Engineers embedded in your environment who ship AI to production
Forward deployed engineering
There is a gap between a powerful AI model and a system that solves your problem. Forward deployed engineers live in that gap. We embed senior engineers in your repos, your Slack and your stack. They learn your workflows and data, then build and ship AI systems that run in production.It is the AI-focused way to run a Dedicated team. The engineer owns an outcome, not a ticket queue.
Our approach
Forward deployed engineering is not consulting. Our engineers write production code inside your environment. The engagement is judged on one thing: working systems your team uses every day.
The forward deployed engineer (FDE) model started at Palantir. It is now how frontier AI labs deliver for enterprise. Engineers embed with the customer, learn the real workflows and data, then build straight to production.
TechEmulsion is an official Claude partner. We bring that model to the mid-market. Companies that will never get a global integrator's FDE pod get one from us, embedded in your repos, Slack, and stack.
Here's what backs every engagement
Senior engineers in your repos, tools, and channels, synced to your business hours
Production code from week one. We learn your workflows by building inside them
AI-native since 2023: RAG pipelines, AI agents, and LLM integrations, with 30+ AI products in production
Built on Claude and other frontier models, with architecture picked for your cost and reliability needs
You own everything: code, IP, infrastructure, and the knowledge to run it. NDA signed as standard
Weekly demos of working software, and honest calls on what should not be built
Engineers close the gap between a powerful AI platform and your messy business problem. They are accountable for the outcome, not the recommendation.
Forward deployed engineering at a glance
A forward deployed engineer is part software engineer, part architect, part operator. Technical, customer-facing, and judged on what ships. The role exists because there is a gap between 'here is a powerful AI model' and 'here is a system that solves your problem.'
Consultants stop at recommendations. Internal teams often lack AI experience. FDEs embed with you and build production systems on frontier models. The market agrees:
Andreessen Horowitz
calls forward deployed engineering the defining services-led growth motion of the AI era. Durable, deeply integrated software ships through hands-on FDEs.
Anthropic
builds enterprise delivery around the FDE model. Its 2026 alliance with DXC trains tens of thousands of Claude-certified FDEs embedded inside customer organizations.
Our delivery record
shows it works at mid-market scale. Pack Assist went from brief to a production AI sales-qualification platform in 8 weeks, embedded with the client's team throughout.
With great AI comes great responsibility, and TechEmulsion takes that responsibility seriously.
Why it's different
What Makes Forward Deployed Engineering Different
01
Embedded, not adjacent
Your FDE works in your repos, joins your standups, and talks in your Slack. You review their work in your normal PR process.
02
Production is the deliverable
Pilots are easy. Production is hard, so every engagement is scoped to land a system your team uses, with monitoring, evals, and handover docs.
03
Claude partner, model-pragmatic
As an official Claude partner we build deep on the Anthropic stack. Your constraints decide the architecture, so we also work with OpenAI, open-weight models, and hybrid setups.
04
Generalists with high autonomy
FDE work means vague requirements and messy data. Our senior generalists scope, design, and build without a project manager in between.
05
Feedback loop to your roadmap
FDEs sit inside your operations and see what your product is missing. You get a running list of improvements alongside the build.
06
Mid-market economics
The Fortune 500 gets FDE pods at global-integrator rates. We deliver embedded senior AI engineering at roughly 4× less than an equivalent US hire.
Across the SDLC
How a Forward Deployed Engagement Runs
From discovery and architecture through development, integration, and optimization:
01
Embed and map
- Your FDE joins your repos, Slack, and standups from day one.
- Output: where AI pays back fastest, plus what not to build.
02
Ship the wedge
- First system targets the fastest provable win, built to production standards.
- Weekly demos. Evals and monitoring wired in before launch.
03
Expand and compound
- With the first system live, the FDE moves to adjacent workflows.
- Each deployment leaves docs, evals, and trained internal owners behind.
04
Hand over or stay embedded
- You choose: full handover to your team, or an ongoing retainer.
- Either way you own the code, infrastructure, and IP from day one.
Client outcomes
What Forward Deployment Delivers
Forward deployment is measured in production systems, not billable hours. Typical engagements:
| Task | Before | After | Impact |
|---|---|---|---|
| AI sales-qualification platform (Pack Assist) | Brief and a manual sales process | Production platform with hybrid static/LLM flow, RAG, and a 30-chat agent dashboard | 8 weeks to production |
| Add a RAG knowledge assistant to an existing SaaS | 6+ months to hire an internal AI team | Embedded FDE ships to production inside the existing codebase | 4 to 8 weeks, no new headcount |
| Customer support automation for a DTC brand | Every ticket handled manually | AI agent trained on catalog and order data resolving the majority of tier-1 tickets | ~60% tickets deflected |
| Embedded AI engineering capacity | US senior AI engineer at full market cost, 3-month search | Senior FDE embedded in your team on a monthly retainer | ~4× lower cost, weeks to start |
Every engagement is built to end as a production deployment you can point to. In this work, the track record is the product.
Tools & platforms
The FDE Deployment Stack
The stack our FDEs deploy with, matched to your environment:
- Anthropic Claude (API, Claude Code, agent SDK)
- OpenAI APIs
- LangChain / LangGraph
- Pinecone / pgvector
- Python FastAPI
- Next.js / TypeScript
- Supabase / PostgreSQL
- AWS (ECS, Lambda, S3)
- Docker + GitHub Actions CI/CD
- Playwright (eval + regression suites)
Why TechEmulsion
Why Teams Choose Forward Deployed Engineering
2023
Building AI systems since before the LLM wave, 30+ AI products in production
8 weeks
Brief to production for Pack Assist, a full AI sales-qualification platform
30+
AI products shipped across SaaS, agencies, e-commerce, and home services
4× less
Embedded senior AI engineering vs. an equivalent US hire
Claude
Official Claude partner building on the Anthropic stack
100%
Client-owned code, IP, and infrastructure: NDA as standard