Deployment workspace

The deployment workspace for AI solution providers.

Deploy to customers more efficiently, more repeatably and with fewer mistakes.

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How it works

One workspace for the deployment loop.

Cephos brings the work between demo and production into one platform. Used by FDEs, deployment leads, founders, and product engineers.

01 / Capture

Calls, video, notes, scans. All queryable.

Audio, video, photos, field notes, site scans. Captured on-site, structured into queryable deployment context. Online or offline.

02 / Track

Every deployment, in one place.

Phase, progress, blockers, owners, and deployment status, visible to the whole team.

03 / Coordinate

Stop chasing Slack threads.

Structured RFIs and follow-ups, linked to the deployment. No Slack archaeology.

04 / Integrate

Integration readiness, never in someone's head.

APIs, credentials, schemas, and access tracked per deployment. Visible, not tribal.

05 / Triage

One queue for production failures.

Alerts, edge cases, and recurring failures, assigned, tracked, resolved together.

06 / Remember

Knowledge that compounds.

Customer-specific rules and fixes saved as memory. The next deployment starts from there.

The difference

Before Cephos. After Cephos.

Before Cephos

Deployment context lives in one person's head.

Scattered calls, docs, Slack threads, dashboards, API specs, credentials, and deployment learnings. None of it carries forward.

  • Every customer deployment restarts from zero
  • Critical field context lost when one teammate is off
  • Same edge cases debugged again and again
  • Field failures can cost thousands per incident, with no learnings carried forward
  • Integration work is invisible to the rest of the team

With Cephos

One workspace. Full deployment context.

Every deployment has context, owners, blockers, alerts, and reusable memory, visible to the whole team.

  • Every deployment has a shared source of truth
  • Blockers, integrations, and alerts visible to all
  • Edge cases saved once, reused across customers
  • Coding agents inherit the team's deployment memory
  • Deployment knowledge compounds instead of disappearing

Capture

Most deployment-critical context never makes it back from the site.

FDE work happens from the field, the office, and home. Cephos lets teams capture and query deployment context in any format, wherever the work happens.

Every call recorded, every field note logged, every API spec ingested, structured into a single queryable deployment record.

Calls Site walkthroughs Recordings
Field notes Files API specs
Photos 3D scans Logs
↓
Deployment Memory
Queryable · Structured · Persistent

Deployment memory

Every deployment teaches the system.

The customer rule someone learned on a Tuesday call. The integration quirk that took a week to debug. The site constraint that broke the first install. Captured once, linked to the deployment that taught you, surfaced when the next one needs it.

Cephos captures what your team learns and makes it available to the next teammate, the next customer, and the next deployment, so knowledge compounds instead of disappearing.

0×
Times the same edge case should be debugged twice.
∞
Deployments that benefit from your team's prior work.
Site visit · Week 1
Lesson: measure before trusting CAD
Customer's loading bay was 6 inches off spec. Added to the site-visit checklist.
Customer call · Week 3
Lesson: ask about live-change sign-off in the first call
Customer's safety officer gates every change. Added to the discovery script.
Field interview · Week 5
Lesson: always talk to night ops
They had a workaround the day team didn't know. Added to the field-context playbook.
Memory saved · Week 5
Three lessons in the playbook.
Every deployment after this one starts with them.

Why now

Deployment is the new AI bottleneck.

AI solution providers are moving from demos to production. Deployment, not models, is now the critical path, and the constraint on revenue.

01

FDE demand has grown 42x since 2023.

Today, deployment scales with headcount.

02

The average AI prototype takes 8 months to reach production.

Every customer brings its own systems, approvals, and physical constraints.

03

FDE work repeats without memory.

The same edge cases, integration patterns, and deployment failures appear across customers, but nothing connects them.

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Deploying AI into real customer environments?

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