Your AI pilot works in 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 runs in production.
Embedded, and accountable 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 ship a working production system and stay until it runs without us.
AI consultancies and advisory firms
Strategy decks, maturity assessments, roadmaps. Genuinely useful documents that do not, by themselves, put anything into production.
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 standing this up in production would genuinely involve.
A costed path to production, with the parts that will be hard 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
Into production, with monitoring, alerting, access control and cost visibility. On your own servers or private cloud where data governance requires it. This is the stage most AI projects never reach.
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.
RAG over your own data
Retrieval across your documents, tickets, contracts or catalogue, with answers traceable to the source they came from rather than asserted.
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.
On-premise deployment
Where your data cannot leave your environment, we run smaller models on your own infrastructure and design around that constraint from the start.
Three shapes, depending on where you are.
Discovery sprint
Stage one on its own. We assess your data, the use case and what production would take, and you get a costed path. Paid, because a free assessment is worth what you pay for it and because it starts the relationship honestly.
Embedded engineer
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.
Discovery to production
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 your environment rather than from a vendor's office, and who stays with the system until it runs in production. The role exists because the hard part of enterprise AI is not model access, which everyone has, but deployment: integration, evaluation, monitoring, security and cost. It is a role firms like Palantir, OpenAI and Databricks staff heavily, and it is only now becoming known in India.
Ownership of the outcome. A contractor bills for time and takes direction from you; if the project fails they still get paid and they leave. We are accountable for the system working in production, we bring the method rather than waiting for yours, and we do not consider the engagement finished until it runs.
Because a demo and a production system are different pieces of engineering. A demo needs a model that answers well on chosen examples. Production needs evaluation that catches wrong answers, monitoring, access control, cost management, and integration with systems built long before anyone said the word AI. Teams that have never shipped one underestimate that gap, and the pilot quietly stalls.
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 discovery sprint. One to two weeks, fixed fee, and at the end you have a costed path to production 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 assess your data, the use case and what production would actually take, and you leave with a costed path. 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.