About iSpark
iSpark is an AI services company. We are an online business based in India and we deliver to clients anywhere in the world. This page sets out what we build, how an engagement runs, what we will not sell you, and how to tell quickly whether talking to us is worth your time.
Most AI company pages open with a founding story and a photograph of a room. We would rather use the space to tell you something you can act on. So this page is a plain description of the business: what it sells, where it operates from, how the work is structured, and the kinds of request we turn away. If you are trying to decide whether to send us a brief, everything you need to make that call is below.
iSpark is a global AI services provider and an online company from India. We design, build, deploy and maintain artificial intelligence systems for organisations in fourteen industries, covering machine learning, generative AI, computer vision, natural language processing, conversational AI, AI agents, data engineering, MLOps and AI governance. Delivery is remote, the working language is English, and the published catalogue runs to 155 individual services grouped under sixteen engineering pillars.
What iSpark actually does
We are an engineering and advisory firm, not a reseller and not a platform. A client comes to us with a problem that sits somewhere between a business process and a data set, and we work out whether a model, a pipeline, an agent or a plain piece of software is the honest answer. Then, if the answer is one we can build well, we build it and hand it over with the documentation, tests and monitoring that make it survivable after we leave.
The published catalogue is organised into sixteen pillars. Each pillar is a body of engineering practice rather than a marketing bucket, and each has its own page describing what it covers and where it stops.
- Model building. Machine learning development, computer vision, natural language processing and generative AI development, covering everything from a forecasting model to a retrieval system sitting on top of a large language model.
- Systems and interfaces. AI agent development, conversational AI, AI automation and AI product design, where the engineering problem is as much about workflow, handoff and interface as it is about the model itself.
- Foundations. Data engineering and MLOps, which is the unglamorous half of the field and the reason most AI pilots never reach production.
- Control and oversight. AI governance, covering bias testing, model documentation, evaluation harnesses and the evidence trail that regulators and auditors ask for.
- Applied work. AI marketing, creative AI, emerging AI research, AI consulting and managed AI services, for teams who need the capability run rather than delivered once.
Alongside the pillars, the site carries fourteen industry sections and twelve business function pages. Those exist because the same technique lands very differently in a hospital than in a warehouse, and because the person reading is usually a finance lead or a head of operations rather than a machine learning engineer. The industry and function pages start from the problem. The service pages start from the method. They link to each other in both directions.
Why the catalogue is this large
It is not a claim that we are equally strong at 155 things. It is a claim that the field is genuinely that wide, and that pretending otherwise leads clients to buy the one technique a vendor happens to sell. Each service page states the depth we work at, and several of them say plainly that the right move is to buy a product instead of commissioning us.
A global services provider and an online company from India
iSpark operates online. There is no branch network, no regional office map and no requirement that anyone travel. The company is based in India and the whole delivery model is remote, which is a deliberate structure rather than a compromise, and it shapes the work in three ways worth stating openly.
- Cost. An Indian engineering base means a rate structure that lets a mid sized organisation run a serious AI project rather than a proof of concept that dies at the budget stage. We would rather you spend the saving on a longer evaluation period than on a bigger slide deck.
- Overlap. Indian working hours overlap comfortably with Europe, the Middle East, Africa, South East Asia and Australia, and reach the American east coast in the morning. We agree the overlap window at the start of an engagement and we keep it, because remote work fails on availability long before it fails on skill.
- Written practice. Remote delivery only works if decisions are written down. Every engagement produces a running record of what was decided and why, which is the same artefact an auditor or a successor team will want anyway.
Clients are worldwide. Data residency, regional privacy law and sector rules are handled per engagement rather than assumed away. If your data cannot leave a jurisdiction, say so in the first conversation and we will design around it or tell you we are not the right supplier. That question has ended engagements before they started, which is the correct outcome when the answer is no.
Everything is quoted and delivered in English. If your organisation needs delivery in another language, the models we build can work in that language, but the project documentation and the working conversations will be in English.
How we work with clients
There are three shapes an engagement takes. Most clients start in the first and move to the second or third once the problem is understood. Nobody is asked to commit to a year before they know whether the idea works.
| Shape | What it is | Typical length | When it makes sense |
|---|---|---|---|
| Assessment | A structured look at your data, your process and the options, ending in a written recommendation that may be to do nothing | 2 to 4 weeks | You suspect AI applies but cannot yet tell which problem to point it at |
| Build | A defined system delivered to production with tests, documentation, monitoring and a handover session | 6 weeks to 6 months | The problem is understood and the value of solving it is quantified |
| Managed | We run and maintain a system, watch it for drift and failure, and retrain or adjust it on an agreed cadence | Ongoing, reviewed quarterly | The system matters to the business but you have no internal team to own it |
Whichever shape applies, the first conversation is thirty minutes and costs nothing. It is not a sales call with a demo attached. It is an attempt to work out what is actually going wrong, and it frequently ends with us naming a product you should buy instead.
What we will not do
A services firm is defined as much by its refusals as its offerings, and ours are written down so you can check them against what you are about to ask for. These are not conditional. They hold even when the engagement is large.
- We will not build a system whose output nobody can check. If there is no way to tell a correct answer from a confident wrong one, the system is not ready to be built, however good the demonstration looked.
- We will not quote on a data set we have not seen. Estimates made before looking at the data are guesses dressed as numbers, and they are the main reason AI projects overrun.
- We will not sell a custom build when a product exists. If a tool you can license this week does eighty percent of the job for a fraction of the cost, we will say so, even when that costs us the engagement.
- We will not build systems that score, rank or profile people in ways we would not defend publicly. That includes covert emotion inference, predictive scoring of individuals for punitive use, and scraping that ignores the terms of the source.
- We will not claim accuracy we have not measured. Numbers on our pages come with the conditions under which they were produced, and where a technique is immature we say the word immature.
On being honest about what is still hard
Large language models remain unreliable at arithmetic over long contexts, at knowing the boundary of their own knowledge, and at tasks where being quietly wrong is worse than refusing. Agent systems compound those errors across steps. None of that makes the technology useless. It makes the design question a question about verification, which is where most of our engineering time actually goes.
How an engagement runs
Five stages, the same on every project, with a written decision point at the end of each one. You can stop at any of them.
Problem framing
Thirty minutes to understand what is going wrong and who feels it. We establish what success would look like in a number you already track, not one we invent.
Data and feasibility review
We look at the actual data, not a description of it. This stage produces an honest verdict on whether the problem is solvable with what exists, and it is where most weak ideas end.
Design and scope
A written specification covering the approach, the evaluation method, the failure modes we expect, the integration points and a cost. Priced before any building begins.
Build and evaluate
Iterative delivery with an evaluation harness in place from the first week. You see results against held out data, not a curated demonstration.
Deploy, document, hand over
Production deployment with monitoring, a runbook, model documentation and a session with whoever will own it. Optionally followed by a managed arrangement if you want us to keep it running.
What we believe about AI work
Six positions that decide how we build. They are opinionated and they cost us work sometimes, which is roughly the point.
Data quality beats model choice
Across almost every engagement, the gains came from fixing inputs rather than swapping architectures. The exciting part of the field is rarely the part that moves the number.
Evaluation before building
If we cannot describe how the system will be measured before it exists, we are not ready to build it. An evaluation harness written afterwards tends to flatter whatever was built.
Boring infrastructure is the differentiator
Pipelines, versioning, monitoring and rollback decide whether a model survives contact with production. Most failed AI projects failed here, not at the modelling stage.
Humans stay in the loop where it matters
For decisions that affect a person's money, health, employment or liberty, the system proposes and a person disposes. We design the review step as a first class feature.
Documentation is part of the deliverable
A system nobody can explain is a system nobody can fix, defend to a regulator, or safely change. The written record ships with the code.
Say when the answer is no
The most valuable thing a consultant can tell you is that the project should not happen. We have ended assessments that way and we will do it again.
Is iSpark a sensible fit?
Worth being direct about this, because a mismatched engagement wastes your budget and our time in equal measure.
Worth talking to us if
- You have a specific operational problem and a rough sense of what solving it is worth
- There is real data behind the process, even if it is messy, incomplete or spread across systems
- Someone internally will own the system after handover, or you want us to run it under a managed arrangement
- You are comfortable working with a remote team and agreeing a daily overlap window
- You would rather hear that a product can solve it than be sold a build
Look elsewhere if
- You need a supplier with staff physically present at your site
- The requirement is a general AI strategy deck with no system at the end of it
- The data cannot leave a jurisdiction we cannot legally operate within, and no local option is acceptable
- You want a demonstration for a board meeting rather than something that will run in production
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
Where is iSpark based?
iSpark is based in India and operates as an online company. Delivery is fully remote and clients are worldwide, with working hours arranged to give a reliable overlap window with your team. There is no office visit required at any stage of an engagement.
Does iSpark work with clients outside India?
Yes. iSpark is a global AI services provider and most engagements are with organisations outside India. Indian working hours overlap well with Europe, the Middle East, Africa, South East Asia and Australia, and reach the American east coast in the morning. Data residency and regional privacy requirements are handled per engagement rather than assumed.
What does iSpark charge, and how are projects priced?
Pricing is quoted per engagement after we have seen the data, because estimates made before that point are guesses. Assessments are fixed price. Builds are quoted against a written specification produced during the design stage. Managed arrangements are monthly and reviewed quarterly. The first thirty minute conversation is free and carries no obligation.
How many services does iSpark offer?
The published catalogue lists 155 individual services grouped under sixteen engineering pillars, alongside fourteen industry sections and twelve business function pages. Each service page states the depth we work at rather than implying uniform expertise across all of them.
Will iSpark tell us if AI is the wrong solution?
Yes, and it happens regularly. Assessments end with a written recommendation that is sometimes to buy an existing product, sometimes to fix a process first, and sometimes to do nothing at all. That recommendation is delivered whether or not it costs us the build.
Who owns the models and code iSpark builds?
You do. Intellectual property in bespoke work transfers to the client on payment, including trained model weights, pipeline code and documentation. Where an engagement uses open source components or a licensed model, the licence terms are listed in the specification before work begins so there are no surprises at handover.
Where to go next
Three ways into the catalogue, depending on whether you are starting from a technique, an industry or a team.
AI services
Start from the engineering. Every pillar, from machine learning to AI governance, with 155 services beneath them.
Read more →Industries
Start from your sector. What AI is actually doing in healthcare, finance, manufacturing, retail and eleven more.
Read more →Solutions
Start from your team. AI for sales, finance, HR, legal, operations, security and the rest of the business.
Read more →Tell us what the problem looks like.
Thirty minutes, no charge, no deck. We will tell you whether this is an AI problem, a data problem, or a process problem, and we will say when the honest answer is to buy something rather than build it.