Days 0 to 2
Audit the flow
We map lead sources, systems, and failure modes. Fields, owners, SLAs, dedupe rules, and what "good" looks like.
GPU compute + training reliability
GPU rental and custom LLM training support.
Training fails for predictable reasons: dirty datasets, leaky splits, brittle pipelines, and long runs with no recovery plan. We provide GPU capacity and the engineering layer that makes training repeatable and safe.
GPU capacity
Provision compute so training is not blocked by hardware availability.
Data cleaning
Deduplication, normalization, QA, and leakage checks to protect model quality.
Run reliability
Monitoring, checkpoints, and failure recovery so long runs do not end in surprises.
How we work
Integrations fail in the gaps. We ship with guardrails: retries, dedupe, validation, and observability from day one.
Week 1 to 2
Ship the integration
We implement pipelines, webhooks, retries, and monitoring. Make/Zapier when it fits, direct APIs when it must.
Week 2 to 4
Prove reliability
We add validation, dashboards, and runbooks so leads do not drop silently and teams can trust the data.