Most AI stops after the demo. Ours goes live.
Most enterprise AI work in India stops at a proof of concept that impressed a room and then went nowhere. Not because the model was wrong, but because getting a demo to answer well and getting a system to run reliably in your business are two different pieces of engineering. Forward Deployed Engineering is how we do the second one.
A senior engineer works inside your team and your environment, builds your system, and stays until it is running every day.
We sit in your team, and we answer for whether it works.
The word is new in India, so it is worth being precise. Here is what it is not, because each of these is something you have probably already been sold once.
Not a consultant
A consultant assesses your situation and hands you a document. We write the code, in your environment, and the deliverable is a system that runs.
Not staff augmentation
A contractor bills hours and owns nothing. When the contract ends they leave, whatever state the work is in. We own the outcome, which is a different arrangement entirely.
Not a licence sale
We are not selling you a platform and calling the integration your problem. The engagement ends when the thing works in your business, not when the software is installed.
You have probably tried one of these.
We are not claiming these firms are bad at what they do. We are saying what they do is a different job, and that the difference is where AI projects usually die.
Large IT services firms
Expensive, slow, and layered. You are sold by a partner and delivered by a pyramid of juniors, against a generic AI centre-of-excellence pitch that is the same one they gave the last three clients.
One named senior engineer, inside your team, who owns whether it works.
Staff augmentation and body shops
They bill hours. They own nothing. Direction has to come from you, and when the contract ends the knowledge walks out with the person.
We hand over something that works, and stay until it runs without us.
AI consultancies and advisory firms
Strategy decks, assessments, roadmaps. Genuinely useful documents that do not, on their own, get anything working.
We write and own the code, in your environment, against the plan we agreed.
Freelancers and small AI shops
Often strong engineers. Usually no evaluation framework, no monitoring, no cost control, and no story for security or data governance when your compliance team asks.
Production discipline: evals, observability, cost control, and on-premise where governance demands it.
Discover, Design, Build, Deploy, Optimize.
Written down, followed on every engagement, and the same method our founder is writing a handbook on. Most firms selling AI services in India have no named method at all, which is why every engagement with them is an experiment.
Discover
We look at the data you actually have rather than the data you wish you had, the business problem behind the AI request, and what getting this working would genuinely involve.
A costed plan to get it running, with the hard parts named out loud.
Design
Architecture, model choice, how the data flows, where it is allowed to go, and crucially the checks we will judge the system by. Agreed in writing before anybody writes code.
A design, and an agreed definition of working so nobody argues about done later.
Build
Senior engineers writing code inside your environment, alongside your team, in short cycles you can see. Your engineers learn the system as it is built rather than at handover.
Working software, reviewed by you as it appears, not presented at the end.
Deploy
Switched on for real, with monitoring, alerts, control over who can use it, and a clear view of what it costs to run. On your own servers where your data has to stay with you. This is the step most AI projects never get to.
A system running against real users and real data, that somebody is watching.
Optimize
Measured against the checks agreed in stage two. Tuned for accuracy and for cost, watched for drift as your data changes, and handed to your team with documentation.
Evidence it works, and a team that can keep it working without us.
The work itself.
Answers out of your own documents
It searches your files, tickets, contracts or catalogue and answers from them, and every answer shows which document it came from instead of just sounding confident.
Custom AI agents
Agents that do real work inside your systems, with clear limits on what they may act on alone and a record of everything they did.
Workflow automation
The work your team repeats every day, automated where the failure cost is understood and a person stays in the loop where it is not.
Evaluation frameworks
The part almost everyone skips. Tests that catch hallucination and regression before your customers do, run on every change.
Integration with what exists
Your ERP, CRM, data warehouse and internal tools. Most of the difficulty in enterprise AI is here, not in the model.
Runs on your own servers
Where your data is not allowed to leave your building, we run smaller models on your own machines and design around that from day one.
Three shapes, depending on where you are.
Discovery sprint
Step one on its own. We look at your data, the job you want done and what it would take to get it working, and you get a costed plan. Paid, because a free assessment is worth what you pay for it, and because it starts the relationship honestly.
An engineer inside your team
One or more senior engineers working inside your team as part of it: your standups, your repository, your environment. The core offering, and the one that suits an organisation with several things to build.
From first look to fully running
All five stages on one scoped use case, priced and billed against milestones. Suits an organisation that wants one thing done properly and wants to know the number in advance.
We are writing the reference text for this discipline.
Udhayaseelan, our founder, is writing The Forward Deployed Engineer Handbook: three volumes on taking enterprise AI from discovery to deployment, built around the same five-stage method we run every engagement on. The table of contents and sample chapters go public as they are written.
It is not published yet, and we will not announce a date we cannot hold. What matters to you is that the method in it is the method your engineers will be working alongside, and that our own engineers are trained on it.
Whether this is for you.
We would rather lose a deal at this paragraph than three months into one.
A senior engineer who works inside your team and on your systems rather than from a vendor's office, and who stays until the thing is running every day. The role exists because the hard part of AI is not getting access to a model, which everyone has now, but everything after that: joining it to your existing systems, testing it, watching it, securing it and controlling what it costs. Firms like Palantir, OpenAI and Databricks staff this role heavily. It is only now becoming known in India.
Who carries the risk. A contractor bills for time and waits for your instructions; if the project fails they still get paid and they leave. We answer for whether the thing actually works, we bring our own way of working rather than waiting for yours, and we do not call the job finished until it is running.
Because a demo and a system your team relies on every day are two different pieces of work. A demo needs to answer well on examples somebody chose. The real thing needs testing that catches wrong answers, monitoring, control over who can use it, a handle on running cost, and connections to systems built long before anyone said the word AI. Teams that have never done it underestimate that gap, and the project quietly stops.
Yes, and we prefer it. Your engineers are in the repository and in the reviews from day one, which is how the knowledge stays with you. An engagement that leaves your team unable to maintain what we built is a failed engagement even if the software works.
Yes. Where data cannot leave your infrastructure we design for that from stage two rather than discovering it at deployment: smaller models running on your own hardware, private cloud, and an architecture that assumes the constraint instead of working around it.
A short study. One to two weeks, fixed fee, and at the end you have a costed plan and a clear view of whether the thing is worth building at all. If the honest answer is that it is not, we will say so, and that is a cheaper way to find out than a six month build.
Not yet. Our founder is writing The Forward Deployed Engineer Handbook, and the table of contents and sample chapters are public as they are written. We will not announce a publication date we cannot hold. What matters for your engagement is that the method in it is the method we use.
Start with a discovery sprint.
One to two weeks, fixed fee. We look at your data, the job you want done and what it would really take to get it working, and you leave with a costed plan. If the honest answer is that it is not worth building, we will tell you that instead, which is a cheap way to find out.