AI that removes the manual work your team repeats every day.
These systems run in production (one proof of concept is marked as such). They were built for 24×7, regulated and safety-critical operations, and they're described here without client names. Each one shows what it solved and where the same pattern fits your business, either as-is or with a few changes.
25+ yearsbuilding and scaling enterprise software
Production GenAIRAG, agents, vector search, multilingual voice
Regulated domainsaviation, medical devices, financial services, and FDA-regulated clients through 21 CFR Part 11 audits, including e-signatures
Aviation · Frontline employee productivity
Your frontline staff need answers from ten systems. Mostly, they get a phone tree.
Sound familiar?
Manuals, policies, schedules and HR information are spread across portals. The people who need them are on the move, on a phone, at any hour. There's an internal bot, but it gets half the questions wrong, so nobody trusts it.
What was built
One conversational interface over both documents and live operational systems. It uses retrieval over manuals, vector search, and agents that call system APIs. Guardrails suit a safety-critical domain, and models are chosen per task to keep token cost under control.
Results
1,000+ daily active users
99.5% of questions answered
Faster access to information across disconnected systems
Where it transfers
DirectAny team living by SOPs, policies or manuals: plants, hospitals, bank branches, franchise networks
With tweaksCustomer-facing support that looks up orders, bookings or account status
Medical devices · Field service
Your best engineer knows the fix. Your newest one is the one on site.
Sound familiar?
Diagnostic know-how sits in thick manuals, old tickets and a few senior heads. Field engineers call the experts, make repeat visits, or replace parts they didn't need to. Every one of those is a direct cost.
What was built
A retrieval (RAG) platform over service manuals, past tickets and fault histories. The engineer describes the symptom and gets likely causes and next steps, each with the source it came from.
Results
Lower on-site repair cost for an international device maker's field teams
Expert knowledge available to every engineer, with its sources
Where it transfers
DirectEquipment makers, elevator and HVAC service, telecom field operations
With tweaksVehicle workshops, IT helpdesks, facilities management
Talent acquisition
Hundreds of CVs, one job description, and a shortlist due tomorrow.
Sound familiar?
Recruiters read CVs against the job description by hand. The must-haves get blurred with the nice-to-haves, and strong profiles written in unusual language get missed. When a client asks "why these five?", the answer is gut feel.
What was built
A recruitment copilot. It breaks a job description into must-have and nice-to-have criteria, scores each CV with evidence pulled from the CV itself, flags gaps, and produces a ranked shortlist the recruiter can defend line by line.
Results
Days → minutes to shortlist candidates against a job description
Evidence-backed rankings recruiters can explain to hiring managers
Where it transfers
DirectExecutive-search research teams, in-house talent acquisition, staffing firms
With tweaksVendor and RFP evaluation, tender scoring, admissions or grant screening
Aviation · Crew operations
Someone drops out of a critical shift. Who covers, and how fast can you find them?
Sound familiar?
A person rostered for a critical duty becomes unavailable. The manager opens several systems and checks eligibility, qualifications and rest rules, then works the phone. Every location repeats this every day, and one wrong pick becomes a compliance problem.
What was built
An agentic workflow inside the chat tool staff already use. It reads the roster and the eligibility rules, checks who is assigned elsewhere, and returns only replacements that pass every rule. A human approves the final choice.
Results
50%+ less effort spent finding a swap
100% accuracy in identifying eligible replacements
One manual task fully automated for every location manager
Where it transfers
DirectHospital and clinic shift cover, security and facility staffing, driver allocation in logistics
With tweaksField-service dispatch, retail store rosters, backup-candidate lists when an offer falls through
Manufacturing · Distributed team
Action tracking for a distributed manufacturing team.
Sound familiar?
Decisions from the Monday meeting live in someone's notebook. Follow-ups happen when someone remembers. By Friday, nobody is sure who owns what, and the teams are in different cities.
What was built
ChaseKaro, an action-item tracker inside Telegram. Items are created in chat or bulk-uploaded from a spreadsheet. Each owner gets reminders where they already are, and the senior team sees status and overdue items without a dashboard login.
What it does now
The owner can hand out work with a voice note in Hinglish, Kannada-English or any mix. ChaseKaro works out who does what by when, checks with the owner, then sends and chases each task. Unanswered overdue items reach the owner, and a 6 PM summary shows what's stuck. Everyone on the team also gets private personal reminders and an 8 AM brief of their day.
Results
Around 60 action items tracked across 8 teams, across 3 locations
Where it transfers
DirectOwner-led businesses where instructions are given by voice: shops, clinics, agencies. Also recruitment agencies tracking next steps with shortlisted candidates, project sites, franchise networks
With tweaksTeams that live on WhatsApp: staff join from a link forwarded there, and the owner can close tasks for anyone who won't use Telegram
Sports academy · Fee collection
Fee collection at a sports academy.
Sound familiar?
Coaches doubled as fee collectors; roughly two-thirds of attending children weren't paying.
What was built
Self-registration with guardian WhatsApp capture, admin-only payment verification with UPI evidence, full audit log.
Where it transfers
DirectAny academy, tuition centre, clinic or club that collects recurring fees by UPI
Small & medium businesses · VoiceIn production
Customers call, many of them prefer not to speak English, and nobody follows up.
Sound familiar?
Calls go unanswered after hours. Enquiries sit in WhatsApp and nobody gets back to them. Callers who'd rather speak Hindi or their local language give up. Follow-ups depend on someone remembering to make them.
What was built
A multilingual voice agent embedded on the website of a children's sports academy I co-own. Callers speak in their own language. The assistant answers from the academy's knowledge base, qualifies the enquiry and books a trial class with all the required details. A coach then confirms the day, time and location on WhatsApp. The agent could do that step too; the academy keeps it human on purpose. It runs as a call from the website; a phone number can be added on request.
Results
70% of ~30 calls ended in a trial-class booking
The rest: too far away, deciding later, or testing the languages
Where it fits
DirectClinic appointments, coaching and training academies, real-estate lead follow-up
With tweaksCandidate follow-ups and first-round screening calls, payment reminders, service-visit scheduling
Demo call to the voice agent, answering from the academy's knowledge base. The caller is an AI voice (Google Gemini) playing a parent, switching languages to show the range. Real callers usually stick to one or two languages. The 70% result comes from real calls.
BengaliHindiKannadaMarathiPunjabiTamilTelugu
Manufacturing and warehouses · SafetyProof of concept
SafeSight: proof of concept, October 2026
Sound familiar?
Most plants have plenty of CCTV cameras and nobody watching them. Footage gets checked after an accident, not before.
What was built
AI safety monitoring on existing CCTV, built to run on one on-site laptop with no cloud. Tested against simulated CCTV footage of six scenarios and a public test image set. See SafeSight →
How we measured it
A 30-minute soak with 3 cameras. Detector accuracy on 90 public test images, with 95% confidence intervals. Every number is in the one-page results sheet.
Results
Scenario (simulated footage)
Expected
Actual
Workers without hard hats
Every violation alerted once
6 of 6 alerted, no duplicates (9 of 9 over 3 loops)
Compliant crews (bending, occlusion, backs of heads)
No alerts
0 alerts over 4 loops
Restricted area: walk-by, then intrusion
Walk-by ignored; intrusion alerted
Walk-by ignored; 1 critical alert, 1 email
New object by text ("ladder"), no training
Boxes, then an experimental alert
Boxes after 20 s, alert after 25 s
Camera streams cut, then restored
Reconnects without a restart
Every camera analysed again 7.8 s after the restart
Soft headwear (caps, beanies, a hood)
Flagged as no hard hat
Failed: 3 of 4 missed (hood caught). Fix: retrain with caps labelled correctly, the first item of every pilot.
Where it fits
DirectMid-size manufacturing plants, warehouses and logistics parks with fixed CCTV
With tweaksChemical, pharma and paint units (hazardous storage zones); construction sites (moving cameras, outdoor light)
SafeSight results measured in our proof of concept (October 2026) on simulated footage and a public test image set, on one Intel laptop. Results on your cameras are measured during a pilot.
Recognise one of these problems?
Send a short note on WhatsApp about the workflow that costs you the most time. I'll tell you honestly whether AI will move the number, and whether one of these patterns works as-is or needs changes.