Blueprint Studio
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What we build, by industry.
Pick your industry to see how each job is done today and what it looks like once it runs itself.
How it works today
Advice reaches farmers late and generic — a co-op agent or extension officer visits occasionally, guidance is one-size-fits-all, and selling decisions are made blind to where prices are heading. Smallholders lose yield to problems caught too late and lose income selling at the wrong moment.
The NeuralYug way
We stream weather, soil, market and pest data into an advisory model that gives each farmer one clear, specific tip in their own language on a basic phone (SMS/IVR), plus sell-now-or-wait price forecasts — with a co-op agent able to review advice before it goes out. It augments the agent, meets farmers on the phones they actually have, and keeps a human check in the loop.
Typical benefit · Our crop-advisory blueprint targets around a 25% yield increase and ~30% income improvement by turning late, generic advice into timely, farmer-specific guidance and better-timed sales.
How it works today
Teachers answer the same student questions repeatedly, grade stacks of assignments by hand, and students who get stuck after hours simply wait until the next class. Grading consistency drifts across a big cohort, and feedback arrives too late to actually help the student improve.
The NeuralYug way
We build a course assistant grounded in *your* materials that answers student questions 24/7 (and says "ask your teacher" when it's unsure rather than guessing), plus assisted grading for objective and structured work with a teacher review step. Teachers keep authority over marks and edge cases; the AI handles the repetitive Q&A and the first pass on grading.
Typical benefit · Grounded student Q&A deflects routine questions the way a support assistant does, and assisted grading turns hours of marking into a quick review — freeing teacher time for the students who need real help, with faster feedback loops for everyone.
How it works today
A new customer submits ID, address proof and supporting documents; a staff member eyeballs each one, retypes the fields, cross-checks them by hand and files everything. It's a bottleneck — manual verification often takes 18+ minutes per customer, delays account opening, and inconsistency between reviewers creates both fraud gaps and false rejections.
The NeuralYug way
We classify and read each document (citizenship, PAN, statements, salary slips), extract fields with a confidence score, cross-check them, and pass clean files straight through while routing anything low-confidence to a compliance officer. A person still owns the final risk decision — the AI clears the routine 70–80% and flags the rest, with a full audit trail.
Typical benefit · Digital verification benchmarks show manual time falling ~78% (18+ min → under a minute) and onboarding cost down 48–70%; our bank-KYC blueprint targets 70% faster KYC, ~99% accuracy on structured docs and 60–80% lower cost per verification.
How it works today
Loan officers gather documents, key in figures, and assess each file largely by manual review and rules of thumb, so decisions are slow, inconsistent between officers, and hard to audit. The document-gathering and verification alone can stretch a decision across days, and thin-file applicants get judged on gut feel.
The NeuralYug way
We automate the file assembly and document reading (statements, salary slips, KYC), surface a consistent, explainable risk view to the officer, and route clean files through faster — while every low-confidence or edge case goes to a human. Crucially, this is decision *support*: the credit decision, the scorecard weights and the final approval stay with your team and your regulator's rules.
Typical benefit · The verification and document layer benchmarks like KYC — up to ~70% faster file handling and 60–80% lower cost per verification — so officers spend their time on judgement, not paperwork, and decisions become consistent and auditable.
How it works today
Monitoring runs on static if-then rules ("flag anything over X"). Fraud rings simply learn the thresholds and slip under them, while ordinary customers keep tripping the same crude rules — so analysts drown in false positives, review queues back up, and genuinely novel fraud gets through because a fixed rule can't see a new pattern.
The NeuralYug way
We stream every transaction through a real-time scorer that combines velocity, device fingerprint and behavioural patterns with ML trained on confirmed fraud, run sanctions/PEP checks in parallel, and surface a ranked, explainable alert to an analyst. A person still makes the call and files the SAR; the model just catches more, with fewer false alarms, and explains why it flagged.
Typical benefit · ML monitoring commonly cuts false positives 50–80% versus rule-based systems while catching more real fraud; our remittance blueprint targets ~60% fewer false positives, sub-300ms decisions and roughly 2x the fraud caught.
How it works today
Compliance teams manually track regulatory changes, clear alerts one by one, and assemble periodic reports by hand — a mid-size team can burn 20–30% of its capacity just monitoring rule changes. It's expensive, reactive, and audit prep becomes a fire drill every reporting cycle.
The NeuralYug way
We automate the grind: ML to cut alert false positives, NLP that parses dense regulatory updates and maps them to your internal obligations, and pipelines that assemble reports with a tamper-evident audit trail. Analysts stay in charge of every filing and judgement — the system does the monitoring, drafting and triage so they focus on the real risks.
Typical benefit · ML monitoring benchmarks at 50–80% fewer false positives with equal-or-better detection, and regulatory-change mapping compresses weeks of manual reading into minutes — shifting compliance from periodic scramble toward continuous, auditable readiness.
How it works today
A clerk opens each invoice (PDF, scan, email), keys the amount, date, vendor and line items into the accounting system, then hunts for the matching PO and payment to reconcile it. It's slow and error-prone — manual processing commonly runs $12–$26 per invoice and 17+ days from receipt to payment, and the errors surface late at month-end close.
The NeuralYug way
We build a document-AI pipeline that reads each invoice (amount, date, party, invoice #) with a confidence score, auto-matches it to the right PO/ledger entry, passes clean matches straight through, and routes only the genuine edge cases to a person — learning from every correction. The human sees exceptions, not the whole stack; the AI never posts a low-confidence match silently.
Typical benefit · Automated processing benchmarks at roughly $2–$5 per invoice (up to ~80% lower for high volume) with cycle time dropping from ~17 days to ~3. Our own reconciliation blueprint targets 40–60% of manual effort removed and ~10 hrs/week saved.
How it works today
The monthly report takes days to build. Someone exports numbers from the accounting system, the sales sheet and two other spreadsheets, pastes them together, fixes the formatting, and chases whoever owns the one figure that never matches. Several 'final' versions circulate, nobody is certain which is current, and by the time leadership reads it the numbers describe a month that has already ended.
The NeuralYug way
We join those sources once, agree what each number actually means and where it comes from, and model the handful of metrics decisions get made on. Then it serves as one live view that is always current, with the definitions written down — so the monthly report stops being a job somebody does and becomes a page somebody opens.
Typical benefit · Teams commonly lose around 5.5 hours per person per week to spreadsheet version-control errors, and Bain & Company finds organisations with advanced analytics are 5x more likely to make faster decisions. The reporting cycle itself typically moves from days to minutes once the numbers are modelled once and served live rather than rebuilt by hand.
How it works today
Citizens queue at offices or call during working hours to ask routine questions ("what documents do I need", "where do I apply") and staff answer the same things all day. Answers vary by who you ask, records of what was said are thin, and access to information depends on being there in person during office hours.
The NeuralYug way
We build a citizen-services assistant grounded on vetted policies and datasets, with an access check before retrieval, citations on every answer, PII redaction and a tamper-evident audit log — and sensitive cases escalate to a named official. Governance is the point: every answer is traceable, and the system only speaks from approved sources, never guesses.
Typical benefit · Our e-gov blueprint targets up to 80% faster answers, ~70% of tier-1 enquiries automatable, and 100% of interactions logged and traceable — giving citizens round-the-clock, consistent answers while keeping full accountability.
How it works today
Every quotation starts from the last similar one. Someone digs out an old file, re-checks which prices have moved, retypes the specification and the standard terms, then waits for a manager to confirm the margin. Tenders are worse — the same company profile, certificates and past-work annexes get reassembled by hand for each submission. Quotes go out days later, and often enough with a stale rate somewhere in them.
The NeuralYug way
We turn your price list, rate card and standard terms into one quoting system: choose the line items, and it prices them at current rates, applies your margin and approval rules, and produces the document in your own format. Tender packs draw from a maintained library of profiles, certificates and past-work sections, so assembling a submission is selection rather than retyping.
Typical benefit · Speed is the part that is actually measurable. A Harvard Business Review study of 2,241 US companies found firms that responded within an hour were 7x more likely to reach a decision-maker than those that waited an hour longer, and sales research commonly attributes 35-50% of deals to whichever vendor responds first. CPQ vendors also report quote-preparation time falling by roughly three quarters — treat that figure as vendor-published rather than independent.
How it works today
For a popular role, a recruiter reads hundreds of CVs by hand to build a shortlist — manual screening averages around 23 hours per hire for high-volume roles — while HR simultaneously answers the same leave, payroll and policy questions over and over. Both are slow, inconsistent, and pull skilled people away from actual interviewing and people work.
The NeuralYug way
We build a screening assistant that ranks and shortlists against the *job's real criteria* (with the recruiter reviewing and overriding — never an opaque auto-reject), plus a RAG helpdesk grounded in your handbook that answers routine employee questions instantly. Humans make every hire/no-hire call; the AI does the reading and the FAQ.
Typical benefit · AI screening cuts initial review time ~70–75% (hours to minutes) and end-to-end time-to-hire by 25–50% when applied across sourcing, screening and scheduling; the employee-helpdesk side deflects the repetitive policy questions the same way support tickets get deflected.
How it works today
Front-desk staff handle intake, phone triage, appointment booking and paper/scattered records by hand. Patients wait on hold, no-shows go unmanaged, and clinicians lose time hunting for information across systems that don't talk to each other.
The NeuralYug way
We automate the non-clinical layer — self-service booking with reminders, intake forms that populate records, and a grounded assistant that answers routine "how/where/when" questions and routes anything clinical to staff. We deliberately do **not** build autonomous diagnosis: symptom questions get safe, general information and a clear "please speak to a clinician" handoff.
Typical benefit · The scheduling layer benchmarks at ~30% fewer no-shows and 40–60% of routine scheduling calls self-served; freeing front-desk and clinician time for care rather than admin.
How it works today
Orders and reservations are taken by phone or in person, so staff are tied up during rushes, calls get missed, and mistakes creep into hand-written orders. After-hours enquiries and out-of-language guests simply go unanswered, and the venue loses covers it could have filled.
The NeuralYug way
We build self-service ordering and reservations plus a multilingual concierge assistant that answers menu, availability and "what's good here" questions from your real data and passes orders/bookings cleanly to the kitchen or team. Staff focus on service in the room; the AI handles the phone-tag, the after-hours enquiries and the language barrier.
Typical benefit · A multilingual concierge/ordering layer commonly handles the majority of routine enquiries without a human and captures after-hours and non-native-language demand that used to be lost — our tourism concierge blueprint targets up to a 30% booking-conversion lift with 68% of routine chats handled without a human, 24/7.
How it works today
Dispatch runs on spreadsheets, phone calls and a planner's mental map; routes are fixed in the morning and can't adapt when traffic, a breakdown or a new order hits. Customers chase order status by phone because there's no live tracking, and manual re-planning of an exception can take 15–20 minutes each time.
The NeuralYug way
We build the operations platform — automated route optimisation that rebalances as conditions change, live order tracking and status notifications, and exception handling that proposes a fix in seconds instead of a phone-tree scramble. Dispatchers keep override control; the system removes the spreadsheet juggling and the "where's my order" calls.
Typical benefit · AI routing benchmarks at 10–20% fuel savings and 15–25% more stops per driver per shift, with exception re-planning dropping from ~15–20 minutes to under two. Our Kathmandu logistics blueprint delivered 58% less manual processing, 3x faster order turnaround and zero lost orders since launch.
How it works today
Human inspectors eyeball products on the line or in the field and mark pass/fail. Even the best inspectors catch only about 80% of defects, and accuracy falls off fast with fatigue — human performance can drop from ~90% to under 60% after a few hours — while manual inspection caps line speed and misses sub-millimetre defects entirely.
The NeuralYug way
We build a computer-vision inspection system trained on your good/defect examples that flags defects in real time, holding accuracy steady across full shifts, and routes uncertain items to a human. The inspector shifts from checking everything to adjudicating the flagged cases — the AI handles the tireless, consistent detection.
Typical benefit · Modern vision systems benchmark at 97–99.5% detection accuracy versus 60–80% for human inspectors, hold that accuracy across three shifts, and run several times faster than a manual line — turning inspection from a fatigue-limited bottleneck into consistent, documented quality.
How it works today
Maintenance is either reactive (fix it after it breaks, with costly unplanned downtime) or calendar-based (service on a fixed schedule whether the machine needs it or not). Both waste money — reactive means surprise line stoppages and rush repairs; scheduled means replacing parts with plenty of life left and still missing failures that don't follow the calendar.
The NeuralYug way
We connect IoT sensor data to ML models (random-forest, LSTM) that learn each machine's normal signature and predict failures before they happen, with clear alerts and confidence, so maintenance is planned around actual condition. Your team decides when to act; the system gives them the early warning and the reason, not a black-box "trust me".
Typical benefit · Predictive maintenance benchmarks at up to 50% less unplanned downtime, ~25% lower maintenance cost and 20–40% longer equipment life, with failure-prediction accuracy commonly above 90% — turning surprise stoppages into scheduled, cheaper interventions.
How it works today
A small marketing team writes every blog, product description and social post from a blank page, hand-crafts meta tags and keyword research, and publishing slows to a trickle. Output can't keep pace with the channels that need feeding, and SEO tasks like clustering keywords and writing descriptions eat hours that could go to strategy.
The NeuralYug way
We build an AI-assisted content workflow — drafting, meta descriptions, keyword clustering and repurposing — tuned to your brand voice, with a mandatory human edit before anything ships. We're honest that AI-generated slop hurts a brand; the value is speed on the mechanical work while your team keeps the judgement, facts and voice. We also build for AEO (answer-engine optimisation) so you get cited, not just listed.
Typical benefit · Marketing teams using AI report roughly 44% higher productivity and 5–15 hours/week saved on drafting, meta and keyword tasks, with faster editing cycles — provided a human reviews every piece (the vast majority of teams do). The win is throughput on the mechanical work, not replacing the writer.
How it works today
Inbound enquiries land in an inbox or a form and wait — the B2B average first response is measured in dozens of hours, and studies find most companies never reply at all. Meanwhile the CRM rots with duplicates, blank fields and stale deals, so reps waste time on admin and the hot leads have already gone cold.
The NeuralYug way
We wire an automation that instantly acknowledges and routes every new lead, drafts a personalised first reply, and enriches the CRM record — plus AI lead-scoring that ranks who's worth calling first, and a hygiene job that de-dupes and flags stale deals. A rep still runs the conversation; the AI just makes sure nobody waits hours and the data stays clean.
Typical benefit · Contacting a lead within five minutes benchmarks at roughly a 32% close rate versus ~12% after a day, and sub-60-second follow-up can lift connect rates several-fold; AI routing commonly speeds first response ~8x. The realistic win is fewer leaks, not magic — you stop losing the leads you already paid for.
How it works today
Support runs on a shared inbox or a helpdesk queue where agents read every ticket, guess the category, and re-answer the same handful of questions all day. Tier-1 questions ("where's my order", "how do I reset this") eat most of the capacity, response times slip during spikes, and a fully-loaded agent-handled ticket typically costs somewhere between $6 and $15 once salary, tooling and overhead are counted.
The NeuralYug way
We put a RAG assistant in front of the queue that answers routine questions from *your* real help content and policies (so it can't invent answers), auto-tags and routes the rest to the right person, and drafts a suggested reply the agent can approve in one click. Anything low-confidence or sensitive escalates to a human with the full context attached — the AI handles the boring 40%, not the judgement calls.
Typical benefit · Median tier-1 deflection across mature programs lands around 40%, with well-tuned setups reaching the high 50s; an AI-resolved contact commonly costs well under $2 versus $6–15 for a human-handled one. Realistic net cost reduction across the whole support org, after infra and the complex long tail, is 20–35% in year one — not the 90% vendors headline.
How it works today
Someone retypes data off scans, PDFs and paper forms into a system all day. It's the definition of low-value work — slow, monotonous, and quietly error-prone (a mistyped digit surfaces as a wrong payment or a broken record weeks later), and it doesn't scale when volume spikes.
The NeuralYug way
We build an OCR + document-understanding pipeline that reads structured and semi-structured documents, extracts the fields with a confidence score, validates them against your rules, and posts clean data straight through — routing only the unclear ones to a person. The reviewer handles exceptions instead of typing everything; the AI never posts a low-confidence field unchecked.
Typical benefit · For structured documents, extraction commonly reaches ~99% field accuracy and removes 40–60% of the manual effort, turning a full-time typing task into a short daily exception review. The hours freed go back to work that needs a human.
How it works today
Bookings happen by phone or DM during office hours, staff play calendar Tetris by hand, and after-hours requests are simply lost. No-shows quietly drain revenue because reminders are manual and inconsistent, and double-bookings creep in whenever two people touch the same calendar.
The NeuralYug way
We build self-service booking with an assistant that handles availability, reschedules in one tap, and sends automated multi-touch reminders — optionally with a predictive no-show score so high-risk slots get an extra nudge or a waitlist backfill. Staff keep control of the rules and the calendar; the automation just removes the phone-tag and the forgotten reminders.
Typical benefit · Automated reminders benchmark at roughly a 30–34% no-show reduction (higher with predictive scoring), plus 8–12% of bookings captured after hours and 40–60% of scheduling calls deflected to self-service.
How it works today
A lawyer or ops person reads each contract line by line, checks it against the company's standard positions from memory, and marks up redlines by hand. It's slow and doesn't scale — review cycles stretch for days, consistency depends on who's reviewing, and outside-counsel spend climbs whenever volume spikes.
The NeuralYug way
We build a playbook-driven review assistant that reads each contract, flags deviations from *your* standard positions, and drafts suggested redlines with the reasoning — so the reviewer starts from a marked-up draft instead of a blank one. A lawyer approves every change; the AI accelerates the first pass and catches the routine issues, it doesn't sign anything.
Typical benefit · AI contract review benchmarks at 45–90% faster review cycles while keeping 85%+ acceptance on standard clauses, with lawyers commonly reporting double-digit hours saved per week and reduced outside-counsel spend.
How it works today
Property enquiries, viewing requests and tenant maintenance issues come in by phone and message and get handled manually, so responses lag and leads to other agents. Listings are updated by hand across portals, and tenant requests get lost in an inbox, hurting both conversion and tenant satisfaction.
The NeuralYug way
We build a property assistant that answers listing and availability questions from your real inventory, books viewings, and logs tenant maintenance requests into a tracked workflow — plus automation to keep listings and status in sync. Agents keep the relationships and negotiations; the AI handles the instant first response and the repetitive coordination.
Typical benefit · The same speed-to-lead dynamics apply — instant response versus hours of delay meaningfully lifts enquiry-to-viewing conversion — while automated maintenance intake and status updates cut the tenant "did anyone see my request?" churn. It's the enquiry and coordination layer done reliably, 24/7.
How it works today
A phone line staffed by agents (often behind a clunky IVR menu) handles every call, so callers wait on hold during peaks, after-hours calls go to voicemail, and a fully-loaded human call commonly costs several dollars each. Agents spend much of their day on repetitive, containable questions that never needed a specialist.
The NeuralYug way
We build a voice agent that handles the routine, containable calls (status, FAQs, simple changes) in natural language and warm-transfers anything complex to a human with context. It's honest about being an assistant and hands off cleanly — the goal is to free agents for the calls that actually need them, not to trap callers in a bot.
Typical benefit · Well-tuned voice agents benchmark at 40–70% containment on suitable call types, with an automated call costing roughly $0.30–$0.55 versus several dollars for a human — a ~80–90% cost reduction on the contained portion, and a realistic 30–50% net cost reduction across the operation in year one.
How it works today
Company knowledge is scattered across drives, wikis, chat threads and people's heads, so staff spend a big slice of every day hunting for information or waiting on a colleague to reply. Studies put it at roughly 20–30% of the workday lost to searching — a 1,000-person firm can quietly lose millions a year — and new hires take months to become self-sufficient.
The NeuralYug way
We build a RAG assistant (a "context layer") over your real documents and systems that answers staff questions with citations back to the source, so people ask in plain language instead of digging — with access controls so everyone only sees what they're allowed to. It answers from *your* data, cites where each answer came from, and says "I don't have that" rather than inventing.
Typical benefit · Organisations deploying RAG commonly report information-search time down 60–80%, turning hours of hunting into seconds of asking and getting new hires productive far faster. A joined-up context layer is also a durable edge — one published benchmark rose from 16.7% to 54.2% accuracy with a knowledge-graph approach.
How it works today
Ordering runs on last-year's numbers plus a manager's gut feel in a spreadsheet. The result is the classic double bind — shelves go empty on the fast movers (lost sales) while cash sits frozen in slow stock and expiry write-offs, because a static spreadsheet can't weigh seasonality, weather, promotions and local events together.
The NeuralYug way
We train demand models (gradient-boosted trees, and LSTM/temporal models where the history warrants) on your sales, seasonality, promotions and external signals, then feed the forecast into clear reorder suggestions a buyer approves. It's decision support, not autopilot — the buyer keeps the final call, and the model states its confidence per SKU.
Typical benefit · ML forecasting commonly reaches 75–90% accuracy and cuts stockouts meaningfully — benchmarks range from ~18% in year one to 30%+ with mature setups, with matching drops in waste and overstock. One published fresh-produce case lifted forecast accuracy from 24% to 76% and in-stock rates from 80% to 90%.
Before you send it
Is this binding?
No. Your answers only tell us the shape of the work so the right person replies with a useful blueprint. Nothing is billed before a written scope you have agreed to.
What if I only have a rough idea?
That's completely normal — most enquiries start there. Describe the problem in your own words and we'll turn it into a plan, or tell you plainly if it isn't a fit for us.
How much does a project cost?
We publish clear starting prices: a Starter project from Rs 15,000, a custom Website Design from Rs 1 lakh, a two-week Automation Sprint from Rs 45,000, and full builds from Rs 1.5 lakh. All exclude 13% VAT.
How fast will you reply?
Within one business day, from a senior engineer rather than a sales rep. Kathmandu, GMT+5:45.