01
RAG pipelines
Retrieval over your documents and data, with sources cited, so every answer can be checked.
What it is
A forward deployed engineer is a senior engineer who works inside your business to scope, build, and ship a production AI system, and stays accountable until it is used.
It is how the frontier labs put their own models to work inside enterprises, and it is how we work. The people who scope the work are the people who build it: no hand-off from a strategy team to a delivery team, and no deck standing in for a system.
The gap is rarely the model. It is the distance between a boardroom ambition and a system people use on Monday morning. We close it by doing both jobs at once: setting the strategy and writing the code.
The difference
Forward deployed engineering, compared with a traditional engagement.
| Question | Forward deployed engineering | A traditional engagement |
|---|---|---|
| What you get | A working system in production, plus the reference implementation and playbook | Recommendations and a roadmap |
| Who builds it | The people who scoped it | Often a separate delivery team, or yours |
| How long | A scoped brief, then a build in a one to six week window | Often months before anything reaches users |
| How success is measured | Criteria set before the first line of code: revenue, margin, hours reclaimed, adoption | Deliverables completed |
| After launch | We stay through rollout and hand-off so the system is used | The engagement ends at the report |
What we build
Systems, not slides.
02
Evaluation harnesses
Tests of model quality on your own cases, run on every change, so quality is measured rather than assumed.
03
Agentic workflows
Multi-step agents that act inside your tools, with guardrails and a person signing off where it matters.
04
MCP workflows
Model Context Protocol servers that connect your systems once, for every model you use.
05
Data products
The pipelines and data models underneath, built to be used, owned, and measured.
06
Reference implementations
Each build documented as a reusable pattern, so the second deployment costs a fraction of the first.
Four moves
One accountable team, from brief to production.
Success criteria (revenue, margin, hours reclaimed, adoption) are set before the first line of code, and we stay through rollout so the system is used, not just shipped.
Move 01 Diagnose
Intake with the business sponsor, then a discovery workshop: where generative AI, agents, and data products create value, and where they don’t.
Workshop
A scoped, build-ready brief, sequenced by dollar impact and risk.
Brief
Move 02 Build
A scoped brief, then a working system in a one to six week window.
Weeks
Forward deployed engineers embedded with your team: RAG pipelines, evaluation harnesses, agentic and MCP workflows.
Production
Move 03 Prove
Success criteria set before the first line of code: revenue, margin, hours reclaimed, adoption.
Criteria
We stay through rollout and hand-off so the system is used, not just shipped.
Rollout
Move 04 Compound
Every win becomes a reusable playbook and reference implementation, so the second deployment costs a fraction of the first.
Playbook
Technical standard
Set on every build.
We make the orchestration, evaluation, and vector store choices, and a clear call on what stays in house and what goes to a partner. We bring an operator’s lens to build-vs-buy and vendor selection, and we build with whichever frontier model fits the job.
AnthropicOpenAIGoogleMeta LlamaMistralOpen-weight models
The ledger
Proof, not promises.
1,000
About 1,000 manual hours a month returned to the business.
$300M+
Data-driven revenue built from zero to $300M+.
40+
40+ generative-AI use cases evaluated, sequenced, and governed in six months.
15,000
15,000 queries a second at sub-10ms, live and earning.
Outcomes delivered by our leadership across operating roles and client engagements.
Questions
Questions, answered.
What does a forward deployed engineer do?
They work inside your team to scope, build, and ship a production system: connecting models to your data and workflows, testing quality against your own cases, and staying through rollout until the system is used.
How long does a build take?
After intake and a discovery workshop, we write a scoped, build-ready brief, sequenced by dollar impact and risk. The build then runs in a one to six week window.
Which models and tools do you use?
Whichever frontier model fits the job: Anthropic, OpenAI, Google, Meta Llama, Mistral, or open-weight models. We set the orchestration, evaluation, and vector store choices on every build. See every lab, compared.
What happens after launch?
We stay through rollout and hand-off so the system is used, not just shipped. Every win becomes a reusable playbook and reference implementation, so the second deployment costs a fraction of the first.
How do we start?
Most engagements come by referral. Tell us what you want to build on the get started page, or write to hello@promiseandproof.com.
Next
Tell us the system you need in production, and by when.