Discovery inside your systems
The first week is spent in your ERP, CRM, help desk and spreadsheets with the people who do the work, not in a workshop. It ends with a written workflow, the data it depends on and the metric the pilot has to move.
Forward deployed engineering
Forward deployed engineering services for AI that has to run in production.
A forward deployed engineer (FDE) works inside your company, in your systems and your meetings, and builds AI through to production instead of handing over a recommendation. AIONDATA provides forward deployed engineers as a service to mid-size and growing companies, led from the San Francisco Bay Area with an engineering team in India.
ONE ENGINEER. INSIDE YOUR TEAM.
ShopifyStore
SAP Business OneERP
HubSpotCRM
PaycorHR & payroll
Forward deployed engineerYour repositories · your standups · your systems
Six-week pilotFixed price · go or no-go
In production, with a trained owner.Evals passed · writes behind approval gates · code in your repositories
Illustrative engagement. Systems, scope and timing are agreed in the scoping week.
Example: one engineer, pilot to production
Tell us the workflow that is stuck between pilot and production
A 20-minute call to see whether a scoping week makes sense. No preparation needed; bring the workflow and the systems it touches.
Please share the workflow and the systems involved—not credentials or customer records. We’ll agree the scope and next steps with you.
NVIDIA Inception member
Part of NVIDIA’s program for startups building with AI and accelerated computing.
About the program
Bay Area roots. Enterprise experience.
Pravin’s experience includes Google and SmartBear. Sravan previously held leadership roles at JPMorgan Chase and First Republic Bank.
Meet the foundersSan Francisco Bay Area, California
9110 Alcosta Blvd Ste H345, San Ramon, CA 94583
US-led delivery, with engineering in India. Supporting US and international organizations.Tell us where you want to start.
How we work
- 01
Scope
A one-week scoping engagement: we map the workflow, confirm system access and data, set the success metric and write a week-by-week pilot plan with a go or no-go at the end.
- 02
Embed
The forward deployed engineer joins your standups, repositories and channels. The first working version runs on real data by the end of week two; the rest of the pilot is evals, edge cases and users.
- 03
Run
The pilot becomes a production system: hardening, monitoring, approvals on every write, and the people who use it doing so daily while we watch the numbers with you.
- 04
Hand over
A trained owner on your side, documentation, runbooks and a list of the next workflows worth doing. Keep us on fractionally or not at all; the system does not depend on us.
The six-week pilot, week by week
Every pilot follows the same shape, so you know what you will see and when. The success metric is set in the scoping week and measured against a baseline before we write code.
- 1
Week 1
Baseline
Access, data pull and the first evaluation cases from your real history; the success metric measured before we write code.
You see: A one-page plan, the baseline number, and a channel where the engineer answers daily.
- 2
Week 2
First version
A first working version on real data, read-only.
You see: A demo on your own records, not a sample set.
- 3
Week 3
Evals
The evaluation suite built out, with edge cases collected from the people who do the job.
You see: An evaluation report: where it is right, where it is wrong, what we change.
- 4
Week 4
Approvals
Integration writes behind approval gates, plus the security review pack.
You see: Drafts appearing in your systems for one-click approval; a pack your IT lead can sign off.
- 5
Week 5
Users
The people who do the job use it daily; the metric is measured against the baseline.
You see: The number moving, or an honest explanation of why it is not.
- 6
Week 6
Handover
Hardening, runbook, handover notes and the go or no-go review.
You see: A production plan with scope and price, or a clean stop with everything documented.
Fixed price for the six weeks. Scope changes are written down and priced, never absorbed silently.
Forward deployed engineer, AI consultant, solutions engineer or staff augmentation?
The names overlap and the proposals look alike. The difference is who owns the result and where the work happens.
| Question | Forward deployed engineer (AIONDATA) | AI consultant | Vendor solutions engineer | Staff augmentation | Lab FDE (OpenAI, Anthropic) |
|---|---|---|---|---|---|
| Who they work for | You, on an outcome you defined | You, on a recommendation | The vendor, on adoption of its product | You, as extra hands | The lab, on adoption of its models |
| Where the work happens | Inside your systems, repositories and meetings | In workshops and documents | In demos and the vendor sandbox | Wherever your backlog points them | Inside your systems, for strategic accounts only |
| Owns production code | Yes, until handover | Rarely | No; configuration and samples | Yes, under your direction | Sometimes |
| Ends with | A running system and a trained owner | A plan | A configured product | Whatever got built | A reference deployment |
| Best when | A workflow is stuck between pilot and production | You need a strategy or a decision | You are buying that product anyway | You know exactly what to build | You are a large strategic customer of that lab |
| How it is priced | Fixed-price pilot, then scoped or monthly | Day rate or fixed fee | Bundled with the licence | Hourly or monthly per person | Usually part of a large commitment |
Lab forward deployed engineers are real and excellent; they are also reserved for the largest accounts. Mid-size companies get the services version, which is what this page describes.
What we leave behind
Forward deployed engineering is judged by what still works after the engineer leaves. These are in every handover.
| Deliverable | What it is | Why it matters |
|---|---|---|
| Evaluation suite | Test cases from your real history with expected results, run on every change | You can change the model or the prompt and know within minutes whether it got worse |
| Code in your repositories | MCP servers, sync services and agents committed to your accounts, with your CI | No vendor lock-in and nothing to migrate when the engagement ends |
| Approval gates and audit log | Every write to a system of record goes through a rule or a person, and is logged | Your auditors and your IT lead can answer “what did the AI do” with a query |
| Runbook and monitoring | What to check, what each alert means, what to do when a vendor API changes | The first incident is handled by your team, not by a ticket to us |
| A trained owner | One named person on your side who has operated the system with us for weeks | Someone who can say no to the next request and yes to the right one |
| Next-workflows list | The three to five follow-on workflows we saw while embedded, ranked by return | The second project starts from evidence, not a brainstorm |
When you need a forward deployed engineer, and when you do not
Not every request should become a pilot. These are the usual cases and what we say in the first call.
| Your situation | Our honest answer |
|---|---|
| A pilot worked in a demo but not on real data or with real users | Yes. This is the case forward deployed engineering exists for. |
| The workflow crosses two or more systems and the integration is the hard part | Yes. Most of our engagements start here. |
| You have strong developers but no AI delivery experience yet | A fractional forward deployed engineer alongside your team, plus technical training, rather than a full pilot. |
| Leadership needs a strategy and a budget before anything is built | Start with AI consulting; an engineer in your systems is premature. |
| You are configuring a product you have already bought, with no custom workflow | Use the vendor’s implementation partner; we cost more than you need. |
| The process changes every month and nobody owns it | Not yet. Appoint a process owner first; automating a moving target wastes the pilot. |
What forward deployed engineering is, and why it is suddenly everywhere
Forward deployed engineering started at Palantir, where engineers were sent to sit with customers and build on their data instead of waiting for requirements. In 2026 OpenAI, Anthropic, AWS and Google Cloud all run forward deployed engineering teams, and Deloitte launched a practice with the name. The reason is the same everywhere: AI demos are easy and production is hard, and the gap is closed by someone who understands the model, the customer’s systems and the customer’s people at the same time.
The lab teams serve the largest accounts. Everyone else gets the services version: forward deployed engineers as a service from a firm that embeds senior engineers in your company for a defined period, builds inside your environment, and leaves behind a running system and a trained owner. That is what AIONDATA offers on this page.
Forward deployed engineers as a service for mid-size companies
Our clients are mid-size and growing companies: consumer brands, distributors, software vendors, staffing and professional services firms, typically between fifty and a thousand people. They run on SAP Business One, NetSuite, Business Central, Shopify, HubSpot, Paycor or QuickBooks rather than a custom platform, and they do not have a machine learning team. Their AI projects stall for ordinary reasons: the data lives in three systems, the vendor API does not do the one thing the workflow needs, and nobody owns the last mile.
A forward deployed engineer is the owner of that last mile. They work in your repositories and your channels, build with the people who do the job, and measure the result against a baseline set before the first line of code. When the pilot passes, the same engineer takes it to production. When it does not, you get a written explanation and everything we built, and you stop.
Hire forward deployed engineers without building a team
Hiring a forward deployed engineer in the Bay Area is a long, expensive search for a role that barely existed two years ago, and most mid-size companies need the capability for a few workflows a year, not every day. Our standing engagement gives you a named engineer for two to five days a week, month to month, in your systems and your meetings, backed by our Noida team for the build work that benefits from more hands.
The code, the MCP servers, the evaluation suites and the documentation are yours from the first commit. We ask for one thing in return: an internal owner who works with us from week one, because the handover only works if someone on your side has operated the system with us.
How we keep client work confidential
Every engagement runs under an NDA and a master services agreement. We never name clients on this site; when we describe an engagement, it is by industry, size band and region only, with the details that could identify the client removed. Demos use synthetic or anonymised data; we never put customer records, credentials or personal data in a prompt, a screenshot or a sales call. If a card reads vaguer than you would like, that is deliberate, and a reference conversation under NDA can usually be arranged.
Why enterprises choose AIONDATA
A Bay Area lead on your time zone, with an engineering team in Noida for throughput and round-the-clock delivery
Fixed-price scoping week and six-week pilot with a go or no-go, so the first decision costs weeks, not quarters
Everything in your repositories and accounts from day one, so nothing has to be migrated when we leave
Evaluation before automation: no agent writes to a system of record until it has passed your cases
ISO 9001:2015 and CMMI Level 3 certified delivery processes; NVIDIA Inception program member
We run our own products in production (AION CRM, AION HRMS, SynergyOS), so “production” means the same thing to us as to you
More AI services
AI Consulting Services
Strategy, roadmap, and hands-on delivery from an enterprise AI team.
Learn moreCustom AI Software Development
Full-cycle design and build of enterprise-grade AI products.
Learn moreTell us the workflow that is stuck between pilot and production
The enquiry form is at the top of this page. Tell us the workflow and the systems involved.
Go to the enquiry form