15 January 2026 | Announcements, Case Studies

From Zero to Deployed in 4 Weeks

From Zero to Deployed in 4 Weeks

Most robotics initiatives don’t fail because teams lack ideas. They fail because deployment is hard: every environment is different, real-world data collection is slow, and “ready for production” is rarely defined with measurable acceptance criteria.

The SyntetiQ Pilot Program is designed to solve that. In a typical engagement, we take you from a clearly defined task to a deployable Skill Pack—supported by benchmarking, monitoring, and rollback—in approximately four weeks (scope-dependent).

What “deployed” means in this pilot

When we say “deployed,” we do not mean a demo video or a model that works once in ideal conditions. A successful pilot produces tangible deliverables:

  • Skill Pack — a site-specific trained skill for your robot and task (versioned)
  • Benchmark Report + Evidence Log — repeatable evaluation across performance, robustness, and safety envelope
  • Deployment Pack — integration templates, monitoring signals, and rollback plan for safe rollout

This is the difference between “AI experiment” and “deployment-ready capability.”

Week-by-week pilot structure (illustrative)

Week 1 — Scope, constraints, and inputs

We start by aligning on what matters operationally:

  • Task definition and success criteria (KPIs)
  • Safety constraints (keep-out zones, speed/force limits, human presence rules)
  • Robot stack and integration path (ROS 2 / vendor APIs)
  • Site inputs audit (what you already have vs what’s needed)

Inputs we can typically work with:

  • CAD/BIM or facility layout (if available)
  • 3D scan / point cloud (optional but helpful)
  • Short video walkthroughs and images
  • Robot specs (URDF/mesh if available, sensors, payload limits)
  • Constraints and operating procedures

Output of Week 1: Pilot plan + KPI targets + data checklist + acceptance criteria.

Week 2 — Trainable digital twin and scenario suite

We build a trainable digital twin that reflects the environment and constraints needed for the task. The goal is not a perfect visual replica, but a twin that supports fast iteration and coverage of relevant scenarios.

We then design a scenario suite that includes:

  • baseline conditions
  • domain shifts (lighting, occlusions, geometry drift)
  • edge cases that drive failure modes

Output of Week 2: Trainable twin + scenario suite definition.

Week 3 — Training + benchmarking loop

We generate synthetic experience and train the candidate skill (IL/RL/Hybrid as appropriate), then evaluate it against the benchmark protocol.

This week is where we turn “learning” into measurable evidence:

  • Task performance metrics
  • Robustness under domain shifts
  • Safety envelope validation (constraint compliance)

Output of Week 3: Candidate Skill Pack + Benchmark results + evidence log draft.

Week 4 — On-robot validation, monitoring, and rollback

We integrate into your stack and validate in a controlled rollout:

  • runtime monitoring signals
  • drift/failure indicators
  • rollback triggers and safe fallback behaviour

We finish with a delivery package that your engineering and ops teams can review and reuse.

Output of Week 4: Skill Pack + Benchmark Report + Deployment Pack.

Note: Timeline depends on task complexity, access to robot hardware, and availability of site inputs. We’ll confirm scope in Week 1.

What makes this pilot different

Benchmarks are part of the deliverable

Many pilots end with “it seems to work.” We deliver a report that supports acceptance testing:

  • what was tested,
  • under what conditions,
  • against which thresholds,
  • and what evidence is available.

Safety is engineered, not assumed

We treat safety envelope validation, monitoring, and rollback as core requirements—not afterthoughts.

Faster iteration without excessive on-site burden

Synthetic scenario coverage reduces the need for long and expensive collection cycles, especially for rare and hazardous edge cases.

Who this pilot is for

The pilot is a strong fit if you have:

  • a defined task (manipulation, inspection, navigation, perception)
  • a robot platform (or simulator + integration path)
  • operational constraints and KPIs you can agree upfront
  • willingness to iterate through a measurable acceptance process

Ready to start?

If you want to explore a pilot, the fastest next step is to share:

  • your task description,
  • robot platform and integration constraints,
  • environment/site type,
  • success KPIs and safety constraints,
  • what site inputs you already have (CAD/scan/video).

Apply here: syntetiq.com/apply

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