Most AI transformation programs end up as slide decks. Invovia builds working software instead. We put senior engineers inside your team, right at the point where AI ambition runs into execution reality, and we stay until your team is faster, stronger, and able to carry it forward on its own.
“Forward Deployed Engineering” sounds like a lot of jargon for a simple idea: instead of handing you a strategy document and wishing you luck, we send our own engineers to sit inside your team and build the thing with you.
An FDE is a senior engineer who joins your daily standups, gets access to your codebase in the first week, and starts shipping real, working code, usually within a few days rather than months. They’re not there to write a report about your AI strategy. They’re there to build the first version, fix what’s broken, and show your own engineers how to do it themselves next time.
“Forward Deployed Engineering” sounds like a lot of jargon for a simple idea: instead of handing you a strategy document and wishing you luck, we send our own engineers to sit inside your team and build the thing with you.
An FDE is a senior engineer who joins your daily standups, gets access to your codebase in the first week, and starts shipping real, working code, usually within a few days rather than months. They’re not there to write a report about your AI strategy. They’re there to build the first version, fix what’s broken, and show your own engineers how to do it themselves next time.
Outcome-accountable engineers in your standups, writing production code and transferring capability until your team can carry it forward.
Redesign the software delivery lifecycle so AI amplifies every phase, compounding velocity sprint over sprint.
Engineering rebuilt from first principles, with team structures, ownership models, and intelligence designed in from day one.
AI-assisted legacy migration, done with discipline, at 3–5× the speed of traditional approaches.
From reactive ticket-chasing to proactive, predictive application management at enterprise scale.
The memory, orchestration, guardrail, and observability layers autonomous agents need to run safely.
Risk systems, fraud detection, and core banking modernization, with SOC 2, PCI-DSS, and OCC awareness built in.
AI feature acceleration, platform re-architecture, multi-tenant LLM infrastructure.
Clinical decision support, HIPAA-compliant platforms, EHR modernization.
FedRAMP-aligned architecture, secure pipelines, mission-critical modernization.
Predictive maintenance, demand forecasting, OT/IT integration.
Personalization, real-time recommendations, high-concurrency infrastructure.
Token economics, context-window behavior, rate limits and failure modes across every major model — plus the multi-agent coordination patterns that keep long-running agents reliable.
Inference and platform layers across AWS, Azure and GCP with specialized providers, vector and data tooling, and guardrails built into the infrastructure rather than bolted on afterward.
One working reference across every layer we operate in — from raw LLM APIs through production observability, and vendor neutral by design.
Copilot, Cursor and evaluation tooling woven into the dev cycle, production agent design, enterprise RAG, plus observability synthesis and automated remediation for run-and-operate.
We keep current production experience with every major provider — Anthropic, OpenAI and open-source models served via vLLM or air-gapped — and take no referral margin, so recommendations come from engineering merit.
THE BEST OF COMPANY
An AI-native engineering services firm for enterprises that need AI to work in production, not in a keynote. We specialize in forward deployed engineering, AI-accelerated delivery, legacy modernization, and safe agentic architecture. Headquartered in Pleasanton, CA; operating globally across North America, Europe, and APAC.
No vendor deck, no pitch. Tell us the problem and we’ll give you a straight answer about whether and how we can help.