Why forward-deployed engineering
A working model is not a running system. Forward-deployed engineering is the way to close the gap, and to leave it closed.
In recent years I have trained more than 3,000 engineers across India, Singapore and the Gulf, including teams at three Fortune 500 companies.
What forward-deployed engineering solves
Forward-deployed engineering (FDE) puts an engineer inside the client's environment, close to the problem, instead of handing over a product from a distance. For AI, it means finding the real workflow, designing the architecture around the client's data and systems, building in evaluation and human review from the start, and staying until the result runs and a named owner can keep it running.
I do it directly for enterprises, and I build deployed teams for services firms and capability centres through FDE upskilling and corporate training. The work is hands-on: I write the code and the evaluation harnesses myself. These are the four situations I am most often called into.
The pilot works, but nobody knows how it fits the operating workflow
Clarify ownership, system boundaries, evaluation and the conditions required for deployment.
The architecture debate begins before the business decision is clear
Establish the outcome, constraints and evidence first. Then make the technology choices.
AI prototypes cannot cross enterprise data and integration boundaries
Design retrieval, agents, APIs and review controls around real security and legacy-system constraints.
Engineers know the tools but cannot yet own customer-context delivery
Build FDE capability through realistic scenarios, applied labs, architecture defence and operational handover.
Building a forward-deployed team of your own? Start with the checklist for companies.
How I help
AI architecture advisory
I look at one stalled or proposed AI initiative and tell leadership what it needs next: proceed, reframe or pause.
The usual starting point is a two-week AI Delivery Readiness Review. Where the use case is agreed but the design is not, an Architecture Sprint follows.
Forward-deployed delivery
I lead the build of one bounded use case inside your systems, with your engineers working alongside. Responsible AI is built in, not added later: evaluation, human review and data boundaries are written down before the build starts.
It ends with working software, evaluation results and a team that can run it.
FDE upskilling and corporate training
Cohort programmes for services firms and capability centres that need engineers who can sit with a client, find the real problem and ship something. Participants work through realistic client scenarios and are assessed on what they produce, not on attendance.
I also run shorter role-specific AI programmes for enterprise teams.
Selected work
Much of this work is under non-disclosure, so some clients are described rather than named.
Architecture and delivery
- One of India's largest two-wheeler manufacturers Designed the AI system for its dealer and customer operations. An AI-led buying experience for customers, a custom model integrated into the enterprise platform, and an analytics product built on the company's historical data: data modelling first, then predictive signals the business could act on. Decided with the client which models run locally and which through API model gardens. Delivered through specification-driven development.
- A top-ten Indian infrastructure company Designed AI automation for internal document processing and for the processes that run on its SAP system.
- A large Indian private-sector bank Machine-learning solutions for its credit-card customer experience and data collection.
- Crédit Agricole Web solutions with AI built in, and prompt-based working methods for everyday office work, with upskilling for the teams that use them.
- Whirlpool and Zeiss, in Italy and Switzerland Predictive AI and machine-learning models, and virtual-reality applications.
Forward-deployed teams and training
- Tredence Building forward-deployed teams for its clients. The FDE capability programme on Google Cloud (model selection and evaluation, retrieval, agents and Vertex AI), the platform that ran its residential cohort, and the delivery processes that go beyond upskilling.
- Persistent Systems Advanced AI engineering covering evaluation and model selection, fine-tuning, FDE practice and agent-first engineering.
- Walmart A three-day programme on Microsoft 365 Copilot, Power Automate and Copilot Studio for procurement teams.
- Other organisations I have trained or advised Infosys, Wipro, Cognizant, Mphasis, Zensar, Hexaware, Publicis Sapient, EY India, Siemens, UnitedHealthcare, USA, Maximus, Innovaccer, upGrad and Volandia, Italy.
Writing
- Loop engineering: when an agent should stop
An agent is a loop. Four decisions I write down before the first run, and the production failures they prevent.
- Designing the pause: green, amber, red
Every agent needs a traffic light before a prompt. How I classify actions as green, amber or red, and why the tool layer enforces it.
- The FDE handover pack: what to leave behind
An AI system that can not be handed over is not finished. The four part handover pack I ask for before any demo.
- FDE operating models for India's GCCs
GCCs have engineers, licences and pilots. What is missing is the role that takes a workflow into production. Three ways to run it.
Get in touch
- Enquiry
- Send an enquiry
- hello@mayurpatil.ai
Based in Nashik, India. I have worked with clients in the United States, the United Kingdom, the UAE, Singapore, Malaysia, India, Italy, Switzerland, South Africa and Mauritius, and I travel for engagements. I speak at industry events.
Looking for an individual seat rather than a corporate programme? See Programmes.