ML R&D Teams that Ship

Why research stalls

Ambiguous scope, weak infrastructure, and no production owner. Teams get stuck in perpetual experimentation because there is no clear path from research to production — a pattern covered in Production ML failure modes. The result is a backlog of notebooks and prototypes with no measurable impact.

Typical constraints we handle

Hiring gaps, unstable pipelines, and missing validation. We often see:

Missing senior leadership to define architecture
Fragmented toolchains across teams
Unclear acceptance criteria for “done”
Models without deployment or monitoring strategy

Typical delivery targets we use:

2–4 week cycles to production‑ready milestones
Clear ownership across research and production stages

Our approach

Senior-only teams with architect-led delivery and explicit SLAs. We provide teams that:

Define system boundaries and delivery milestones
Build reproducible pipelines and evaluation harnesses
Own production readiness, not just research output
Transfer knowledge to internal teams

Architecture examples

Reproducible experiments, model registries, and deployment workflows. Typical components include:

Experiment tracking and reproducibility
Model registry with versioning
Automated evaluation and deployment pipelines
Monitoring and drift detection

Case studies

Applied AI and trading systems delivery with production ownership. Examples:

Engagement models

Embedded squads or project-based delivery with handover.

What we deliver

You get more than a team; you get production outcomes:

Clear delivery roadmap and acceptance criteria
Stable pipelines and reproducible training
Monitoring, alerting, and incident response plans
Documentation and handover for internal teams

When to engage

Engage us if:

Research is not turning into production value
You need senior leadership for AI delivery
You require faster execution without sacrificing quality

FAQs

What do you deliver in the first sprint?
A clear roadmap, system boundaries, and a production‑ready milestone with measurable acceptance criteria.

Do you integrate with in‑house teams?
Yes. We embed alongside internal teams and transfer knowledge to keep delivery sustainable.

How do you prevent research from stalling?
By tying every sprint to production outcomes and keeping ownership explicit.

Typical engagement flow

We start with a short audit and roadmap, then embed a small squad to deliver production-ready milestones. The engagement can be extended for ongoing support or transitioned to your team with a structured handover.

Team composition

Teams are built from senior engineers with clear roles:

Architect or tech lead who owns system design
ML engineer for model development and evaluation
Backend engineer for infrastructure and integration
Optional data engineer for ingestion and quality

We keep teams small to maintain velocity and accountability.

Operating model

We align on milestones, not just tasks. Every sprint should deliver a measurable outcome: a tested pipeline, a monitored model, or a production integration. This keeps work grounded in business impact instead of research output.

Signals of success

You should see:

Shorter cycle time from idea to deployment
Reproducible experiments with clear baselines
Stable production metrics over time

If any of these degrade, we adjust process, tooling, or scope.

Typical range outcomes

Across teams, we usually improve:

30–50% reduction in time from experiment to deployment
20–40% fewer production incidents related to model changes