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Forward Deployed Engineering, sized for the mid-market

Your outsourced Forward Deployed Engineer.

A senior, US-based engineer who embeds in your team, builds the solution, ships it into production, and owns it through adoption. The model Palantir and the AI labs reserve for seven-figure clients, built for ops-heavy small and mid-sized businesses.

Start a Forward Deploy Sprint See how it works →

Established Texas company since 2020 · Senior US engineers + agentic AI · Your data can stay in your environment

What is a Forward Deployed Engineer?

The Forward Deployed Engineer (FDE) is the model Palantir invented and OpenAI, Anthropic, and Snowflake now run. Instead of selling you software and walking away, the company puts a senior engineer directly inside the customer's environment to discover the real problem, build the solution, and ship it into production. It is the fastest-growing role in software for one reason: it works.

Until now that model was locked behind enterprise platform spend. You could only get a Forward Deployed Engineer if you were buying a six- or seven-figure system to go with them. PenDraco democratizes it. We bring the same embed, build, ship, and own discipline to companies with the same problem: messy operational data, no senior software talent in-house, and no intention of writing a Palantir check.

In plain terms: an outsourced FDE is a senior engineer embedded in your team, without a full-time hire. Not a contractor you have to manage. Not advice you then have to implement yourself. A builder who owns the outcome.

Embed

We work inside your systems and alongside your people, not from a distant backlog.

Build

We define what done means, then build it. Deterministic where it should be, AI where it adds value.

Ship

Real work, into your production environment, used by real people on real data.

Own

We stay through adoption, then hand off something your team actually runs.

How an engagement works

Three steps, designed so you see value before you commit, and so the work compounds instead of evaporating when we leave.

  1. Step 1

    Forward Deploy Sprint

    A fixed-fee, 30 to 60 day embedded engagement on one painful, expensive workflow. Real users, real data, a defined finish line. You keep everything we build, whether or not you continue.

  2. Step 2

    Embedded FDE Retainer

    A senior engineer embedded in your team month to month, building and shipping the way an in-house principal would, plus a decade of patterns and reusable accelerators. A fractional CTO who actually writes the code.

  3. Step 3

    Managed Platform + Jakada.ai

    We operate your open-source ERPNext / Frappe platform, then layer Jakada.ai, our AI agent for ERPNext, on top, so your team runs the system in plain language. This is the part that keeps paying off after the build is done.

We hold ourselves to first integration within two weeks and production within ninety days. If a problem cannot reach production in ninety days, it was mis-scoped, and we say so.

See the full offer →

Why this compounds instead of evaporating

Most consulting is linear. You pay for hours, the hours end, and the knowledge walks out the door. PenDraco is built as a flywheel, so every engagement leaves you with more than you started with.

1

An embedded FDE lands the work and ships it into production.

2

The work runs on a platform we operate for you, recurring and supported.

3

Your team adopts Jakada.ai and the managed platform, so the system keeps improving.

4

Each engagement deposits reusable accelerators into our library.

5

The next build starts from a higher baseline, which lowers your cost and our delivery time.

Senior engineers, multiplied by AI, on hardware we own

The honest objection to embedded engineering for smaller companies is cost. We changed what one senior engineer can deliver, not just the price.

Experienced engineers wielding AI

A senior FDE augmented by agentic AI delivers what used to take a small team. The differentiator is experience plus AI leverage, not cheap labor or offshore arbitrage.

Owned, offline, open-weight inference

We run open-weight models on owned, on-premise hardware. That turns per-token AI cost into a fixed, owned capability, so there is no usage meter to pass on to you and your pricing does not move with the frontier API market.

Your data never has to leave

Because inference runs on owned, offline hardware, client data does not have to ship to a third-party model provider. Decisive for regulated and data-sensitive work.

Read the AI advantage in full →

A decade of embed, build, ship, own, before it had a name

PenDraco was founded by Steve A. Onoja, who rose to Master Principal Customer Success Engineer, Qlik's top technical band, and earned two President's Awards across roughly ten years of forward-deployed delivery.

Kareo

Rescued a failing five-node cluster from inside the customer's AWS and Snowflake; nightly reload cut from 50-plus hours to 20.

Insurity

Built an underwriter next-best-action engine and shipped it inside the customer's own product. Qlik NA OEM Partner of the Year 2023.

Lenovo

Owned the on-premise architecture across 330,000-plus seats, then led the migration to Qlik Cloud.

More on the founder and the record → See the delivered work →

Put a Forward Deployed Engineer inside your business.

You have the same problem the enterprise has: operational data scattered across spreadsheets and disconnected tools, no single source of truth, no senior software talent in-house. You should have the same solution.

Start a Forward Deploy Sprint Book a scoping call →

Fixed fee. Defined finish line. You keep the work.