FdeJit's forward deployed engineers (FDEs) embed with your team. They learn your business first, then deploy models in your own environment, connect your systems, and keep iterating as you grow.
Illustrative estimate. Final scope comes from discovery.
A forward deployed engineer (FDE) doesn't just ship software. They embed in your business until the AI is actually working.
Engineers work on site or remotely beside your business owners to define the problem. You get results, not slide decks.
Models and data stay in your data center or dedicated cloud, with access control and audit built to your compliance needs.
We connect ERP, CRM, ticketing and knowledge bases so AI shows up in the tools your staff already use.
We train your team to run it, then keep adding use cases as the business grows.
Your private model exposes an OpenAI-compatible API, so existing systems connect by changing one URL.
from openai import OpenAI client = OpenAI( base_url="http://ai.company.local/v1", api_key="internal-key") r = client.chat.completions.create( model="qwen-72b", messages=[{"role":"user","content":"Hello"}]) # runs inside your own environment
Sized to your business, from a workstation in the office to multi-rack GPU clusters.
One or two RTX 4090 cards. Runs in an office and suits a single pilot use case.
4U rack server with 4 × RTX 4090 or H100, serving several teams at once.
Multi-node interconnect and dedicated networking, with optional liquid cooling, for company-wide rollout and fine-tuning.
Best starting points: sensitive data, repeatable processes, lots of manual work.
Policies, contracts and product docs become a Q&A system. Nothing leaves your network.
Source code never goes to a public cloud. Completion, review and docs run in-house.
Medical records, legal papers and financial documents are summarized and extracted locally.
Factory inspection and support bots run locally with low latency, even offline.
From a two-week proof to a long-term embed, matched to your stage. Hardware can be bought or leased. Prices are illustrative.
| Model | Best for | FDE team | Duration | Reference price |
|---|---|---|---|---|
| Discovery sprint | Proving one use case | 1 FDE | 2 weeks | $8k |
| Embedded delivery | Rolling out several use cases | 2–3 FDEs | 3–6 months | $16k / month |
| Ongoing partner | Iteration and scaling | As needed | Monthly | from $5k / month |
Every step has clear deliverables and acceptance criteria.
Interview business owners and map processes, data and pain points.
Pick priority use cases, then models, hardware and budget.
Private deployment, system integration and user training.
Add use cases and upgrade models and hardware as you grow.
An engineer will reach out within one business day to set up a free discovery call.