Category 05 / Managed Data Teams
The continuous engagement model. A dedicated team — named operators, a named delivery lead, a dedicated QA layer — embedded in your comms stack, trained on your schema, scaling up and down with your pipeline. An extension of your AI org, staffed in Nairobi.
Operating model
What you get
Not a platform subscription. Not a marketplace of strangers. A named group of full-time operators accountable to a lead, to a rubric, and to you.
One senior person, accountable to you. Not a rotating account manager, not a shared inbox. The same person on week 1 and week 52.
4 to 40+ full-time operators depending on your pipeline. All calibrated against your gold set before a single production record touches their queue.
Reviewer seniority above operators, holding the rubric and the inter-annotator agreement line. The same team that ships the labels is not the team that signs off on them.
We work in CVAT, Labelbox, V7, Encord, Supervisely, or your internal tool. We don’t force a platform on you, and we don’t rent you one.
Delivery lead sits in your Slack, Linear, or Asana. Weekly syncs, async standups, exception escalation paths defined before production starts.
Throughput, accuracy, turnaround, exception handling — all documented in the SOW, all tracked against numbers, all reviewed in the weekly.
How engagements work
Engagements have a shape, not a fixed scope. Five phases to get from your first brief to a team delivering into your pipeline — then the same team scales with you.
We read your schema, your task documentation, your current QA standards. Understand what you’re actually training.
A written SOW: team size, ramp plan, SLAs, rubrics, tooling integration. You approve it before we staff.
The operator pool trains on your gold-standard data. IAA benchmarked before anyone touches production records.
Weekly delivery cadence, reported metrics, continuous recalibration. Schema evolutions handled in-team, not with a new SOW.
Add operators for volume spikes, reduce for quieter cycles. The team flexes; the institutional knowledge stays.
How this differs
Buyers evaluating us are also evaluating crowdsourced platforms and project agencies. Here’s where the operating model diverges.
Labelers you can’t name, on a platform that owns the relationship.
Dedicated operators, accountable to a named lead, embedded in your stack. The same team, scaling with your pipeline.
Fixed-scope engagements that end when the statement-of-work ends.
When this fits
Managed Data Teams is the right tier when you’ve outgrown project work, and where data quality is tied directly to what your model actually does.
Annotation volume has outgrown the spot-market or crowd approach. You need dedicated capacity that flexes with the roadmap, not one that caps at a platform limit.
A mislabelled edge case costs you a deployment. You need a team accountable to your rubric and your gold reference — not a tool reporting on itself.
Your data needs don’t end at V1. Schema evolves, edge cases accumulate, training cycles continue. You need an operations partner, not a vendor.
Training data is sensitive. You need ISO 27001, real access control, and an operator pool that has passed more than a click-through NDA.
One account, sustained
Numbers from our longest-running Managed Team engagement. One team, one schema lineage, four years.
For one of our longest-running clients, a dedicated team has processed over one million records across a relationship spanning three years. Operators who know the schema the way the client’s own engineers do. When the schema evolves, the team evolves with it. No new SOW. No re-learning. Client named under NDA.
Scope with us
We read your task schema, your volume requirements, your QA standards. Then we staff and deliver against them. No cold quotes. No shelf-priced seats.