FDE (Forward Deployed Engineer) is Tanjuan’s core delivery model: engineers go deep into your business on-site, combine general AI capability with your private data and processes, and deliver measurable business results directly.
The heart of FDE is turning "selling a product" into "embedding on-site to create value" — engineers don’t wait for requirements in a vendor’s office, they go into the customer’s business and own the final business outcome.
Palantir proved the FDE model over nearly two decades: engineers embedded on-site, wired data and software into critical operations, pulled off complex deployments others couldn’t, and earned exceptionally high retention and expansion.
OpenAI and Anthropic now build FDE teams to drive enterprise AI too — because no matter how strong a general model is, a "last mile" of scenarios, data and process still stands between it and business value.
We combine FDE with our in-house OpenClaw agent platform: the platform provides general agent capability, and FDE engineers wire it into your systems, data and processes, testing every step against business metrics.
Companies have bought plenty of "general AI tools" but put few of them to real use. The difference isn’t the model — it’s the delivery: who is responsible for wiring capability into the business, and who owns the result.
| Dimension | Traditional software delivery | FDE model |
|---|---|---|
| Entry approach | Sell a standardized product; the customer figures out how to use it | Engineers embed on-site, deep in the business |
| Understanding needs | Pre-sales demo + requirements doc | Run the workflow with the front line, living the real business |
| AI capability | General model, general features | General AI capability × private enterprise data & processes |
| Deliverable | Software features and training | A system running in production + measurable business results |
| Iteration rhythm | Released on version cycles | Small fast steps on-site, validated and adjusted weekly |
| Definition of success | Done once it passes acceptance | Only counts when business metrics improve |
We don’t start by "installing a big system" — we start from one high-value scenario: a small pilot, validated weekly, expanded once it works, with metrics to watch at every step.
Enter the business, run the workflow with the front line, and find the high-value scenarios truly worth doing that pay off measurably.
Combine general AI capability with your private data and processes, and set goals together as measurable business metrics.
Move in small fast steps on OpenClaw, validate impact directly in the real business, and see metrics shift week by week.
Turn what works into maintainable systems and documentation your team can take over and keep extending independently.
FDE stands for Forward Deployed Engineer. Rather than waiting for requirements in a vendor’s office, the engineer goes deep into the customer’s business, co-creates solutions by combining general technical capability with the customer’s private data and processes, and owns the final business outcome. The model was proven at Palantir, and OpenAI and Anthropic also use it to drive enterprise AI.
Outsourcing delivers "software written to a spec"; FDE delivers "measurable business results." An FDE engineer first embeds to understand the business, defines metrics with you, then iterates in small steps in the real business until metrics improve — rather than ending at acceptance.
Consulting usually delivers reports and advice, leaving implementation to the customer. An FDE engineer both designs and builds — delivering a system running in production and the business-metric changes that come with it.
Companies with real business scenarios and private data that want a measurable return on AI investment. Typical signals: bought general AI tools but can’t put them to use; clear cost-cutting or efficiency goals; complex workflows that standardized products can’t plug into.
It starts with a scenario assessment: pick one high-value scenario for a few-week small-scale pilot, validating metrics weekly; once it works, expand the scope and consolidate it into a long-term system. We don’t recommend launching a big, all-encompassing project up front.
Solutions are built inside the customer’s environment and support private deployment — in private mode, data can stay within your domain. Access control, action auditing and confidentiality agreements all follow your security requirements.
Book a scenario assessment: bring your business problem and we’ll look together at which steps are worth using AI for measurable results, and where the first pilot should begin.
