TeamPL runs field data collection worldwide — egocentric video, on-site capture, GIS and mystery visits — and builds the AI workflows that standardise it, shorten the pipeline and cut the cost per record.
TeamPL started as a field data and mystery visit operation and grew into a geo data collection and AI workflow automation company. Today we help organisations put AI to work on the unglamorous part of the pipeline — collecting, validating and standardising data at the point it is captured, not after.
Every project starts as something that exists in the physical world and ends as a validated record in your schema. We handle everything in between — and automate as much of it as the work allows.
We map where your data process actually loses time and money, then automate it: intake, validation, standardisation, enrichment and routing. The measure of success is a lower cost per record and a shorter path from capture to usable data — not a pilot that demos well and then stalls.
First-person video captured on head-mounted and body-worn cameras, for training models that need to see the world the way a person does. Scripted tasks or natural activity, indoors and outdoors, across environments and demographics.
Collectors go to the site and record what is actually there, to a written specification. Photo, video, audio, sensor readings and attribute checks — consistent across every collector, so the set trains a model instead of confusing one.
Geospatial capture and verification on the ground: coordinates, attributes, GNSS traces and layer updates confirmed on site rather than inferred from imagery. Standardised by automated workflows and delivered in GeoJSON, Shapefile or your own schema.
Trained visitors assess locations against your checklist and report back in a consistent, comparable format: service quality, compliance, merchandising, pricing. Ten years of programmes across retail, hospitality and services.
Labelling, classification and QA passes on data we collected or data you already have. We work inside your tooling or hand back a clean, reconciled dataset with the exception log attached.
Client names are under NDA. The numbers are not.
We built an automated workflow that made a client's site discoverable and citable by AI assistants. From no AI-referred traffic at all to several thousand monthly visitors, acquired without paid media — a channel that did not exist for them a year earlier.
Large-scale egocentric capture programmes run to client specification, across environments, activities and participant profiles. Recruitment, briefing, consent, capture and QA handled end to end, with a consistent output format from the first hour to the ten-thousandth.
We replaced manual handling in a GIS field data pipeline with automated workflows covering intake, validation and standardisation. Collectors now submit into a system that checks and normalises as it receives, which removed the reconciliation stage almost entirely.
A decade of mystery visit and field audit programmes, with visitor networks built and maintained across multiple countries. Long-running programmes mean we know how audit data degrades over time, and how to design a checklist that stays comparable across years.
We turn your goal into a written spec: what counts as a valid record, what to capture, how to handle the exceptions, and which steps are worth automating. Ambiguity is resolved here, not in the field.
A small batch is collected first and reviewed together. You see the real output format before the full scope is committed, and the spec is corrected while it is still cheap to correct.
Work is assigned by complexity and location, and submissions run through automated validation as they arrive. Progress is visible per item, per day — no waiting until the end to find out where the project stands.
Two review passes: completeness against the scope, then accuracy against the spec. Anything failing is re-collected. You receive the dataset, the evidence and the exception log.
We build local collector teams rather than flying crews in. That keeps cost predictable, makes re-visits possible when your data needs refreshing, and means a new market is a question of lead time — not of capability.
If your project needs a country we have not worked in yet, tell us anyway. Expanding into new markets is something we actively want.
Collected data is only worth what its weakest collector produced. Our controls exist to make that sentence untrue.
Worldwide. We run projects on every continent, with our deepest experience and local collector networks in Europe and Asia.
Most projects move from brief to pilot batch within one to two weeks, once the specification is agreed.
Priced per project after scoping — driven by volume, region and how much of the pipeline needs automating. We quote after the initial brief, usually within two working days.
Yes. Client specifications and outputs are covered by NDA, and collectors are briefed on a need-to-know basis before assignment.
Collection protocols are written for GDPR from the start. Where people appear in footage, consent and anonymisation are handled at the point of capture.
Whatever your schema requires — CSV, JSON, GeoJSON, Shapefile, or a format matched to your existing pipeline. Automated validation runs before delivery.
Yes — every engagement starts with a small pilot batch, so you can review the real output format before the full scope is committed.
Yes. We also provide annotation, QA and standardisation services on existing datasets, not only data we capture ourselves.
Send the outline and we will come back with a scope, a pilot batch size and a price — usually within two working days.
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