Custom AI for Indian businesses that has to survive a regulator's question and a board review. We build LLM applications, autonomous agents, computer vision and predictive models into the systems your team already runs, host them in Indian regions when the data cannot leave, and work IST hours from first call to handover.
Trusted by teams across India, the Gulf and worldwide
Six AI Service Lines Built for Indian Businesses
Indian businesses sit on claims files, contracts, patient records, product catalogues and call recordings that people still work through by hand. Six lines cover the job end to end, from the first prototype scored on your own data to a model your team runs without us, with the data protection paperwork written as we go.
Our Services
01 · Service line
LLM & Generative AI Applications
Answers pulled from your own circulars and contracts, with the source shown
Drafting, summarising and Q&A inside Microsoft 365 or Google Workspace
Evaluation sets and hallucination testing before anyone depends on it
Token cost tuned per workload and reviewed monthly, in rupees
GPT, Claude, Gemini, Llama and Mistral, picked per task on a benchmark
A demo that impresses a board is the easy part. The return shows up in the working day afterwards, so here is where custom AI genuinely pays for itself in an Indian business.
Hours back, every week
The work that quietly eats an Indian team is reading, sorting and re-keying. The model takes the first pass and your people handle the exceptions, instead of two juniors working the whole queue.
Email, WhatsApp and scanned PDFs read, classified and routed
Drafts prepared for review rather than typed from scratch
Your people approve, the model does the keying
Decisions on evidence, not instinct
Forecasting, scoring and anomaly detection turn the history already sitting in Tally, your CRM or your warehouse into a number someone can act on this week.
Cash-flow and demand forecasts out of your own ledger
Lead and collections scores that rank honestly
Anomalies flagged before the GST filing, not after
Systems that survive Monday morning
A pilot that works on one laptop and a system that holds at 10am on a Monday are different builds, and we design for the second one from day one.
A test set that says how good it actually is
Drift watched and retraining scheduled
Spend in rupees and latency tracked per workload
A service customers notice
Answers in seconds instead of a queue nobody clears, and personalisation built on what a customer actually did rather than the segment they were dropped into.
Customers answered at 11pm without a night shift
Recommendations built on what people actually bought
Service tiers a competitor cannot copy in a quarter
AI That Earns Its Keep Inside the Working Day
A chatbot bolted to the website is not a transformation programme. These six disciplines are how we turn the data an Indian business already holds into decisions it can defend, with our AI development and cloud teams behind them. If you want agents that act rather than answer, start at AI agent development.
[1]Retrieval-Augmented Generation
Every answer comes from your own policies, contracts and records and arrives with its source attached, so the model cannot invent a clause or a price that was never written down.
[2]Autonomous AI Agents
An agent works a task step by step, calling Tally, Odoo, your CRM or your helpdesk as it goes, and stops for a named person to approve anything that moves money or leaves the building.
[3]Predictive Modelling
Forecasts, scores and rankings trained on your own history, always measured against the simplest rule your finance head would have used instead, so the uplift is a number and not a claim.
[4]Computer Vision
Reading photographs, scans, drawings and video the way your best inspector does, running on site when the connection is poor or the answer is needed in seconds.
[5]Conversational & Voice AI
Chat and telephone assistants that hold a natural conversation in English, Hindi or a regional language, answer only from what your knowledge base actually says, and pass the conversation to a colleague with the context intact.
[6]MLOps & Evaluation
Prompt and model versions tracked, drift caught before a customer notices, retraining on a schedule and a rupee cost dashboard, so nobody has to guess whether the system got worse this month.
Technologies
The AI Stack We Build On for Indian Teams
Seven techniques that separate a model that sounds convincing from one an Indian regulator, auditor or customer can rely on. Pick one to see where it sits in a build.
Large Language Models
GPT, Claude, Gemini, Llama and Mistral are all on the bench. Which one wins is settled by a benchmark on your own tasks rather than by a press release. Often a smaller model clears the bar at a tenth of the cost per call, and we say so when it does.
Retrieval & Vector Search
Circulars, policies, tickets and internal wikis indexed by meaning rather than keyword, so the model is handed the passages that matter and cites them back. We build on pgvector, Pinecone or OpenSearch depending on where your data already sits, not where a vendor would like it to.
AI Governance & Guardrails
Filters on what goes in and what comes back, personal data redacted before it reaches any third-party API, refusals handled gracefully and every prompt and response logged. Under the DPDP Act and the Data Protection Board's guidance that log is how you answer a subject access request without guessing.
Fine-Tuning & Distillation
Fine-tuning on your labelled examples, or distilling a large model into a small one that runs cheaply in a Indian region, comes last on our list rather than first. Retrieval and better prompting usually get there for less, so those are exhausted before anyone touches weights.
Evaluation & Observability
A test set held back from the build, scored automatically, with a regression check on every prompt or model change. It turns whether the system improved into a number on a dashboard rather than an argument about how last week felt.
Vision & Multimodal Models
Models that read photographs, video and scanned paper as readily as typed text. In Indian businesses that tends to mean claims photographs, site drawings, shelf footage and the paper forms nobody has time to key in.
Private & Self-Hosted Deployment
Open-weight models running inside your own VPC in Azure Central India or AWS Mumbai, so regulated data never leaves the region. The right choice for hospital, RBI-regulated and public-sector work, and for anyone whose data processing agreement says so.
Thinking About AI? Begin With a Free Use-Case Audit.
One call about your data, your queues and what you are actually trying to change tells you which use case is worth funding first, what it will realistically cost in rupees and where AI is the wrong tool. So the first line of the budget goes somewhere that pays back.
Free · No obligation · Delivered within 48 hours, IST
Industries
AI Built for Indian Industries
Nine sectors where Indian businesses are already putting models to work, from RBI-regulated lenders to hospital groups on ABDM. See our fintech and healthcare practices for more, or enterprise software for when the AI has to reach the rest of the business.
[01]Financial Services & FinTech
Underwriting, collections and fraud work where an RBI inspection will read the audit trail and ask for the explanation.
Delivered since 2018 across AI, mobile, cloud and enterprise builds.
70%
Front-desk call load removed
Reduction in calls reaching the front desk after the healthcare voice agent we built went live, answering around the clock.
78%
Queries resolved automatically
Share of customer questions the WhatsApp assistant we built for an e-commerce client resolves without a person.
15+
Countries served
Client operations running on Appther-built platforms worldwide, including India and the Gulf.
Case Studies
Work We've Shipped
Three AI builds we can point at: a voice agent answering every patient call, a WhatsApp assistant carrying frontline support, and a routing engine trained on live fleet data.
Why Indian Teams Choose Appther for Custom AI Development?
Any agency here can demo a chatbot in a week. Far fewer will push your own data through a test set in week one, say plainly what a model can and cannot do, and put the data protection paperwork in the plan rather than the appendix. We build the whole stack in house from Noida, with our AI development and cloud infrastructure teams on the same roadmap.
01
One team, whole stack
Pipeline, retrieval, model, interface and the APIs your other systems call are built by one team in Noida on one roadmap, so when an answer goes wrong there is nobody to point at but us.
02
Measured, not demoed
Nothing ships without a held-back test set and a plain baseline it has to beat. When the simple rule does as well as the model, you hear it from us before you pay for the model.
03
Compliance in the plan, not the annexure
Consent and notice under the DPDP Act, a data protection impact assessment where one is needed, India-region hosting and an audit log are scoped in discovery, so your data officer signs off before the build rather than after it, backed by our AI engineering practice.
04
Fixed scope, fixed price in rupees
Discovery ends with a written scope and a fixed rupee quote, before a sprint starts. You own all of it: code, prompts, fine-tuned weights and documentation, in your own repository, with an NDA signed before discovery.
Security & Compliance
What Happens to Your Data Inside an AI Build
Training and prompting put your data somewhere new, and in India that carries obligations to the Data Protection Board, to your customers and often to a sector regulator. Here is how each category is handled, limited to what we can honestly claim.
Personal data turns up in prompts and training sets far more often than teams expect, and under the DPDP Act that exposure is yours. We start from minimisation, so the DPDP Act compliance is a property of how the system is built rather than something bolted on before launch.
the DPDP Act & DPA 2018Consent mapped per feature, a DPIA where automated decisions affect people, retention rules enforced in code and the CERT-In six-hour incident report rehearsed, not just documented.
CERT-In DirectionsWhere you serve EU customers, transfer mechanisms and EU representative obligations are covered, and the model behaves the same on both sides of the Channel.
MeitY AI advisoriesSystems classified by risk tier, with the transparency, logging and human-oversight obligations for your tier documented before the build starts.
Data minimisationAadhaar, PAN and account numbers are redacted before the call. The model is sent the least data that still answers the question, which cuts risk and token cost together.
The weakest route into a model is the one that ends up being used. Identity, transport, secrets and prompt handling are built as one system rather than three features added each somebody else's job.
Tenant isolationEvery tenant gets its own keys and its own namespace in the vector store, so a retrieval query for one customer cannot surface another customer's documents, by construction rather than by policy.
Encrypted transportEvery call to a model travels over TLS, secrets live in a managed vault, and nothing in a prompt or response is written to a log outside your tenancy or outside an Indian region.
Prompt-injection defenceAnything a user uploads, or a website hands back, is treated as data and never as an instruction, and every tool call is checked against a schema so a booby-trapped PDF cannot act as if it were staff.
Least-privilege toolsAn agent is given the smallest set of permissions that still gets the job done. Moving money, messaging a customer or deleting records all wait for a named person.
These are the engineering practices we apply on every India project. Appther is ISO/IEC 27001 and ISO 9001:2015 certified, and the same practices are aligned with SOC 2.
OWASP Top 10 for LLMsEach release is checked against the OWASP list for LLM applications, from prompt injection and insecure output handling through to excessive agency.
ISO/IEC 27001 certifiedAccess control, change management and incident response run under Appther's ISO/IEC 27001-certified information security management system, whether or not your organisation holds the certificate yet.
Secure coding & reviewNothing merges without a second engineer reading it. Static analysis and dependency scanning run in CI, and the repository holds no secrets.
Penetration testingFor regulated work an independent tester is booked before launch, and every finding is fixed and retested before anything is disclosed.
Appther's ISO certifications cover how we work; they do not certify your product. ISO 27001, SOC 2 or ABDM attestation of your finished product sits with your compliance team and its assessor. Our job is to build and document it so the assessment is a short one.
In a regulated sector the rule changes the architecture, not only the paperwork around it. Those constraints are scoped in discovery so none of them arrives in month four as a surprise.
ABDM-ready buildsFor hospital and clinical workloads: ABDM alignment, clinical safety documentation, patient data kept in Indian regions and no health record sent to an endpoint outside your agreement.
RBI fair practicesDecisions that affect a customer's outcome are logged with their inputs, so you can explain to the RBI, SEBI or IRDAI what the model recommended and why.
PCI DSS scope reductionCard data is redacted before it reaches any model, so the AI layer stays out of scope and your assessor's job stays small.
India data residencyAzure Central India, AWS Mumbai or in-tenancy hosting for prompts, embeddings and logs, documented for your data officer and any regulator who asks. Payment data stays in India, as the RBI requires.
AI products lose trust and fail a public-sector review for predictable reasons. We build against the rules that catch AI features out specifically, so yours clears review the first time.
Disclosure of AI useUsers are told when they are dealing with a model and can reach a person without having to argue with it first, which is what the DPDP Act and the RBI both expect to see.
Human escalation pathsEvery assistant we ship has a written route to a person on IST, and the model is built to take it when confidence drops rather than bluff its way through.
WCAG 2.2 AAContrast, focus order and screen-reader behaviour are tested against WCAG 2.2 AA, and against GIGW for public sector Bodies Accessibility Regulations where they apply.
Failure statesA model that is unsure says so and shows where its answer came from, rather than answering with a confidence it has not earned.
Grouped by the layer each one belongs to, from the model that answers to the pipeline that keeps it honest. Every one of them can run inside an Indian region when your data requires it.
OpenAI
Anthropic Claude
Google Gemini
Meta Llama
Mistral
DeepSeek
Qwen
Hugging Face
LangChain
LangGraph
CrewAI
PyTorch
TensorFlow
FastAPI
Python
Node.js
pgvector
Milvus
Qdrant
Redis
MongoDB
TimescaleDB
InfluxDB
Grafana
AWS Bedrock
Azure OpenAI
Google Vertex AI
Docker
Kubernetes
Terraform
React
Twilio
Feature Set
What Ships in an AI Build Here
The building blocks that turn up in most AI platforms we deliver for Indian teams, from the first retrieval query through to production monitoring.
Document Q&A Over Your Own Data
Autonomous Task Agents
Scheduled Model Retraining
Streaming Responses & Live Transcription
Forecasting & Churn Prediction
Model Quality & Cost Dashboards
End-to-End Encryption
Per-Tenant Isolation & Access Control
Multi-Model Routing (GPT, Claude, Gemini, Llama)
ERP, CRM & Helpdesk Integrations
Role-Based Access Control
Alerts & Human Escalation
Prompt & Model Versioning
Computer Vision & OCR
Evaluation & Drift Analytics
Token & Inference Cost Tuning
Architecture
AI Architecture, Layer by Layer
A production AI system is ten layers, and each has to agree with the ones either side of it. A mistake in one surfaces somewhere else, usually as an answer nobody trusts, so we design all ten together rather than one after another.
Data Sources
Where the answers actually live: Tally or the ERP, the document store, the warehouse, the ticket history and the folder on the shared drive that everyone still uses.
01
Ingestion & Cleaning
Parsing, de-duplication and chunking, including the scans. An AI project that disappoints is usually a data problem wearing a model problem's clothes.
02
Embeddings
Text, images and audio turned into vectors, so what reaches the model is chosen by meaning rather than by matching keywords.
03
Vector Store
pgvector, Qdrant or Milvus, sized to your corpus, hosted in an Indian region and partitioned by tenant, so one customer's retrieval can never wander into another's documents.
04
Retrieval
Hybrid search and re-ranking that put the right passage in front of the model, with the citation carried all the way through to the answer.
05
Model Layer
The model itself, hosted or self-run in an Indian region, picked for each task on cost, latency and accuracy rather than on reputation.
06
Orchestration
Prompts, tools, memory and multi-step plans. This is where an agent decides what to do next and which of your systems it is permitted to touch.
07
Guardrails
Filtering in and out, Aadhaar and PAN redacted, refusals handled, and an audit log that lets you answer a subject access request truthfully.
08
APIs & Integration
The answer delivered into Zoho, Salesforce, Odoo, the helpdesk or your own product, because a model that lives behind a separate login is opened twice and then forgotten.
09
Evaluation & Monitoring
Held-back test sets, drift detection and a rupee cost dashboard, so quality stays a number you can watch rather than a feeling you have.
10
Data Sources
Where the answers actually live: Tally or the ERP, the document store, the warehouse, the ticket history and the folder on the shared drive that everyone still uses.
01
Ingestion & Cleaning
Parsing, de-duplication and chunking, including the scans. An AI project that disappoints is usually a data problem wearing a model problem's clothes.
02
Embeddings
Text, images and audio turned into vectors, so what reaches the model is chosen by meaning rather than by matching keywords.
03
Vector Store
pgvector, Qdrant or Milvus, sized to your corpus, hosted in an Indian region and partitioned by tenant, so one customer's retrieval can never wander into another's documents.
04
Retrieval
Hybrid search and re-ranking that put the right passage in front of the model, with the citation carried all the way through to the answer.
05
Model Layer
The model itself, hosted or self-run in an Indian region, picked for each task on cost, latency and accuracy rather than on reputation.
06
Orchestration
Prompts, tools, memory and multi-step plans. This is where an agent decides what to do next and which of your systems it is permitted to touch.
07
Guardrails
Filtering in and out, Aadhaar and PAN redacted, refusals handled, and an audit log that lets you answer a subject access request truthfully.
08
APIs & Integration
The answer delivered into Zoho, Salesforce, Odoo, the helpdesk or your own product, because a model that lives behind a separate login is opened twice and then forgotten.
09
Evaluation & Monitoring
Held-back test sets, drift detection and a rupee cost dashboard, so quality stays a number you can watch rather than a feeling you have.
Your data, your latency budget and the accuracy you actually need decide which of these fits, not what is trending this quarter. Our AI agent development and AI development services pages cover the layers on either side of the model.
Our Process
How We Deliver AI Projects in India
From the first call to a system your own team runs, the work goes through thirteen stages. Model work punishes guesswork, so the risky parts get proven on your own data early, the DPDP file grows alongside the build, and every stage ends with a result you can measure.
Week 1
Discovery
The use case, its constraints and what success actually means, settled before anyone names a model or a platform.
1
What we doWe sit with the people doing the work today, trace where the hours actually go, and check whether your data can carry the use case at all. Existing tools, contracts and processor agreements get read here rather than discovered in month three.
What you getA written problem statement, the one metric that decides success, and a shortlist ranked by return rather than by novelty.
Week 1–2
Business Case
What the AI has to be worth, in rupees, before it is worth building at all.
2
What we doWe put a rupee figure on the hours or the revenue it is meant to produce, size the token and infrastructure spend at launch and at scale, and agree the number we will be judged against.
What you getA business case with the arithmetic shown, and a scope you can hold us to in writing.
Week 2–3
Data Readiness
An honest look at whether your data can carry the use case.
3
What we doCoverage, labelling, freshness and access rights are audited across every system involved, including what is still only in spreadsheets, and we identify what must be cleaned, joined or collected before a model can be trusted. The consent and purpose for each source is confirmed here, not assumed.
What you getA data assessment, a cleaning plan and a plain statement of what to build first.
Week 3–5
Prototype & Evaluation
A working prototype scored against a baseline, rather than a demo video.
4
What we doWe build the simplest thing that might work, usually retrieval and a good prompt, and score it on a held-back set from your own data against a plain rules-based baseline. Sometimes the baseline wins, and you hear that from us.
What you getA prototype running on your own data, a measured accuracy figure, and a go or no-go you can act on.
Week 4–7
Model Selection
The model chosen on cost, latency and accuracy for your tasks, not on its reputation.
5
What we doCandidate models, hosted and open-weight, are benchmarked on the tasks from stage four, including the code-mixed ones. We weigh residency, per-token cost at your volume and the price of being wrong, and trial self-hosting in a Indian region where the data demands it.
What you getA documented model decision with the figures behind it, including the rupee cost per thousand requests you should expect.
Week 5–10
Retrieval & Data Pipeline
The plumbing that decides what the model is ever allowed to see.
6
What we doChunking, embedding, hybrid search and re-ranking are built over your own document sources, partitioned by tenant so one customer's retrieval can never reach another's records.
What you getA retrieval layer that cites its sources, an ingestion pipeline your own team can re-run, and a corpus you can grow without us.
Week 7–13
Orchestration & Agents
Prompts, tools and multi-step plans wired into your real systems.
7
What we doWe agree which tools the agent may call, which actions need a named person's sign-off, and how memory and context are managed, then connect it to your CRM, helpdesk or ERP through supported APIs.
What you getWorking agent flows with approval gates, and an audit log of everything the agent has done.
Week 8–14
Guardrails & Compliance
The safety layer, designed in from the first sprint rather than bolted on at the end.
8
What we doFiltering on input and output, personal-data redaction, refusal handling and rate limits, each mapped to the DPDP Act, the Data Protection Board's guidance, any sector rules and your own data processing agreements.
What you getA documented guardrail set, a data protection impact assessment where one is needed, and a file your data protection officer can answer questions from.
Week 9–16
Integration
The output delivered into the tools your team already has open all day.
9
What we doWe connect to Zoho, Salesforce, HubSpot, Odoo, Tally, Zendesk or your own product through supported APIs, so the answer arrives where the work happens instead of behind another login.
What you getAI inside the daily workflow, in systems people already have open, so it gets used more than twice.
Week 10–17
Interface & UX
The screen your people will actually sit in front of every day.
10
What we doWe design for the moment the model is unsure: the source shown, a hand-off offered, the escalation route obvious. Then it gets tested with the people who will use it, not the people who signed for it.
What you getAn interface people trust, accessible to WCAG 2.2 AA, and still in daily use in week six.
Week 14–20
Evaluation & Hardening
Proof it holds up before anyone starts relying on it.
11
What we doRegression tests on every prompt and model change, red-team prompts probing for injection and leakage, load tests run at realistic volume, and a penetration test is booked where the workload is regulated.
What you getA quality baseline to measure against, and a system that holds under load and under attack.
Week 18–22
Deployment
Production rollout, staged so one bad change never reaches everyone at once.
12
What we doA canary release behind feature flags in your own cloud account, in a Indian region where residency applies, with observability on latency, cost and quality and a rehearsed rollback.
What you getA live system, runbooks and dashboards, and a team of yours that knows how to operate it.
Ongoing
Monitoring & Retraining
The years after launch, which is where most of an AI system's life is actually spent.
13
What we doWe watch for drift as your data and your customers shift, retrain on a schedule or on a trigger, and re-run the evaluation set whenever a new model version is released.
What you getQuality that holds over time, rupee costs that stay predictable, and a monthly report your board can read.
Client Voices
What Clients Say After Go-Live
“Appther built our student competition app exactly the way we envisioned it: easy registration, smooth judging, and real-time results that keep participants engaged.”
Dionna MilemOwner · My BOB Team LLC
“Building a mental health app means every detail has to be handled with care, and Appther got that from day one. The product feels warm, simple, and genuinely helpful.”
Bob SinghalProfessor of Joy · Joyscore Inc
“Appther exceeded every expectation, on time and on budget, and the final product has been instrumental in scaling our business. A rare engineering partner.”
Johnathan PefferCEO · Bull's Eye Technologies Inc
“Implementing an ERP across our operations felt daunting at first, but Appther made the transition seamless. Every department now works off a single source of truth.”
Pushpender KumarGeneral Manager · AB Sugar Ltd
Latest Insights
Guides for Indian AI Buyers
Architecture, cost and integration guides written from real builds, for the questions Indian teams ask before they fund one.
Where models earn their place inside Sage, Odoo, Dynamics and NetSuite, and where they do not.
Free 30-Minute AI Strategy Call, India Hours
Your AI Roadmap Starts With One Call on IST. Make It This Week.
Thirty minutes on IST leaves you with the use case worth doing first, a realistic rupee budget and a delivery date. No deck, no obligation, and a straight answer if AI turns out to be the wrong tool for it.
Prioritised use case Budget in rupees Delivery timeline
It depends on scope. A focused pilot, one use case with a working model and a live integration, usually lands between ₹12,00,000 and ₹35,00,000. A production platform with several models, MLOps and enterprise integrations starts around ₹50,00,000. You get a fixed rupee quote after a free scoping call, not an open-ended day rate.
A proof of concept on your own data takes four to six weeks. A production system with monitoring, retraining and integrations lands in three to six months, and the variable that moves that date most is almost never the model. It is how much of your data is still sitting in spreadsheets, which is why the data assessment is stage three rather than stage nine.
Yes, because the team is here. Engineering runs from our Noida office on IST, with a named senior engineer as your point of contact, so a question at 10am gets an answer at 10am rather than the next morning.
GPT, Claude, Gemini, Llama, Mistral and open-weight models, hosted through Azure OpenAI, AWS Bedrock, Google Vertex or in your own tenancy. The choice is made per workload by benchmarking cost, latency and accuracy on your own tasks, and we will tell you when a smaller model clears the bar at a fraction of the cost per call.
Consent, purpose limitation and retention are mapped per feature during discovery, with a data protection impact assessment where the processing warrants one. Incident handling is built to meet the CERT-In six-hour window, and where the RBI, IRDAI or ABDM apply, those constraints shape the architecture rather than the appendix. What you end up with is a file your data protection officer can hand over.
Yes. Where residency matters, models run in AWS Mumbai, Azure Central India or your own tenancy, with open weights hosted in-region rather than calling out to an API somewhere else. Where it does not matter, hosted APIs are cheaper and faster to launch, and we will recommend whichever fits your obligations rather than whichever is easier for us.
Yes, and that is usually where the return actually is. We connect models to Tally, Zoho, Odoo, Salesforce, HubSpot, Zendesk, SAP and in-house systems through supported APIs, and where a system has no API we say so in discovery rather than in month four.
It depends on how sensitive the data is, how much volume you have and how much latency you can live with. Commercial APIs win on speed to launch and quality per rupee at low volume. Open weights win when the data cannot leave your tenancy, when per-token pricing stops making sense at your volume, or when you need the model pinned so an answer does not quietly change next quarter.
All of it. Code, prompts, evaluation sets, fine-tuned weights and documentation are yours, in your own repository and your own cloud accounts, with an NDA signed before discovery starts. Nothing depends on us if you decide to take it in-house.
Fintech and lending, healthcare and diagnostics, retail and marketplaces, manufacturing, logistics, insurance, real estate and education. The common thread is a document or conversation backlog that people are still working through by hand, and a regulator who will want to know how the decision was made.
An AI Engineer Replies Within One Business Day, India Hours
Let's Scope Your AI Project
Tell us what you want to automate, predict or answer, and which systems hold the data. No sales pitch, just a straight answer on approach, scope, timeline and cost in rupees. Prefer email? Use our contact page.
Free 30-min strategy callBooked on India time, with plain advice on what to fund first.
Fixed-price quote in rupeesWritten scope and price before the build starts, no day-rate drift.
NDA on requestYour data, prompts and roadmap stay yours.
