Core service
AI Agents & Workflow Automation
- Agents that read requests, look up records, draft replies and update systems, with a person approving anything risky.
- Typical wins: order handling, support triage, quote preparation and back-office admin.
Forward-Deployed AI Engineers
Senior AI engineers who join your team and ship to production
Most AI projects don't fail on the model. They fail in the gap between a good demo and the messy reality of your data, systems and people. Our forward-deployed engineers close that gap. They sit inside your team, learn how the work actually gets done, and build AI that people use every day.
The model
What Is a Forward-Deployed AI Engineer?
A forward-deployed engineer is a hands-on developer who works directly with the people who will use the software, instead of waiting at the end of a long chain of requirements documents.
In practice, that means joining your standups, sitting in on the calls where the work happens, reading the spreadsheets people actually use, and then writing the code. When something doesn't fit, they change it the same week.
It's the approach leading AI companies use to get their products working inside large customers. We offer the same model to businesses that want AI built around their own workflows.
How it's different
- 1
Close to the problem
Engineers talk to the people doing the work, not just to a project manager.
- 2
Builds, not advises
The output is working software in production, not a slide deck.
- 3
Owns the outcome
Success is measured by adoption and results, not hours logged.
Is it right for you?
When Forward-Deployed Engineers Make Sense
This model works best when the hard part isn't the AI itself, but fitting it into how your business really runs.
- 01
Your AI pilot never reached production
The demo impressed everyone, then stalled on integration, data quality or security questions nobody owned.
- 02
Your team knows the business, not LLMs
You have strong developers and domain experts, but no one who has shipped agents, RAG or evaluation pipelines before.
- 03
Your data lives in too many places
The answers are spread across an ERP, a CRM, shared drives and email threads, and someone has to connect them properly.
- 04
You need results this quarter
There's no time for a long discovery phase. You want a working first use case in weeks and a clear plan for the next one.
Compare
Forward-Deployed Engineers vs Consultants vs Staff Augmentation
All three have their place. The difference is who owns the result and how close the engineers get to the real problem.
| Our focusForward-deployed engineers | AI consultancy | Staff augmentation | |
|---|---|---|---|
| What you get | Working AI in production, built with your team | Strategy, assessments and recommendations | Extra developers who take tickets |
| Who defines the work | Shaped together, from time with your users | The consultants, in a report | Your team writes every task |
| AI experience | Senior engineers who have shipped AI before | Strong on strategy, varies on delivery | Depends on the individual hired |
| Success measured by | Adoption, time saved and business results | Quality of the plan | Tickets closed and hours billed |
| Best for | Turning a clear business problem into a live AI system | Deciding where AI fits and what to build first | Adding capacity to a team that already knows the way |
What we build
What Our Forward-Deployed Engineers Build
Every engagement starts with one business problem. These are the ones we're asked to solve most often.
Knowledge Assistants (RAG)
- Assistants that answer questions from your manuals, policies, tickets and contracts, and show the source for every answer.
- Access follows your existing permissions, so people only see what they're allowed to.
Document Processing
- Invoices, purchase orders, forms and delivery notes read, checked and entered into your systems automatically.
- Exceptions go to a person, with the reason clearly flagged.
AI Inside Your ERP & CRM
- Copilots and automations built on top of the systems you already run, without a risky replacement project.
- We work with the APIs, databases and quirks of older platforms, not just the modern ones.
Evaluation & Monitoring
- Test sets built from real questions, quality checks before every release, and dashboards for accuracy, cost and usage.
- So you can prove the system works, and notice quickly if it stops working.
The first 90 days
What a Typical Engagement Looks Like
Week 1: Embed
Engineers get access, meet the people doing the work and map the systems, data and pain points first-hand.
Weeks 2–3: Prove It
We pick one high-value use case and build a working prototype on your real data, with success measures everyone agrees on.
Weeks 4–8: Build for Production
Integrations, security, error handling, evaluation and monitoring. The unglamorous work that makes AI reliable.
Weeks 8–12: Roll Out
A staged launch with real users, weekly fixes based on their feedback, and results reported against the original goals.
Then: Hand Over or Scale
Your team takes over with full documentation and training, or we move on to the next use case together.
Have an AI Idea That's Stuck?
Tell us what you've tried so far. We'll tell you honestly whether an embedded engineer is the right fix, and what the first four weeks would look like.
What's included
What Every Engagement Includes
No hidden extras. These come as standard, whether you start with one engineer or a small team.
- Senior AI engineers, with a technical lead
- Working hours that overlap with yours
- Code in your repositories from day one
- Weekly demos of working software
- Security and architecture review
- Evaluation suite & monitoring
- Documentation & runbooks
- Knowledge transfer to your team
- NDA and IP owned by you
Technology
Tools Our Engineers Work With
We fit into your stack, not the other way round. These are the tools we use most, alongside whatever you already run.
- Models
- OpenAI GPTAnthropic ClaudeGoogle GeminiLlamaMistral
- Agents & Retrieval
- LangGraphLlamaIndexModel Context Protocol (MCP)pgvectorQdrant
- Engineering
- PythonTypeScriptFastAPINode.jsDocker & Kubernetes
- Cloud & Evaluation
- AWSAzureGoogle CloudLangfusePromptfoo
Why Infilon
Why Choose Infilon's Forward-Deployed Engineers?
We've been building software for businesses since 2009, much of it ERP, integration and data work. That's exactly where most AI projects get stuck, and where our engineers are most at home.
We're based in Ahmedabad and work with clients worldwide, with meeting times that overlap US, UK, European and Middle East business hours. You get senior people who have done this before, at a cost that makes an embedded team realistic.
Ways to work with us
- One embedded engineer for a focused use case
- A small pod: two to four engineers and a technical lead
- Monthly engagements, scaled up or down as needs change
- Hand over to your team, or keep us on for ongoing support
FAQ
Forward-Deployed AI Engineers FAQs
What is a forward-deployed engineer?
A forward-deployed engineer is a software engineer who works directly inside a customer's team, close to the people and processes the software is for. Instead of building from a written spec, they learn the problem first-hand and adapt the solution as they go.
How is this different from hiring dedicated developers?
Dedicated developers work on the tasks your team gives them. Forward-deployed engineers take on a business problem, help decide what to build, and are responsible for getting it live and used. They also bring AI experience your team may not have yet.
Do your engineers work on-site?
Most of our engagements are remote. Our engineers join your chat channels, standups and code reviews, with working hours that overlap yours. If on-site time matters for your project, tell us at the start and we'll plan it in.
How quickly can an engineer start?
Usually within one to two weeks of agreeing the scope, depending on the skills needed and your onboarding process.
Who owns the code and the models?
You do. Everything is built in your repositories and cloud accounts, under an NDA, and all intellectual property belongs to you.
How do you keep our data secure?
Engineers work inside your systems with the access you grant, following your security policies. We use private model endpoints where needed, keep sensitive data out of prompts and logs, and can sign additional data-protection agreements.
What happens when the engagement ends?
We hand over documentation, runbooks and evaluation tests, and run training sessions with your team. Many clients keep a smaller support arrangement afterwards, but there's no lock-in.
Ready to Put AI Engineers Inside Your Team?
Tell us about the problem you want to solve. We'll suggest the right team size and show you what the first month would deliver.
Talk to Our AI Engineers