reads each study once & replaced the manual header check before every handover. Each study now leaves with its patient identifiers removed & its organ masks attached as a standard DICOM-SEG file.
View detailsAn Indian denim manufacturer needed delivery challan automation, because one clerk had to retype every handwritten slip from its five partner weaving mills. Brainy Neurals built a textile ERP that uses a local vision model to read photographed challans into ledger rows. Everything runs on the client’s own server, where clerks photograph each slip on a phone & check the rows beside it. Data entry per challan fell from 10 to 15 minutes to under five seconds.
View detailsA global in-vitro diagnostics manufacturer needed a conversational analytics assistant because its commercial teams waited on analysts for every answer. Brainy Neurals built an assistant that uses large language models to turn plain-English questions into queries on the governed data warehouse. Marketing & commercial staff type into a secure chat, while analysts check the work in a second view that replaced hand-built SQL reports. Questions that once took days now come back in seconds, each one shown with the exact query behind it.
View detailsA US clinical research organization needed clinical protocol data extraction because its teams retyped the study parameters into eCRF templates by hand. Brainy Neurals built a document pipeline that uses language models to read each protocol & fill the eCRF files itself. The pipeline runs across the client’s active studies & feeds a study chatbot from the same data, replacing page-by-page manual reads. eCRF Excel files are now generated from each protocol instead of typed, with standardized terms & no rebuild for new studies.
View detailsAn Australasian land acquisition consultancy needed automated site screening, because staff checked every rural site by hand in 10 council map viewers. Brainy Neurals built a web platform that uses map layer checks & an AI assistant to score each address & explain the result. Staff enter one address or a batch on a dashboard that pulls each council’s public planning layers, replacing the 10 viewers. Screening now starts from one lookup, & every result keeps the council data it came from for the team to see.
View detailsA US strategy & innovation consultancy needed automated market research, because each read on a new business idea took days of scattered manual desk work. Brainy Neurals built a research platform that uses generative AI with live search to write a sourced report on each idea. Analysts paste an idea into the consultancy’s own intake, & the report replaces the days of browser tabs that came before partner review. The client reports market research time fell by about 70 percent against its manual baseline.
View detailsA US diagnostics & life sciences manufacturer needed automated competitive intelligence because analysts checked dozens of competitor & regulator sites by hand. Brainy Neurals built a collection platform that uses a language model to read each new article & file it under one category per company. Every week the pipeline runs on the client’s own cloud tenancy & replaces the rounds of opening sites one at a time. Analysts now search one archive of full article text, & adding a competitor takes one row in a source list.
View detailsA US molecular diagnostics manufacturer needed an automated data pipeline, because its team pulled flu data by hand from the CDC’s dashboards every week. Brainy Neurals built a scheduled system that uses scripted browser steps to export each dashboard feed & tidy it. Each weekly run loads the clean data into the client’s cloud warehouse, where demand & forecasting teams pick it up. Analysts stopped downloading files, & a change check now keeps duplicate loads out of the warehouse.
View detailsAn Australian EdTech startup needed learners to hear feedback on their Australian English pronunciation while they were still talking in a live conversation. Brainy Neurals built an AI voice tutor for Australian English that uses a realtime speech model to coach & score each spoken turn. The learner’s phone streams audio straight to the model, & a Python backend keeps each turn’s history in place of a human tutor’s notes. Learners now practice on first open with no account or schedule, & every utterance leaves three skill scores behind.
View detailsA US vehicle repair estimating software company wanted AI vehicle damage estimation because pricing each truck job from photographs took 45 to 60 minutes. Brainy Neurals built a pipeline that uses computer vision to find each damaged part, then machine learning to predict its repair hours. Estimators on the client’s platform now open a checked first draft instead of reading every photo by hand. A first draft now takes about five minutes of pipeline time, roughly a 90 percent cut in estimator time per job.
View detailsA UK construction contractor needed AI tender document analysis, because its analysts read hundreds of tender documents by hand before every bid decision. Brainy Neurals built a four-module platform that uses language & vision models to sort files, pull out requirements, measure drawings & draft replies. Analysts, surveyors, estimators & writers in the bid office now check its output instead of retyping every file. First-pass review now takes hours instead of days, & every extracted item links back to its source page.
View detailsA medical evidence synthesis organization needed systematic review automation because its researchers pulled every outcome from research papers by hand. Brainy Neurals built an AI paper analyzer that uses large language models to read each paper & extract every outcome with its statistics. The analyzer runs in production, where researchers approve each proposed outcome instead of typing it into private spreadsheets. Reviewers now go from raw PDF to appraised evidence in one pipeline, with a researcher’s approval on every outcome.
View detailsA European land development consultancy handled site plan analysis by hand, so an expert measured every plot boundary & building offset on each drawing. Brainy Neurals built a drawing reader that uses trained vision models to turn plan pixels into real-world units. Surveyors at the consultancy upload a plan & get plot areas with setback distances marked on it, in place of the scale rule. Measurement now takes one pass, & the system refuses any drawing it can’t read, stating the reason.
View detailsA US digital commerce organization needed product research automation, because hundreds of keyword tasks waited on analysts who worked each one by hand. Brainy Neurals built a task-triggered pipeline that uses AI agents to read each new task & write its keyword report back. The pipeline runs beside the client’s task platform around the clock & replaces the searching & copying that analysts once did in four tools. Research that took hours of analyst work now finishes in minutes, with no manual research steps left in each task.
View detailsBefore AI procurement automation, a UK construction & building materials company checked supplier prices by hand & found price changes only on invoices. Brainy Neurals built a platform that uses live web scraping & machine learning to rank supplier prices & forecast demand. The platform’s 12 modules read the company’s existing database, so buyers search seven supplier sites & the catalog with no migration. One search now returns ranked prices, & a buyer assistant answers spend questions from live data.
View detailsAn Indian ice manufacturer needed AI object counting because its loading gates kept miscounting ice blocks whenever four trucks loaded at the same time. Brainy Neurals built a camera system that uses object detection & tracking to count every block crossing each gate. One machine inside the plant reads all four conveyor cameras & replaces a tally that depended on whoever stood closest. Counts reach the owner’s phone every hour, & no video leaves the site.
View detailsA Spanish legal services organization needed AI contract review because it checked corporate contracts by hand in Spanish, Catalan & English for hours. Brainy Neurals built a review system that uses document AI & a language model to check every clause against known laws.
View detailsA US B2B sales consultancy needed AI lead follow-up automation, since inbound leads waited hours for replies & each follow-up lived in someone’s memory. Brainy Neurals built a follow-up engine that uses a large language model to qualify each email & draft every reply. Two workflows run inside the client’s own mailbox & CRM, pacing up to 30 follow-ups by business day & stopping on any reply. The client reports 10 to 15 hours of email & CRM work back each week, by its own estimate.
View detailsA US sports technology company wanted AI golf swing analysis for its golf coaching platform to replace 45-minute coach reviews & motion capture rooms. Brainy Neurals built a cloud engine that uses computer vision to measure each swing from two phone videos & write the report. Golfers & coaches film on any driving range, & the engine replaces the frame-by-frame coach review with scored swing faults. A full analysis now returns in under 60 seconds, down from 30 to 45 minutes of coach time per swing.
View detailsAn Indian architectural design firm needed an AI floor plan generator, since each residential client brief still cost a full manual CAD drafting session. Brainy Neurals built a two-agent system that uses AI agents to read the brief & place every room as real geometry.
View detailsAn EdTech company selling into US universities wanted an AI business simulation platform where students practise decisions & see what each one would cost. Brainy Neurals built a multi-tenant web platform that uses seven cooperating AI agents to run & score each business scenario in real time.
View detailsA recycling technology company needed AI battery sorting because its e-waste recovery sites kept no record of the batteries crossing their sorting belts. Brainy Neurals built belt scanners that use a camera model giving one battery many labels to read its type, chemistry, condition & heat. Each scanner decides on an NVIDIA Jetson beside the belt, so operators see live verdicts where a sorter’s glance used to be. Every battery on an equipped line now leaves with six label families & one of three routing verdicts.
View detailsA US performance marketing organization in e-commerce turned to AI ad creative generation because four separate specialist teams built every ad by hand. Brainy Neurals built a creative engine that uses generative AI to study winning ads, then write, illustrate, voice & cut new ones. Marketers brief it from a product page, review what comes back & push the chosen set into live campaign tests. The client reports creative production moved from weeks to hours, & new variations now test the same day.
View detailsA multi-brand European restaurant group checked hundreds of delivery platform invoices by hand each cycle, with no restaurant invoice reconciliation automation. Brainy Neurals built an automation pipeline that uses browser automation & layout rules to download every invoice, read its figures & fill the sheets. Runs sign in to three delivery platforms monthly or on demand, replacing the logins & retyping the finance team once did. The finance team now reviews finished spreadsheets instead of building them, & a dashboard shows every run live.
View detailsA US industrial group with cameras across its manufacturing plants & distribution warehouses needed an outside vendor for every new AI detection use case. Brainy Neurals built a no-code computer vision platform that uses object detection models to spot events in live camera video. Site teams run it on the cameras already in place, without writing code or calling a vendor. New use cases go live in days instead of months, & assisted labels cost 50 to 70 percent less than manual work.
View detailsA US industrial device manufacturer needed on-device object detection so its camera hardware could spot objects without sending every frame to the cloud. Brainy Neurals built a detection pipeline that uses a compact neural network to find objects in each live frame. The model runs on the neural processing unit built into each device & replaced a first version that sent each frame to the cloud. Each device now detects objects with zero cloud calls & keeps working when the network drops.
View detailsAn Indian smart-city authority policed traffic violations with manual patrols, and hand-written challans full of errors kept turning into disputes.
View detailsA multi-site construction group ran construction progress monitoring by hand, with walkthroughs & spreadsheets, so schedule slips stayed hidden until billing. Brainy Neurals built a 4D monitoring system that uses computer vision on 360-degree video to check every building element against the BIM model. One site engineer walks each floor with a camera on a pole, & floor-wise reports replace the old spreadsheet in progress meetings. The group reports 40 to 60 percent less supervision effort, with schedule deviations now flagged weeks earlier than before.
View detailsA youth, amateur & semi-professional basketball analytics platform needed automated basketball stats from video, as hand tagging fell behind at tournaments. Brainy Neurals built a pipeline that uses computer vision to track players & score every play. Inside the platform, coaches upload ordinary game recordings & each one returns seven event types, per-player stats, clips & an annotated video. Analysts now check that output instead of tagging by hand, & the client reports lower turnaround & cost per game.
View details
40–60% less supervision
A European national rail network’s electrification maintenance division measured contact wire height and stagger manually under live overhead wire. Brainy Neurals built a train-mounted stereo camera system that measures five geometry parameters as the train runs. Every reading is tagged to a mast and checked against tolerance on the live feed.
- 40–60% reduction in supervision effort
- Deviation flags weeks before billing disputes
- Floor-wise dashboards and PDF engineering reports, rolled out across multiple sites with reduced rework
10–20x coverage
A US-licensed gastroenterology practice needed patients to follow strict medical diets — celiac disease, Crohn’s, IBS, cirrhosis — without calling the front desk over every meal. We built a Flutter app whose 13 AI modules all run through one GPT-4o mini engine, with every response filtered through the patient’s diagnosed condition, physician-approved diet plan, dietary preference, and allergies. Rule-based prompt templates enforce those constraints; the model never answers outside them.
- 10–20x inspection coverage vs walking patrols
- 50% targeted pantograph wear reduction
- Manual walking inspections eliminated, no linemen on masts
How an engagement becomes a case study.
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01
Scope
A 30-minute architecture call, a written scoping summary, and a fixed-cost POC proposal. NDA available before the first call.
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02
Build
A POC sprint on representative data. It ends with a working demo and a written go or no-go decision.
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03
Ship
Production build, integration with your systems, deployment on your infrastructure, runbook and handoff.
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04
Measure
Monitoring, drift detection, quarterly model refresh. Metrics tracked where they matter, in production.
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05
Publish
A case goes public only with client sign-off and a metric that held. Most engagements stay private.
Engagements that don’t follow this path: staff augmentation engagements (where we embed engineers into the client’s team and the client owns the architecture) follow a different model. Those engagements aren’t represented in the case studies above because the architecture isn’t ours to publish.
How our case studies compare to typical vendor portfolios.
Most enterprise AI vendors publish case studies that look the same: a client logo, a problem sentence, an outcome metric, and a quote. The table below uses the evaluation criteria that actually matter when a CTO is deciding whom to trust with a real production engagement.
| Evaluation criterion | Brainy NeuralsCase studies | Typical generalist vendorPortfolio |
|---|---|---|
| Architectural depth disclosed | Component diagram, technology choices, and trade-off log included in every detail page. Reference architecture PDFs available for the most-used patterns. | Outcome metric only. Architecture occasionally summarized at a marketing level. Detailed component diagrams rarely available. |
| Named architect attribution | Every detail page names the architect who led the engagement, with credentials and a LinkedIn link. Buyer can verify the architect still works here. | Generic ‘our team delivered’ attribution. Named individuals (if any) are senior salespeople, not the engineers who built the work. |
| Trade-offs and failure modes documented | Each detail page includes a ‘what we considered and rejected’ section, plus the failure modes the architecture engineers for. | Trade-offs treated as proprietary or simply not discussed. Failure modes rarely acknowledged. |
| Reference call availability | Reference calls available on request for every detailed case study, mediated by us. Clients have agreed to take the call at engagement signing. | References promised but rarely delivered. Calls often facilitated only at proposal stage and only with the client’s marketing contact. |
| Publication standard transparency | Three explicit conditions for publication (client sign-off, reusable pattern, verifiable metrics) – disclosed in the editor’s note on this page. | Publication criteria not disclosed. Case studies appear to be cherry-picked from the most flattering engagements. |
A note from the founder
Why we don’t publish every engagement we ship.
Most case study pages are sales documents pretending to be portfolios. I want to be straight about how this one is curated.
We have shipped every engagement listed here. You see 45 cards because that is what we can publish honestly today: real industry, real capability, real number, real stack. Client names are withheld wherever an NDA requires it, and in banking and healthcare it usually does.
A full public write-up goes out only when the client signs off on attribution and the metric still holds in production. That rule keeps the count lower than our marketing team would like. I consider that a feature.
If you are comparing us against larger generalist firms, ask one question: do the people who built the case studies still work there, and can you talk to them directly? Here the answer is yes. The architect on the call is the architect on the project.
Frequently asked questions.
FAQ-01Do you have case studies in my industry?
FAQ-02Can I get reference calls with your clients?
FAQ-03Why are most engagements anonymized?
FAQ-04What separates a case study from an engagement?
FAQ-05Can I see actual architecture diagrams and code?
FAQ-06How recent are these case studies?
FAQ-07Did you only publish your best work?
FAQ-08How does my engagement become a case study?
FAQ-09Why do most cards expand instead of opening a page?
Related resources.
Services hub
11 specialized AI development services, each with case examples.
Blog & insights
Engineering deep-dives and architecture pattern explainers.
About Brainy Neurals
The team behind the work and the certifications that back it.
Founder profile · Mitesh Patel
NVIDIA Certified AI Architect. The architect who leads the engagements above.
Talk to the architect
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