Welcome to Frontais
Scroll prototype · AI.next

The FDE pod journey, revealed by scroll

Scroll into the section below. It pins in place and each scroll step moves the pod to its next stage. Click any stage to jump.

Scroll down
From Pilot to Production

Only 4 in 33

AI proofs of concept make it to production.

Source: IDC research with Lenovo, 2025

We build for the 4, from day one.

See how an FDE pod gets you there
Reached production
Stalled as a pilot
FDE AS A SERVICE

A pod that works as part of your team.

Forward-deployed engineers embed with your people, find where AI pays back, ship agents into the work, and then hand the capability over.

3–5 people

A cross-functional pod: FDE lead, AI and data engineers, and a value lead.

8–12 weeks

From kickoff to a first use case running in production.

Your tools

Embedded in your systems, rituals and security model, not working beside them.

Your goalAgents in production, capability you own
Leading this stage Your teamowners and experts FDE lead AI engineer Data engineer Value lead
STAGE 1 OF 5 · WEEK 1

Join your team

Engineers join your team, learn your workflows, systems and constraints, and work in your tools.

YOU GET

A team that understands your business, not a vendor waiting on tickets.

Talk to an FDE

More ways to put AI to work.

Combine them with an FDE pod, or start with the one that fits your goal.

01

Agentic AI as a Service

Agentic AI as a Service

Agents built for your business domain, plus assistants and copilots grounded in your own data, orchestrated across your systems.

Agents doing real work in your core workflows.

Talk to us
02

AI ROI and advisory

AI ROI and advisory

Find where AI pays back, agree the value baseline and KPIs before anything is built, and measure the return after go-live.

A value case your leadership can act on.

Talk to us
03

AI Observability

AI Observability

Monitor, trace and evaluate every model and agent in production: quality, drift, cost and safety in one view.

Trusted AI with visible quality, cost and risk.

Talk to us
04

Harness as a Service

Harness as a Service

A managed runtime around your agents: connections, memory, orchestrators, guardrails, evaluation and monitoring.

Agents in production without building that layer yourself.

Talk to us
WAYS TO START

Three ways to begin, depending on where you are.

Each has a clear scope and leaves you with assets you own: agents, use cases and playbooks.

MOST TEAMS START HERE

AI ROI assessment

We find where AI pays back in your business, agree the baseline and KPIs, and shortlist the first use cases worth building.

YOU GET

A value case and a ranked list of use cases.

Start with an AI ROI assessment
ALREADY KNOW THE PROBLEM?

FDE pod on one use case

Already know the use case? A pod embeds with your team and ships it.

You get: a first agent in production in 8–12 weeks.

Data readiness review

Not sure your data is ready for agents? Start with Data.Next.

You get: a roadmap to agent-ready data.

TECHNOLOGIES WE WORK IN

An open stack, not a lock-in.

We use what your platforms already do well, integrate proven tools, and build only what sets you apart. Open standards keep models and tools replaceable.

Leading foundation models
Amazon Bedrock
Model Context Protocol (MCP)
RAG and vector search
Agent frameworks and runtimes
Multi-model routing
Evaluation and tracing
Guardrails and policy controls
KEEP EXPLORING

Related foundations.

Data.Next

One governed data foundation for analytics and AI, so every agent works from trusted context.

Cloud.Next

A secure, well-run home for every workload, AWS first, ready to run data platforms and agents at scale.