Custom Model Integration

Bring your algorithms on an AI-native harness that self-learns

CVector runs your own algorithms on an AI-native harness, equipped with self-improving agent skills and an easy-to-use interface built for learning and knowledge capture. Your models keep their edge while CVector handles deployment, context, and continuous improvement — so every run gets sharper and your team's expertise is captured along the way, not lost between shifts

Start with one model

See how it works

Benefits

Run the models you trust on a harness that scores, remembers, and learns

Your model, live on the harness without rebuilding your stack.

CVector hosts the models you already trust, or builds new ones with you, on a managed AI-native harness. You keep your IP and your edge while skipping the integration, infrastructure, and MLOps overhead.

Every output dollar-scored and tied to margin.

Your model's outputs run through CVector's TEA value layer, turned into dollar-scored recommendations your team can act on. That same score is the reward signal the system learns from, tying your model to real plant margin over time.

Skills that sharpen and knowledge that compounds.

CVector wraps your models in self-improving agent skills that add context, guardrails, and feedback loops, plus an interface that captures operator decisions and rationale as they happen. The more your Knowledge Hub holds, the more sophisticated the models and skills you can build.

Outcomes

Measurable impact across deployment speed, model performance, and retained knowledge.

Each result is tied back to your own models and your team's decisions, so you can see exactly what the harness adds.

40-70%

Faster algorithm deployment

From model handoff to production on CVector's managed harness, with no rip-and-replace

8-20%

Improvement in model performance

As self-improving agent skills incorporate feedback over the first month

50-75%

Reduction in integration effort

By removing custom MLOps, infrastructure, and glue-code work

1 month

To first dollar-scored recommendations

From model onboarding to live recommendations ranked by margin impact through the plant techno-economic model output

Auditable

Decision history for every recommendation

Backed by a full trail of inputs, model outputs, and trusted operator edits

Knowledge retained

Across shift changes and staff transitions

Via decisions and rationale captured directly in the interface

How It Works

Continuous learning around the algorithms you already trust

CVector runs your models or builds new ones on its AI-native harness, alongside the live data, dollar-scoring, and Knowledge Hub already in place, and wraps them in agent skills and an interface that improves with every use.

Bring or build the model

Deploy a model you already trust as-is, IP intact and no rebuild, or build a new custom model and agent skills with CVector on the harness.

Self-improving agent skills and knowledge capture

Agent skills add context, guardrails, and feedback loops that refine outputs over time, while the interface records operator reasoning so institutional knowledge is retained.

Scored and contextualized by the harness

Your model draws on the same fused live data and Knowledge Hub as the standard solution, and outputs become dollar-scored recommendations by the plant economic value layer.

Your team stays in control

Every recommendation is accepted, rejected, or adjusted by your team.

Testimonials

Built in partnership with industrial leaders.

Tristan Gilbert

CTO, Ammobia

"Our partnership with CVector gives us the analytical capabilities of a much larger company at a fraction of the cost and headcount."

Industries

Where proprietary models meet business outcomes.

Best fit for teams with their own models who need them deployed, dollar-scored, and improving, with operator knowledge captured, and without rebuilding their stack.