Solutions
Forward Deployed Engineers
Senior builders, inside your team.
Trida's forward-deployed engineers join your standups, get access to your repo, and start shipping within a week. They aren't advisors and they aren't contractors on a bench — they're senior AI engineers accountable for getting a system into production.
Every one of them has shipped AI to production before. That's the bar, and it's why they don't need a quarter to become useful.
Why AI stalls
inside engineering orgs.
Ramp time eats the engagement
Contract engineers spend their first months learning your domain instead of building in it. By the time they're productive, the budget conversation has started.
Specs die crossing the wall
The people who understand the business write documents; the people who write code read them. Every handoff loses the context that makes AI systems actually work.
Pilots that never leave staging
The demo impressed everyone. Then evals, monitoring, data governance, and on-call ownership turned out to be nobody's job, and the pilot quietly expired.
Bolted-on instead of built-in
Vendor tools promise AI in a box, but your workflows, data, and constraints aren't in the box. Generic tooling produces generic results.
What they actually do
in your codebase.
Forward-deployed engineers do the unglamorous work that separates a demo from a dependable system — inside your stack, to your standards.
Ship in your repo
Your codebase, your CI, your review process. The code they write is code your team can maintain, because it looks like your team wrote it.
Build the AI layer
RAG pipelines, agent harnesses, eval suites, inference infrastructure — the connective tissue between models and your product.
Wire into your data
Real integrations with your data sources, with the access controls and governance your security team will actually approve.
Demo weekly, with real users
Progress is measured by what users can do this week that they couldn't last week — not by sprint velocity or slideware.
Harden for production
Monitoring, evals in CI, failure handling, cost controls, and a clean on-call story before anything is called done.
Pair until you own it
Docs, training, and side-by-side work with your engineers so ownership transfers before we leave, not after.
The engagement arc
Week 1–2
1. Embed & align
Our engineers join your Slack, get repo access, and run discovery sessions. By end of week two we have a shared definition of success and a technical roadmap.
Week 3–8
2. Build & ship
We write code. Real code, in your codebase. Weekly demos with real users — not slides. We adapt fast based on what the data and users tell us.
Week 9–12
3. Harden & hand off
We stress-test for production, document everything, and work alongside your engineers until ownership is fully transferred.
Who this is for
Engineering leaders with an AI mandate
The board wants AI shipped this year; your roadmap was already full. FDEs add senior capacity without a six-month hiring cycle.
Teams with a stalled pilot
Something promising is stuck in staging. We take it the last mile — evals, hardening, rollout — or tell you honestly why it shouldn't ship.
Platforms adding AI for real
You want AI built into the product, not bolted on beside it. That takes engineers who work in your architecture, not around it.
Put senior AI engineers in your repo.
Tell us what you're trying to ship. Within a week, forward-deployed engineers are writing code in your codebase — priced on outcomes, not hours.
Let's talk →